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Generation of typical weather data using the ISO Test Reference Year (TRY) method for major cities of South Korea
Abstract
Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design. Simulation often requires hourly weather data. Such data sets are the Test Reference Years (TRYs), Typical Meteorological Year (TMY) and Weather Year for Energy Calculations (WYEC). Typical weather data consists of 8760 values of various selected meteorological parameters such as ambient temperature, solar radiation, relative humidity and wind velocity and are originally derived from long-term data. This paper discusses methods of selecting typical weather data, the possibility of using the cloud cover data instead of daily global radiation and describes the selection of ISO Test Reference Year (TRY) for major cities of South Korea. The ISO-15927 procedure and algorithms are explained in detail and the Finkelstein–Schafer statistic, the basic selector statistic explained. ISO TRYs for the major cities of South Korea are derived from 20 years of meteorological data recorded during the period 1986–2005. A comparison is made between the 7 sites demonstrating the link between dry-bulb temperature, solar radiation and latitude.
... Fue desarrollada por los SANDIA National Laboratories en Estados Unidos y es utilizada para elaborar los años meteorológicos típicos de EEUU en sus sucesivas versiones TMY1, TMY2 y TMY3 [2][3][4]. La metodología SANDIA ha sido utilizada exitosamente en varios países [5][6][7][8][9][10][11][12] y es altamente aceptada internacionalmente. ...
... Las sucesivas versiones de los TMY elaborados para EEUU difieren en la cantidad y calidad de las series de datos base y en actualizaciones de implementación derivadas de la inclusión de mayor cantidad de datos. Variaciones de esta metodología base han sido utilizadas para confeccionar series típicas para otras partes del mundo [5][6][7][8][9][10][11][12]. ...
... En la Figura 6.4 se muestran los dos tipos de medidas de irradiancia difusa utilizadas para este trabajo. 7. La medida sistemática con banda de sombra requiere alinear semanalmente su posición. ...
Las series anuales típicas son utilizadas para simular actividades humanas bajo condiciones meteorológicas características de una región. Deben ser representativas de una estadística de largo plazo en términos de sus valores medios y de su variabilidad para que las simulaciones sean realistas. En este trabajo se presenta el desarrollo de cinco series típicas a escala horaria para uso en aplicaciones de energía solar u otras actividades que sean principalmente influenciadas por la radiación solar a nivel de superficie. Para construir una serie típica se requieren datos de largo plazo de las variables involucradas. Como en Uruguay no existen medidas de largo plazo de irradiación solar, se utilizan 15 años de estimaciones por satélite de esta variable que tienen baja incertidumbre sobre el territorio nacional. Las series típicas cuentan con valores de irradiación global en plano horizontal e inclinado y de irradiación directa en incidencia normal. Se incluye además información de temperatura, humedad, presión y viento, todas variables medidas en tierra por instituciones nacionales. Las series típicas son elaboradas para las localidades de Montevideo, Salto, Rivera, Rocha y Colonia, donde la estadística de largo plazo de medidas completas está disponible o nos ha sido posible construirla. La metodología utilizada para desarrollar este AMTUes es de uso internacional y está bien documentada en la bibliografía científica especializada.
... This is repeated for all 12 months to produce the final file. In this case the Finkelstein-Schaefer statistic (Finkelstein and Schafer, 1971) was used, having been widely used previously for this purpose elsewhere globally Lam et al., 1996;Lee et al., 2010). Meanwhile the DSY files are selected as an entire single year based on an extreme value analysis of an overheating metric. ...
... In order to select a given month, a measure is required to assess how far a given month is away from its average conditions. One such metric is the Finkelstein-Schaefer (FS) statistic (Finkelstein and Schafer, 1971) which has been widely used for this particular application Lam et al., 1996;Lee et al., 2010). This measures the difference in the cumulative density function (CDF) of daily mean values of a weather variable within a given month and year versus the CDF of values for all years of that given month. ...
The climate of Ireland is changing. Consequently, the Department of Housing, Local Government and Heritage funded this project to update ‘Climate maps and data to support building design standards in Ireland’. The motivation of this particular work was to produce a set of representative ‘weather files’ for use in the building energy modelling sector.
... In recent years, the use of computational tools for the assessment of building energy performance has increased significantly since it represents a key strategy for the correct design of highly efficient buildings, as well as to quantify the potential energy savings in retrofit projects [4] . The general objective is to achieve the best indoor thermal comfort with minimum energy consumption, maintaining the quality of life with lower energy needs. ...
... The Taylor diagram has been widely used to evaluate climate models over the past years because of its useful features to compare and evaluate multiple aspects of models and gauging the relative skill of many different models [28][29][30][31] . Each point in the two-dimensional Taylor diagram can represent simultaneously three statistics ( R , RMSD and σ ) (see Figs. [3][4][5][6][7][8][9]. The correlation coefficient R is a number between −1 and + 1 measuring the strength or consistency of the relationship between two variables ( x and y ). ...
The assessment of building energy performance through dynamic simulations has been increasing significantly in recent years since it represents a key strategy for the correct design of highly efficient buildings. Results of dynamic energy simulations are affected by many uncertainties, and its reliability depends on the accuracy of the input variables. One of the most influential variables is the climate surrounding the building, a reason why the use of accurate weather data files is essential, but experimental datasets are not always available. In this context, this paper analyses numerical weather datasets obtained from different regional climate models by comparing them with real data; in addition, it evaluates their impact on the energy performance of a historical building in Asunción through dynamic simulations. The database of five different weather data sources is compared with observed meteorological data in order to assess their accuracy through statistical analyses. Moreover, some methodologies to estimate diffused and direct components of the global solar radiation are evaluated, with the objective of solving the problem of missing direct and diffused solar data components from the meteorological codes. Subsequently, weather data files are generated to quantify the influence of measured/simulated meteorological data on the evaluation of building energy performance. The results obtained in this paper show that the simulated meteorological data agree very well with real observations for the year under study. Also, the simulations of the building energy performance delivered similar values to those obtained using the real weather dataset. Therefore, the regional climate models can represent a reliable tool for building energy performance assessment, and mainly for the calibration of building energy models when measured weather data is not available.
... Kalamees and Kurnitsky [15] analyzed the Estonian climate to construct TRY for heating and cooling energy calculations. Lee et al. [16] generated TRY according to screw upISO-15927 standards for major cities of South Korea from 20 years of meteorological data. A modified method for generation of TMY from recorded weather data was presented by Zhang et al. [17]. ...
... Both critical and positive weather years were identified as the most severe and most favorable weather years, respectively, in terms of frost induced damage to building walls in Prague, Czech Republic. However, contrary to the methodologies described in the Introduction section [14][15][16][17][18][19][20], those years were not generated synthetically, but selected as "peak years", providing the most and least severe conditions inducing the frost damage. Those weather years were selected from the weather data period of 1982-2011. ...
Building energy demands in long-term studies are mostly calculated using the averaged weather data sets, reflecting
the changes in environmental conditions over time, thus the different energy consumption each year.
This paper reviews and discusses the coupled effects of warming trend in global mean surface temperatures,
application of different design weather datasets, and utilization of different methods of building energy assessment
on the calculated energy demands. The possible inaccuracies of building energy analyses caused by
those three factors are investigated on the example of a residential house in Central Europe. For that purpose
eight different weather data sets for Prague, Czech Republic are selected and simulations in two different scales
are performed. The analysis of the effect of recent weather data is performed by an improved methodology. First,
the simulation brings an increased precision as an advanced hygrothermal model is used for energy calculations.
Second, the building performance is assessed, contrary to the Czech national standards, using both heating and
cooling energy demands. The simulation results confirm the warming trend in the time period of 2013–2017 as
the average heating demands are 3.95% lower and the average cooling demands 3.96% higher in a comparison
with the Test Reference Year. In the extreme years, a 12–15% decrease of energy consumed for heating and up to
20% increase of energy necessary for cooling is found. This is in accordance with the presumed warming trend
that has been widely discussed during the last few decades. Furthermore, the presented results verify the critical
and positive design weather years as suitable for application in the simulation of heating and cooling energy
demands in the Czech Republic.
... One could simulate the solution behaviour considering all individual years of a weather data-set comprising several years and then design the solution for the most critical year. This procedure is time-consuming and rather confusing as large amount of data are required (Janjai & Deeyai, 2009;Lee, Yoo, & Levermore, 2010). On the other hand, one can use a typical or reference year, which is a single 12 month year of hourly data that represents the range of weather patterns that would typically be found in a multi-year data-set (Janjai & Deeyai, 2009;Kalamees & Kurnitski, 2006;Lee et al., 2010). ...
... This procedure is time-consuming and rather confusing as large amount of data are required (Janjai & Deeyai, 2009;Lee, Yoo, & Levermore, 2010). On the other hand, one can use a typical or reference year, which is a single 12 month year of hourly data that represents the range of weather patterns that would typically be found in a multi-year data-set (Janjai & Deeyai, 2009;Kalamees & Kurnitski, 2006;Lee et al., 2010). ...
The exterior weather is a key factor in the behaviour of buildings, which may affect mechanical resistance, durability and appearance of materials and components. Also, it may influence the indoor air quality, limit the comfort of users and increase energy costs. Therefore, exterior weather data is an important input for hygrothermal simulation, as a tool to previously assess the behaviour of buildings. In this work, the methodology to create a reference year according to ISO 15927-4 was assessed. The meteorological data were statistically analysed and the missing data were filled. Two reference years were obtained: one with gaps and the other without omissions, in order to find out what were the implications brought by filling the data. Hygrothermal simulations were conducted considering the two reference years. The results were statistically compared and they showed the reference years presented differences, although only three months are actually different. The outputs of the hygrothermal simulations are similar in some periods and different in the periods of time close to the months that are different in the reference years. Although these dissimilarities are highlighted by statistical differences they are not relevant enough to result in a significant difference in the durability of the materials.
... One of the key factors that influences the accuracy of building performance simulation is the reliability of outdoor meteorological data as input [5][6][7]. Thus, the selection of appropriate meteorological data is a prerequisite for producing valid simulation results [8][9][10]. ...
This study aims to demonstrate the comprehensive development of typical meteorological years (TMYs) under relatively limited observational data. The distribution of missing hourly observational data of the 2011–2020 period at all sites was examined. This paper proposes a quality control method for filling the gaps in the missing hourly observational data using bias-corrected ERA5 reanalysis data in the process of developing TMYs. Initially, the temperature bias distribution from −4.5 °C to 2.7 °C was reduced to a range of −0.014 °C to 0.005 °C. The relative humidity bias distribution was −6 % to 10 %, and was reduced to −0.32 % to 0.07 %. The bias distribution of wind speeds ranging from −4 m/s to 2 m/s was reduced to −0.02 m/s to 0.35 m/s. The Sandia method with a modified weighting of Finkelstein-Shaffer (FS) statistics was applied to eight climate elements, namely, global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, temperature, precipitation, wind speed, relative humidity, and dew point temperature to generate TMYs at 106 sites across eight climate zones in Indonesia. The verification results showed that the average correlation and RMSE between TMYs and their long-term averages were 0.96 and 75 w/m2 for global horizontal radiation, respectively, while those for temperature were 0.86 and 1.3 °C, respectively.
... In the second technique, candidate months are scored according to a statistical method known as root mean square deviation (RMSD) with long-term data, and the month with the highest score can be suitable for TMY [6][7][8]. In the last technique, some researchers designate the month simply by the lowest weighted sum of TMY without any further calculations [9][10][11]. From the point of view of the energy analysis of buildings by some researchers, it is stated that after the candidate month is ranked according to the weighted sum value, there is no evidence that the typical month selection process is more useful by moving on to a second stage [12,13]. ...
The most important parameter which affects the results of building energy analysis is the weather data and it can be obtained by different methods for the same location. Although lots of studies have been conducted for Türkiye, it was seen that the impact of different weather data for the same location has never been investigated. The aims of this study were to compare the heating and cooling demands of the buildings with respect to different weather files. Building loads were calculated using five different meteorological source data. Calculations are made for eight cities which represent heating and cooling dominated climates of Türkiye. Calculation procedure of internal heat gain was explained in detail. All simulations were performed using Energyplus v9.2. The findings of the comparison showed that although some results are similar to each other for some weather files, they could have great variances in the energy analysis also. A common missing meteorological data-filling algorithm may be developed in order to reduce the deviations in energy analysis results.
Cite this article as: Acar U, Kaşka Ö, Tokgöz N. The effects of different typical meteorological year data onthe heating and cooling demand of buildings: Case study of Turkey. J Ther Eng 2022;8(5):667-680.
... The conventional TMY is composed of twelve typical meteorological months (TMMs), which are selected from the multiple years (e.g., 1979-2015 in this study) using the Sandia method [34,39]. The selection of TMMs is briefly introduced as follows. ...
Proper sizing of energy systems in a zero-energy building is crucial to ensure that the zero-energy building can perform well during its operational process. Meteorological data should be well prepared as they are important inputs for the system design of a zero-energy building. Particularly, the extreme and typical weather data are crucial for the sizing of air-conditioning system and renewable system, respectively. However, the existing weather datasets including typical meteorological year (TMY) and extreme meteorological year (XMY) shows limited extreme and typical weather information of multiple years, which would lead to improper system sizes and consequently result in deteriorated building performance. To fill this gap, this study develops a calibrated TMY weather file by applying a quantile mapping method on conventional TMY using multi-year weather data, which can accurately represent both extreme and typical weather information of multiple years, overcoming the limitations of the existing weather datasets. In the validated case, the calibrated TMY is developed using the weather data of 1979–2015 purchased from the Hong Kong Observatory. The results indicate that in comparison with the conventional TMY-based design, the calibrated TMY-based design substantially improves the building performance with reduction of 28 unmet hours, 11% improvement of load match ratio and 10.4% reduction of lifecycle cost. Therefore, the calibrated TMY shows great potentials to replace the conventional TMY for zero-energy building system design in practice.
... Notably, the outdoor temperature data is selected directly from the local weather stations such as Ladybugs and Energy Plus weather website instead of the temperature of specific location, leading to the inequality of idealised and realistic performances. It may be caused by solar exposure, different shadings, heat island effects and fabric heat release, especially in dense urban areas [13]. In Lupato's research [14], the simulation databases of the new and old weather files do not match, thus the reference location is used to compare the differences. ...
Building baseline energy simulation models are usually inaccurate validation with real-data measurements, failing to predict post-retrofit energy performance and a further selection of retrofit measures. This paper investigated a data-driven based validation and calibration approach with different input outdoor temperature sources to analyse its impact on building baseline energy consumption. Based on the developed degree day concepts, baseline energy performance prediction is performed and compared with measurements to identify the impact of different degree day concepts on space heating electricity usage. This approach is applied to a 1940s Madrid end-terraced residential building. Energy simulation models were created using IES VE simulation software with three outdoor temperature sources. The electricity data are measured in the REZBUILD project before the retrofit, with baseline measured main supply electricity consumption of 6553.7kWh in one year. Results indicate that approximately 4℃ difference in outdoor air temperature occurs between on-site and local weather stations' outdoor temperature. Meanwhile, during space heating periods, the average indoor air temperature of 19.2°C in type 1, 16.9°C in type 2 and 15.6°C in type 3 could not meet the indoor thermal comfort setting temperature of 21°C due to limited heating system capacity. Compared with measurement results, the main supply electricity consumption has differences of 1.5% (type 1), 23.1% (type 2) and 26.1% (type 3). Meanwhile, the Heating degree days (HDD) concept could not comprehensively consider the energy use in real buildings, especially those with limited space heating capacity, poor insulation fabric, and related occupancy behaviours. Therefore, considering buildings with uncomfortable indoor environments, it is recommended to integrate baseline energy consumption with HDD and Heating discomfort degree days (HDDD) concepts, which can be helpful to analyse the heat loss compositions in-depth and predict the post-retrofit energy consumption based on the accurate baseline results.
... In Estonian, Kalamees and Kurnitsky created TRY for heating and cooling energy calculations [10]. In South Korea, Lee et al. generated TRY from 20 years of meteorological data according to ISO15927 standards for seven major cities [11]. Zang et al. proposed a modified method for the generation of TMY for 35 cities in China from recorded weather data [12]. ...
Future weather file Statistical and dynamical downscaling Energy use intensity Greenhouse gas emission A B S T R A C T In recent years, the building sector has received increasing attention with attempts to limit its energy con-sumptions and GHG emissions. In fact, buildings account for more than 30% of the overall energy demand worldwide, with projections for increases in this quota due to climate changes, urbanization, and higher living comfort standards. This study investigates the effects of climate changes on the heating and cooling energy demand of buildings in the most populated urban region in Canada, i.e. the city of Toronto in Ontario. Statistical and dynamical downscaling methods are utilized to generate several future weather files, starting from different baseline climates including the old Canadian Weather Year for Energy Calculation CWEC (representing the 1959-1989 period) and the new CWEC 2016 (representing the 1998-2014 period). In dynamical downscaling, a regional climate model is used to obtain a finer resolution than traditional general circulation models. The generated future weather data sets are then used for simulating the energy demand of 16 building prototypes. The simulation results show an average decrease of 18%-33% for the heating energy use intensity, and an average increase of 15%-126% for the cooling energy use intensity by 2070, depending on the baseline climatic file of use and building typology. The forecasted GHG emissions of each building prototype are then discussed. The results demonstrate the need to perform building modelling with sensitivity analysis of future climate scenarios in order to design more resilient buildings.
... In Estonian, Kalamees and Kurnitsky created TRY for heating and cooling energy calculations [10]. In South Korea, Lee et al. generated TRY from 20 years of meteorological data according to ISO15927 standards for seven major cities [11]. Zang et al. proposed a modified method for the generation of TMY for 35 cities in China from recorded weather data [12]. ...
Office buildings are responsible for a substantial portion of the energy demand in the commercial sector. To better understand and address the impacts of climate change on their energy demand and comfort levels, this paper investigates office buildings located in extremely cold, cold-humid, and cool-humid Canadian climate zones. Building energy simulations are performed using climate projections for the 2056-2075 period. The effect of extending thermostat setpoints, as a demand response strategy on reducing energy demand, is also studied under future climate conditions. The results quantify the expected decrease in the heating and the increase in the cooling loads due to the future warmer temperatures across Canada. However, the magnitude of change varies significantly among the three selected climate zones. Extending the temperature setpoints would reduce the annual energy demand by 8.0-19.2% in Quebec City, 1.8-9.0% in Toronto, and 1.8-9.6% in Vancouver. For all three selected cities, extending the temperature setpoints result in a substantial percentage of zones with a predicted mean vote (PMV) outside of the ±0.5 range. The benefits of increased levels of insulation for reaching thermal comfort during cold winter days and the penalty that would occur in summer days are assessed. Finally, the greenhouse gas emissions for the present and forecasted future energy demand of heating and cooling are determined.
... Many of the previous studies listed in Table 1 focused on generating typical meteorological years in different location with different recording period and selection method (i.e. Filkenstein-Schafer statistical method, Modified Festa-Ratto method, and Argiriou models) [12][13][14] . However, many researchers proposed that Filkenstein-Schafer statistical method is the best method with high accuracy compared to other methods 15) . ...
Typical meteorological year (TMY) data has significant importance for solar resource assessment, as well as for building performance analysis. The necessity of high-accuracy TMY data has been well known for many years for the financial viability of solar long-term planing project as it represents long-term weather features. However, the TMY does not mean the use of real-time data; it is therefore important to determine the accuracy of the TMY dataset. In this study, the solar energy performance of TMY was evaluated quantitatively by comparing it to multi-year average weather data (2008-2017) based on the statistical analysis method. The PV power output were produced by using 100kW photovoltaic system based on PVsyst software at six sites. The results showed that the difference between the TMY-PV power output and long-term PV power output (measured with percentage error) are smaller than 9%, which means there is close-fit agreement between TMY and long-term averages PV power output. These findings suggests that TMY can provide reliable estimation of PV power in the feasibility study of PV project.
... Las sucesivas versiones de los TMY (Typical Meteorological Year) de EEUU difieren en la cantidad y calidad de las series de datos base y en actualizaciones de implementación derivadas de la inclusión de mayor cantidad de datos, con pequeñas variaciones metodológicas. Variaciones de esta metodología han sido utilizadas para confeccionar series típicas en otras partes del mundo (Bulut, 2004;Skeiker, 2004;Chan et al., 2006;Anderson et al., 2007;Lee et al., 2010;Ohunakin et al., 2013;Pusat et al., 2015). La metodología SANDIA toma como sub-serie temporal la escala mensual, por lo que el problema consiste en definir los 12 meses típicos (uno para cada mes del año) y concatenarlos en forma consistente en unaúnica serie anual horaria, donde cada mes típico es elegido del conjunto de meses-año disponibles para ese mes. ...
Conocer el recurso solar disponible es una necesidad básica para el desarrollo de los emprendimientos que aprovechan este tipo de energía. La incertidumbre con que se conoce el recurso es el principal factor que afecta la viabilidad financiera de los proyectos de mediano y gran porte. El primer Mapa Solar del Uruguay (MSUv1), concluido en 2009, representó un primer esfuerzo por sistematizar el conocimiento del recurso solar en Uruguay. Sin embargo, debido a la escasa información disponible en la época y a la metodología utilizada, el MSUv1 presenta fuertes limitaciones. Estas limitaciones pueden ser resueltas utilizando un modelo basado en imágenes de satélite que haya sido ajustado y validado con medidas en Tierra de buena calidad. En esta tesis desarrollamos y optimizamos un modelo satelital adaptado a las particularidades de la región que permite estimar con baja incertidumbre la irradiación solar hora a hora para cualquier punto del territorio, desde el año 2000 a la fecha. Este modelo fue el mejor de una serie de modelos empíricos que implementamos, todos ellos ajustados y evaluados utilizando los datos de irradiación solar registrados en las redes de medidas del Laboratorio de Energía Solar (LES) y la empresa eléctrica estatal (UTE). No obstante su simplicidad, todas las comparaciones realizadas, ya sea a corto o largo plazo, contra datos u otros modelos satelitales, concluyen que el desempeño del modelo es excelente y que es la alternativa de menor incertidumbre para estimar el recurso en la región, incluso frente a modelos comerciales más sofisticados.
El modelo es aplicado para construir mapas mensuales y anuales del potencial solar, que conforman la segunda versión del Mapa Solar del Uruguay (MSUv2) y representan un avance sustancial en la cantidad y calidad de la información disponible del comportamiento de largo plazo del recurso. Esta nueva versión reduce la incertidumbre del mapeado anual y mensual de 15 % a 2 % y aumenta la resolución espacial de 150 km a 3 km, además de incluir componentes de la radiación solar con interés ingenieril que no habían sido mapeadas hasta la fecha y un mapa de potencial de generación fotovoltaica. La información contenida en el MSUv2 incluye también una caracterización de la variabilidad inter-anual y la información necesaria para manejar el riesgo financiero de los proyectos de energı́a solar. La posibilidad de generar estimaciones horarias de más de 15 años de irradiación solar permitió la elaboración de series horarias típicas para la simulación detallada de emprendimientos solares. Estas series conforman el Año Meteorológico Típico para aplicaciones de Energía Solar (AMTUes) y son representativas de la media de largo plazo y la variabilidad horaria típica. Además de incluir la irradiación solar, en el AMTUes se incluyen otras variables necesarias para las simulaciones, como la temperatura ambiente y la humedad relativa, entre otras. Este conjunto de herramientas desarrolladas abren paso a una mayor y mejor utilización de la energía solar en Uruguay.
Finalmente, esperamos que la infraestructura y las capacidades locales generadas a lo largo de este trabajo, tanto en la recepción y procesamiento de información satelital, como en la base de información sobre el recurso solar en Uruguay, puedan ser aprovechadas por actores del sector público o privado para sus fines específicos.
... Currently, the climate model in the form of a reference year is considered productive [7][8][9][10][11][12][13][14]. It provides hourly data of various meteorological and actinometric indicators for all months of the year, selected from a long-term sample in such a way that monthly average climatic data characteristics approach their long-term average values, and their scatter also most fully reflects the scatter of individual parameter values in each month [15,16]. This model is good for solving problems with thermal inertia objects. ...
In the present work, climate models were presented, which are intended for calculating energy consumption of air conditioning systems, spent on processing the outdoor air to supply it to the room as a supply air, and on the assimilation of heat from external and internal sources. Currently, the number of buildings is growing, in which the bulk of the heat surplus is made up of internal heat. Therefore, for such buildings, a climate model is needed that contains only data on the repeatability of combinations of temperature and humidity of the outside air. Such a model is a probabilistic-statistical climate model. To calculate the energy consumption of air conditioning systems in rooms with a load formed due to heat transfer through external building envelopes and the direct penetration of the heat of solar radiation, we need a model that contains not only the mentioned probabilistic characteristics, but also changes over time combinations of climate parameters. Such a model is a reference year. The article presents data on the development of a reference year for the city of Hanoi and shows that when assimilating only internal heat surpluses it is advisable to use a probabilistic-statistical model, that provides a much more accurate result.
... Wind speed is used as a secondary variable in the final selection of the typical month among the 3 candidates previously selected. The above method has been used for the construction of two TRYs for Greenland (Kragh et al., 2005), for obtaining it in 6 locations in Estonia (Kalamees and Kurnitski, 2006) as well as for 7 cities in South Korea (Lee et al., 2010) and for Alūksne (Latvia) (Ruduks and Lešinskis, 2015). Pernigotto et al. (2014a) used this procedure to evaluate the energy performance of buildings in 5 cities in northern Italy, while García and Torres (García and Torres, 2015) demonstrated the applicability for a different purpose, that is, the long-term evaluation of photovoltaic systems. ...
When assessing the long-term daylight availability or the performance of natural lighting systems in a given location, it is necessary to have representative data of local daylight conditions. The use of a daylight test reference year (TRY) becomes a good option in these cases. This paper proposes and evaluates a procedure for the generation of a typical illuminance year (TIY) considering illuminance as the only variable for selecting the typical periods that make up the reference year. Two versions of TIY are presented, one composed of 12 typical months selected from the series of observations and another composed of 365 typical days. Each of these versions is used to obtain a global illuminance TIY (TGIY) and a diffuse illuminance TIY (TDIY) from a 27-year dataset corresponding to the Vaulx-en-Velin station (France). Furthermore, 12 luminous efficacy models have been evaluated in order to obtain a TIY from a TRY generated from irradiance data when no illuminance data are available. Thus, a global luminous efficacy model and a diffuse model are selected after benchmarking different models, considering both their original coefficients and those adjusted to local conditions. The results reveal that the monthly version of the TGIY and the daily version of the TDIY show the best overall fit to the long-term dataset. TIYs obtained from illuminance data are also observed to be statistically indistinguishable from those obtained after applying a luminous efficacy model to an irradiance-based TRY.
... The most basic analyses are statistical maximum, minimum and mean values. Data is also commonly statistically processed into a one representative year models such as Typical Meteorological Year (TMY) [5], [6], Test Reference Year (TRY) [7], and Model Year Climate (MYC) [8]. The one-year models are widely used in building and energy simulation tools for performance predictions. ...
This aim of this paper is to evaluate the accuracy of long-term weather data models for performance prediction of grid-connected photovoltaic (GCPV) systems. The analyses were done for a 6-year old metal deck roof retrofitted GCPV system located in Shah Alam, Malaysia. The monthly and annual energy yield of the actual field data for three consecutive years were compared with the predicted yield using the long-term weather data models. These models were the Typical Meteorological Year (TMY), Model Year Climate (MYC), Microclimate data, and statistical Long-Term Mean for ground station data at Subang. The findings can be a reference for photovoltaic (PV) system designers on the range of accuracy when using the weather data models for performance predictions of GCPV system in Malaysia.
... In Estonian, Kalamees and Kurnitsky created TRY for heating and cooling energy calculations [10]. In South Korea, Lee et al. generated TRY from 20 years of meteorological data according to ISO15927 standards for seven major cities [11]. Zang et al. proposed a modified method for the generation of TMY for 35 cities in China from recorded weather data [12]. ...
Future weather file Statistical and dynamical downscaling Energy use intensity Greenhouse gas emission A B S T R A C T In recent years, the building sector has received increasing attention with attempts to limit its energy con-sumptions and GHG emissions. In fact, buildings account for more than 30% of the overall energy demand worldwide, with projections for increases in this quota due to climate changes, urbanization, and higher living comfort standards. This study investigates the effects of climate changes on the heating and cooling energy demand of buildings in the most populated urban region in Canada, i.e. the city of Toronto in Ontario. Statistical and dynamical downscaling methods are utilized to generate several future weather files, starting from different baseline climates including the old Canadian Weather Year for Energy Calculation CWEC (representing the 1959-1989 period) and the new CWEC 2016 (representing the 1998-2014 period). In dynamical downscaling, a regional climate model is used to obtain a finer resolution than traditional general circulation models. The generated future weather data sets are then used for simulating the energy demand of 16 building prototypes. The simulation results show an average decrease of 18%-33% for the heating energy use intensity, and an average increase of 15%-126% for the cooling energy use intensity by 2070, depending on the baseline climatic file of use and building typology. The forecasted GHG emissions of each building prototype are then discussed. The results demonstrate the need to perform building modelling with sensitivity analysis of future climate scenarios in order to design more resilient buildings.
... Hence, satellite data can provide the basis for a more physically realistic HD and CD data product. Typically, computer simulations of buildings and solar energy systems [49,50] use test reference years (TRYs) and typical meteorological years (TMYs), which represent a year of hourly weather data values. TRYs and TMYs are extracted from long-term data records (a minimum of 10 years), do not provide information on extreme events, and do not necessarily represent actual conditions at any given time. ...
The urban heat island (UHI) effect influences the heating and cooling (H&C) energy demand of buildings and should be taken into account in H&C energy demand simulations. To provide information about this effect, the PLANHEAT integrated tool-which is a GIS-based, open-source software tool for selecting, simulating and comparing alternative low-carbon and economically sustainable H&C scenarios-includes a dataset of 1 × 1 km hourly heating and cooling degrees (HD and CD, respectively). HD and CD are energy demand proxies that are defined as the deviation of the outdoor surface air temperature from a base temperature, above or below which a building is assumed to need heating or cooling, respectively. PLANHEAT's HD and CD are calculated from a dataset of gridded surface air temperatures that have been derived using satellite thermal data from Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI). This article describes the method for producing this dataset and presents the results for Antwerp (Belgium), which is one of the three validation cities of PLANHEAT. The results demonstrate the spatial and temporal information of PLANHEAT's HD and CD dataset, while the accuracy assessment reveals that they agree well with reference values retrieved from in situ surface air temperatures. This dataset is an example of application-oriented research that provides location-specific results with practical utility.
... All weather data were obtained from the Czech Hydrometeorological Institute, which is the official authority for meteorology, climatology, hydrology, and air quality protection in the Czech Republic. All data were applied in the form of the Test Reference Year (TRY) [26][27][28]. The data included hourly values of temperature, relative humidity, precipitation, wind direction, wind velocity, diffuse and direct short-wave radiation, sky long wave emission radiation, and long wave emission radiation. ...
In this study, we present a method for the rapid evaluation of thermal performance of building envelopes without the need of using sophisticated and time-consuming computational modeling. The proposed approach is based on the prediction of monthly energy balances per unit area of a wall assembly using monthly averages of temperature and relative humidity, as well as the elevation of a building's location. Contrary to most other methods, the obtained results include how moisture content in the wall effects its thermal performance. The developed formulas for calculation of monthly energy balances are verified for nine commonly used wall assemblies in Central Europe in 10 randomly selected locations. The observed agreement of the predicated data was determined using advanced finite-element simulation tools and hourly climatic data, which makes for good prerequisites for the further application of the method in both research and building practices.
... As a peninsula located at the intersection of the Asian continent and the Pacific Ocean, Korea is in a temperate climate zone and consumes a high amount of energy for both heating and cooling [26]. Given existing research, the effects of applying BIPV windows in Korea are expected to vary according to the design and operating conditions of particular buildings. ...
This study analyzed the heating and cooling performance of an office building in Daegu, Korea, equipped with amorphous-Si (a-Si) building-integrated photovoltaic (BIPV) windows. EnergyPlus was used to simulate and compare the heating and cooling loads of models for clear glass double-layer, heat-absorbing glass double-layer, and low-emissivity (low-e) glass double-layer windows. In addition, the impact of changes in building operation time, temperature settings, air infiltration from the entrances, and internal load were also analyzed as these all have a large impact on heating and cooling loads. Finally, three types of heating and cooling equipment were tested, and their power and primary energy consumption analyzed, to determine the actual energy used. Under baseline conditions, there was an 18.2% reduction in heating and cooling loads when the BIPV model was used compared to when the clear glass double-layer window was used. In addition, increases in temperature settings and air infiltration from the entrances had a negative effect on the reduction of the heating and cooling loads demonstrating a need for intensive management of these features if a-Si BIPV windows are installed in a building.
... For Belgium, the existing typical years (Test Reference Year or TRY [63], International Weather year for Energy Calculations or IWEC [64,65] and Meteonorm Typical Years [66]) are based on observational data. These cover at least one decade [67,68] and are available for three locations (Ostend, Uccle and Saint-Hubert) with the exception of the Meteonorm data. The Meteonorm software moreover allows to extract a typical year applying different methodologies (i.e. ...
As buildings have a relatively long life span, it is important to consider climate change in energy performance modelling. Good quality weather data are needed to obtain accurate results. This chapter discusses widely used methods to predict future weather data (dynamical downscaling, stochastic weather generators and morphing) and provides an overview of available weather datasets (multi-year, typical years, extreme years and representative years) for building simulations. A Flemish office building is used for a comparative analysis of the estimated heating and cooling load making use of 1-year weather files (typical and extreme future climate conditions) derived from a recently developed convection-permitting climate model for Belgium. Climate models and weather generators are identified as the most preferred for the estimation of the average energy consumption and thermal comfort in average and extreme situations. Climate models have the advantage to better represent extreme weather events and climate differences due to territorial settings, while weather generators can generate multiple climate realizations. A combination of a typical year with an extreme cold and extreme warm year was found to result in an overall good representation of the energy need for heating and cooling in average and extreme weather conditions. Further, the influence of the methodological choices to extract 1-year weather files (typical or extreme years) from the 30-year climate data is highlighted as different results were obtained when different meteorological variables were considered for the creation of the 1-year files.
... All weather data were obtained from the Czech Hydrometeorological Institute, which is the official authority for meteorology, climatology, hydrology, and air quality protection in the Czech Republic. ey included hourly values of temperature, relative humidity, precipitation, wind direction, wind velocity, diffuse and direct shortwave radiation, sky longwave emission radiation, and longwave emission radiation and were provided in the form of the test reference year (TRY) [19][20][21]. e list of involved locations together with their elevations is shown in Table 1. e map of weather stations is depicted in Figure 1. ...
A comprehensive analysis of environmental loads across the Czech Republic in terms of frost-induced damage is presented. Computational simulation of hygrothermal performance of eleven characteristic types of building envelopes, composed of both contemporary and historical materials, is performed at first. The exterior boundary conditions of the computational model are defined by a set of weather data characterizing the environmental conditions in the Czech Republic, which are acquired from 64 weather stations. The results of hygrothermal simulations are assessed using several specific damage functions. In this way, the basic datasets for the frost damage analysis are obtained. Their application as input parameters of a specially developed correction procedure based on elevation makes them possible to obtain a continuous coverage of the geographic area of the Czech Republic. Finally, isopleths of the supposed frost damage are drawn, depending on the envelope type, and damage maps are produced which may help the engineers to enhance the building envelope design process. The presented results indicate the necessity of paying attention to local environmental loads in the building enclosure design process and reveal both critical and favorable locations from the point of view of frost-induced damage to buildings.
... Todėl dėl gamtinių sąlygų būsimos kaitos arba jos tikslaus nežinojimo kyla poreikis taikyti tikimybinius parametrų nustatymo metodus atsižvelgiant į praeityje sukauptus duomenis ir jų kitimo tendencijas. Lyginant vieną laikotarpį su kitu pasitaiko ryškesnių požymių, todėl tikimybinis gamtinių sąlygų galimo kitimo ateityje įvertinimas yra aktualus ir plačiai taikomas, ypač vėjo energijos naudojimo srityje (Foley et al. 2012 (Lee et al. 2010), apimančius vienų metų laikotarpį. Šie duomenys gaunami statistiškai apdorojant matavimais sukauptus meteorologinius duomenis, kad rezultatas atspindėtų bendrą gamtinių sąlygų vaizdą (Skeiker et al. 2009). ...
... All weather data were obtained from the Czech Hydrometeorological Institute, which is the official authority for meteorology, climatology, hydrology, and air quality protection in the Czech Republic. They included hourly values of temperature, relative humidity, precipitation, wind direction, wind velocity, diffuse and direct short wave radiation, sky long wave emission radiation and long wave emission radiation and were provided in the form of the Test Reference Year (TRY) [7][8][9]. The list of involved locations together with their elevations is shown in Table 1. ...
Heating and cooling energy demands of two selected building enclosures are analyzed for the whole territory of the Czech Republic. For that reason, a series of computational simulation is carried and the results are post-processed by specially developed correction procedure allowing plotting smooth isopleths from limited input data. The results show significant differences in annual energy consumption through unit are of the wall.
... Although this standard describes a procedure for generating a reference year suitable for evaluating the annual heating and cooling requirements in buildings based on long-term data, it was found that the same reference year can also be used to predict the output of solar photovoltaic energy systems [11]. The resulting data series consists of 8760 hourly values of various selected meteorological parameters such as the air temperature, solar radiation, relative humidity, and wind speed [12]. Several authors [12e14] reported TRY data for different locations around the world. ...
... Alternatively, some authors in the literature (e.g., [46][47][48]) chose the reference month simply taking the one with the lowest value of weighted Finkelstein-Schafer statistics. ...
There are several methods in the literature for the definition of weather data for building energy simulation and the most popular ones, such as typical meteorological years and European test reference years, are based on Finkelstein–Schafer statistics. However, even starting from the same multi-year weather data series, the developed reference years can present different levels of representativeness, which can affect the simulation outcome. In this work, we investigated to which extent the uncertainty in the determination of typical weather conditions can affect the results of building energy refurbishment when cost-optimal approach is implemented for the selection of energy efficiency measures by means of the NSGA-II genetic algorithm coupled with TRNSYS simulations. Six different reference years were determined for two north Italy climates, Trento and Monza, respectively in the Alpine and in the continental temperate regions. Four types of energy efficiency measures, related to both building envelope and HVAC system, were applied to six existing building typologies. Results showed how the choice of reference year can alter the shape of the Pareto fronts, the number of solutions included and the selection among the alternatives of the energy efficiency measures, for the entire front and, in particular, for energy and economic optima.
... A lack of DNI measurement data has been a major obstacle to meaningful studies. Recently, Lee et al.[7]modeled GHI with cloud cover data for major cities in Korea and, successively, Lee et al.[8]reported a solar radiation model developed for estimating DNI with GHI. However, the model of Lee et al.[8]tends to underestimate DNI data such that DNI values exceeding 750 W/m 2 seldom occur. ...
Reliable solar radiation data are important for energy simulations in buildings and solar energy systems. Although direct normal irradiance (DNI) is required for simulations, in addition to global horizontal irradiance (GHI), a lack of DNI measurement data is quite often due to high cost and maintenance. Solar radiation models are widely used in order to overcome the limitation, but only a few studies have been devoted to solar radiation data and modeling in Korea. This study investigates the most suitable solar radiation model that converts GHI into DNI for Korea, using measurement data of the city of Daejeon from 2007 to 2009. After ten existing models were evaluated, the Reindl-2 model was selected as the best. A new model was developed for further improvement, and it substantially decreased estimation errors compared to the ten investigated models. The new model was also evaluated for nine major cities other than Daejeon from the standpoint of typical meteorological year (TMY) data, and consistent evaluation results confirmed that the new model is reliably applicable across Korea.
... Many efforts have been made to generate typical meteorological years in multitudinous locations globally, including Nigeria [14,15], Greece [16], Cyprus [17], Syria [18,19], Malaysia [20], Spain [21], Thailand [22,23], Saudi Arabia [24], South Korea [25], and Turkey [26]. In China, several typical year data sets have been published in recent years. ...
Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.
... A case study was conducted based on the verification model using Seoul weather data (Kwanho, 2010) which was generated by using ISO TRY method for thirty years. Unlike the verification model, the case study model excluded a BIPV part and was constructed to form the cavity of each floor as a single zone. ...
... Existing methods for making a typical weather year for BES generally connect monthly segments of weather data selected from MY meteorological records using summary statistics, such as monthly averages or Finkelstein-Schafer (FS) statistics [1][2][3]. For example, the Test Reference Year (TRY) by the Chartered Institution of Building Services Engineers (CIBSE) and International Weather Years for Energy Calculations (IWECs) by ASHRAE are made by this conventional method, based on FS statistics. ...
In building design or research processes, building energy simulations (BES) are conducted using weather data. There are two types of weather data for BES: typical weather year is used to estimate annual cooling/heating loads and design weather data to estimate maximum cooling/heating loads. In this study, we propose a new type of weather year data (called the Typical and Design Weather Year: TDWY) that can be used as both typical weather year and design weather data. To create the TDWY, we selected an average year based on Finkelstein-Schafer statistics and applied quantile mapping (QM) to the average year with parent multi-year (MY) weather data. The cumulative distribution functions of the TDWY created by QM consist completely of parent MY weather data for all the weather components used in QM. As the monthly and annual averages of the TDWY based on QM are equal to those of the parent MY weather data, high performance of the TDWY as typical weather year can be expected. In addition, the hourly values of the TDWY include from the minimum value to the maximum value of the parent MY weather data each month, so the TDWY can also be used as design weather data. To validate the performance of the TDWY, we conducted BES. The TDWY showed better than double the performance for estimating average cooling/heating loads compared to the existing typical weather year and could accurately estimate maximum cooling/heating loads.
... For a network of stations in the United States, a representative database consisting of weather data was created. Hall's method has been used to successfully generate TMYs for a number of locations across the globe (Chan et al., 2006, Guggenberger et al., 2013, Hall et al., 1978, Jiang, 2010, Kalogirou, 2003, Lee et al., 2010, Skeiker, 2004, Skeiker, 2007, Yang et al., 2007, Zang et al., 2012, Zariņš, 2001, Zhang, 2006. ...
Meteorological conditions vary significantly from year to year. For this reason, there is a need to create a typical meteorological year (TMY) data model, to represent the long term weather conditions over a year. TMY data is one of the main sources for successful building energy simulations. Two different typical meteorological data models were generated and compared: TMY and TMY-2. Both models where created by analysing every 3-hour weather data for a 30-year period (1984–2013) in Alūksne, Latvia, provided by the Latvian Environment Geology and Meteorology Centre (LEGMC). TMY model was created using statistical approach, but to create second model - TMY-2, 30 year average data were applied. In the TMY model creation representative typical meteorological months (TMM) were selected. TMM for each of the 12 calendar months were selected by choosing the one with the smallest deviation from the long-term average weather data. The 12 TMMs, selected from the different years, were used to create a TMY for Alūksne. The data gathered from TMY and TMY-2 models where compared with the climate data from the Latvian Cabinet of Ministers regulation No. 379, Regulations Regarding Latvian Building Code LBN 003-01. Average monthly temperature values in LBN 003-01 were lower than the TMY and TMY-2 values. TMY selection process should include the most recent meteorological observations and should be periodically renewed to reflect the long term climate change.
... Estes dados meteorológicos são muitas vezes gerados através de métodos estatísticos que recorrem a bases de dados horárias. Em [7] Em [10,11,12,13], são apresentados vários TRY para diferentes locais em diferentes partes do globo, que são referidos aqui como exemplo do tipo de análise estatística que é feita com o objetivo de ser utilizada na estimativa da produção de sistemas de energia solar. ...
This dissertation consists on determining a typical meteorological year for several
locations in Madeira Island, on a PV system modelling and on the power production
estimate. The typical meteorological year construction was done with the help of a
Matlab algorithm, developed for this purpose, and it’s based on data series of solar
radiation, temperature, relative humidity and wind velocity. The PV cell modelling was
based on one diode, i.e., three parameters model, implemented with a Matlab model
algorithm and validated through the manufacturer’s data. The power production
estimate was done through the typical meteorological year obtained, and ArcGIS Pro
was used to generate a map with that estimate. The higher production value was 1922.1
hours at peak power, obtained in Areeiro, and it was obtained a value of 1672.1 hours at
peak power in Funchal, Madeira Island, Portugal.
... The exterior climatic conditions during the experiment are given in Figure 3. In the experimental and numerical simulations it is very common to use Test Reference Years (TRY) [15] or Typical Meteorological Years (TMY) [16] as input data. Unfortunately, there are some drawbacks within the use of this data as the construction of TRY or TMY consists in averaging of real weather data from several decades from the past. ...
Hygrothermal performance of a building envelope based on cellular concrete blocks is studied in the paper. Simultaneously, the strain fields induced by the heat and moisture changes are monitored. The studied wall is exposed to the climatic load corresponding to the winter climatic conditions of the moderate year for Prague. The winter climatic exposure is chosen in order to simulate the critical conditions of the building structure from the point of view of material performance and temperature and humidity loading. The evaluation of hygrothermal performance of a researched wall is done on the basis of relative humidity and temperature profiles measured along the cross section of the cellular concrete blocks. Strain gauges are fixed on the wall surface in expected orientation of the blocks expansion. The obtained results show a good hygrothermal function of the analyzed cellular concrete wall and its insignificant strain.
... The following Table 2 lists these input. All data are the monthly parameters referred to the so-called Test Reference Year (TRY), i.e. a dataset of meteorological variables throughout a year, representing reference data in the region so that no biases due to the annual anomalies occur [72,73]. The TRY is from the time series collected, pre-processed, filtered and checked by the Italian National Agency for New Technologies, Energy, and Sustainable Economic Development (ENEA) [74]. ...
The availability of reliable climatologic data is essential for multiple purposes in a wide set of anthropic activities and operative sectors. Frequently direct measures present spatial and temporal lacks so that predictive approaches become of interest. This paper focuses on the prediction of the Monthly Average Daily Global Solar Radiation (MADGSR) over Italy using Artificial Neural Networks (ANNs). Data from 45 locations compose the multi-location ANN training and testing sets. For each location, 13 input parameters are considered, including the geographical coordinates and the monthly values for the most frequently adopted climatologic parameters. A subset of 17 locations is used for ANN training, while the testing step is against data from the remaining 28 locations. Furthermore, the Automatic Relevance Determination method (ARD) is used to point out the most relevant input for the accurate MADGSR prediction. The ANN best configuration includes 7 parameters, only, i.e. Top of Atmosphere (TOA) radiation, day length, number of rainy days and average rainfall, latitude and altitude. The correlation performances, expressed through statistical indicators as the Mean Absolute Percentage Error (MAPE), range between 1.67% and 4.25%, depending on the number and type of the chosen input, representing a good solution compared to the current standards.
... The most important part in hygrothermal analysis of any building construction is the input weather data. In the numerical simulations it is very common to use Test Reference Years (TRY) [1][2][3] or Typical Meteorological Years (TMY) [4][5][6] as input data. Unfortunately, there are some drawbacks within the use of this data as the construction of TRY or TMY consists in averaging of real weather data from several decades from the past. ...
... In particular, cloud cover data is used in both weather analyses and meteorological data, and is very important for solar energy and air traffic management. In studies to calculate solar radiation using information from clouds, Yoo et al. (2008) and Lee et al. (2010) used cloud cover data from the Korea Meteorological Administration (KMA) to estimate the solar radiation in major cities, and Kim et al. (2004) verified the monthly average sunshine duration through the relationship between cloud cover and solar radiation in the Seoul region of Korea. Furthermore, cloud cover has frequently been studied in the context of utilizing solar energy as a source of renewable energy. ...
Cloud cover information is used alongside weather forecasts in various fields of research; however, ground observation of cloud cover is conducted by human observers, a method that is subjective and has low temporal and spatial resolutions. To address these problems, we have developed an improved algorithm to calculate cloud cover using sky image data obtained with Skyviewer equipment. The algorithm uses a variable threshold for the Red Blue Ratio (RBR), determined from the frequency distribution of the Green Blue Ratio (GBR), to calculate cloud cover more accurately than existing algorithms. To verify the accuracy of the algorithm, we conducted daily, monthly, seasonal, and yearly statistical analyses of human observations of cloud cover, obtained every hour from 0800 to 1700 Local Standard Time (LST) for the entirety of 2012 at the Gangwon Regional Meteorological Administration (GRMA), Korea. A case study compared daily images taken at 1200 LST in each season with pixel images of cloud cover calculated by our improved algorithm. The selected cases yielded a high correlation coefficient of 0.93 with the GRMA data. A monthly case study showed low root mean square errors (RMSEs) and high correlation coefficients (Rs) for December (RMSE = 1.64 tenths and R = 0.92) and August (RMSE = 1.43 tenths and R = 0.91). In addition, seasonal cases yielded a high correlation of 0.9 and 87% consistency within ± 2 tenths for winter and a correlation of 0.83 and 82% consistency for summer, when cases of cloud-free or overcast conditions are frequent. Annual analyses showed that the bias of GRMA and Skyviewer cloud cover data for 2012 was −0.36 tenth, and the RMSE was 2.12 tenths, with the GRMA data showing more cloud cover. Considering that the GRMA and Skyviewer data were gathered at different spatial locations, GRMA and Skyviewer data were well correlated (R = 0.87) and showed a consistency of 80% in their cloud cover data (consistent within ± 2 tenths).
This study aimed to review the existing research focalizing on the missing data imputation techniques for
the systems enabling actual evapotranspiration calculation (such as eddy covariance, Bowen ratio, and
lysimeters) and divergent evapotranspiration related variables, i.e. temperature, wind speed, humidity,
and solar radiation. Thus, the Scopus engine was utilized to scan the entire literature and 62 articles were
diligently investigated. Results show classical approaches have been widely used by researchers due to
their ease of implementation. However, the applicability and validity of these methods heavily rely on
assumptions made about the distribution and characteristics of missing data. Hence, advanced imputation
techniques produce more accurate outcomes as they handle complex and non-linear problems. Also,
current trends embraced by the research community revealed that employing deep learning techniques
and incorporating explainable artificial intelligence into imputations have significant potential to make
insightful contributions to the body of knowledge.
This study compares cloud radiation model (CRM) and sunshine fraction radiation model (SFRM) according to the solar altitude using hourly sunshine duration (SD) and cloud cover (CC) data.
Solar irradiance measurements are not easy for the expensive measuring equipment and precise measuring technology. The two models with the site fitting and South Korea coefficients have
been analyzed for fourteen cities of South Korea during the period (1986-2015) and evaluated using the root mean square error (RMSE) and the mean bias error (MBE). From the comparison of the results, it is found that the SFRM with the site fitting coefficients could be the best method for fourteen locations. It may be concluded that the SFRM models of South Korea coefficients generated in this study may be used reasonably well for calculating the hourly horizontal global
irradiance (HGI) at any other location of South Korea.
Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002-2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.
It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
This study aims to estimate surface solar irradiation using sky view factor (SVF), sunshine factor (SF) and solar irradiation models by the fish-eye image. The SVF and the SF contribute the solar irradiation for shielding from the sky and obtaining from the sunshine. It is possible to generate the SVF and the SF values for calculating from the simple geometric measurements, fisheye images, and simple model. The case study is a new building in Ulsan and looking for the best orientation and slope using the drone with a fisheye lens. The study demonstrates that the SVF and the SF analysis using fish-eye image is a useful and effective tool for design, optimization and performance evaluation of solar technologies and building energy for any geographical locations and buildings.
Hourly weather data are needed for building energy calculations; however, many available data from previous years are 3‐hr‐based. Therefore, appropriate interpolation methods should be employed in order to fill the gaps of the data. In this paper, linear and cubic spline functions are used to interpolate the weather data of Kerman, Iran. Weather data have different behaviours in heating and cooling seasons. Therefore, this paper analyses the interpolation methods in heating and cooling seasons separately. In order to specify all the weather psychrometric characteristics, three independent parameters are required. First, temperature and pressure were taken as two of the parameters. Then, humidity ratio and relative humidity were tested and analysed as the third parameter. Furthermore, the methods of filling weather data gaps were examined in particular to their effects on calculating accurate energy consumption. The results showed that the best interpolation method for each variable depends on the criterion of accuracy. Each method gives the best accuracy for a specific case. However, for temperature and pressure data, cubic splines were more accurate than the linear ones in all cases. For other weather data, it is important to consider the purpose of filling the gaps. The appropriate criterion of accuracy is different according to whether the average or the extreme values of the data are required. For calculation of the energy consumption variables, one interpolation technique should be used for all weather elements; linear interpolation by using relative humidity as the third psychrometric variable yielded better results for this aim.
The deployment of solar energy projects in a given region requires a precise estimation of potential solar resources. For that purpose, generating a typical meteorological year is of great importance, although in principle it is a tool used in construction or engineering. Various methods for deriving typical meteorological years have been developed, but their final results can be significantly different. In this paper, two major methodologies (TMY3 method and ISO 15927-4 standard) were applied to 12-year measured data series recorded during the period 2003-2014 in Belsk, central Poland. The sums of global solar radiation obtained in typical meteorological years were compared to the long-term average measured sums of global solar radiation in order to decide which method can be recommended as best reflecting solar conditions in Poland. According to this study, the differences between the respective TMY data sets and long-term measured data set (measured with percentage root mean square error – %RMSE) are bigger than 5%. ISO 15927-4 standard slightly better approximates solar conditions in central Poland than TMY3 method – the %RMSE equals 5.25% and 6.71% respectively.
Along with growing concern about energy consumption from the building sector, computer simulations play an important role to analyze the performance of buildings and building systems. A test reference year (TRY) is widely adopted as a representative weather data to get reliable outcomes from the simulations. TRYs of the major 18 locations in South Korea were determined using the method presented by ISO 15927-4 to assure the objectivity of the results from the building energy simulations. The TRY should represent the main climate parameters of the long-term data as close as possible. TRYs were compared with the long-term measured data of 10 years to evaluate their representativeness. According to the statistical results, TRYs in this study have a good representativeness of the weather data for South Korea. The relative influence factors of different climatic parameters on the building energy are important to establish the strategies to minimize the energy consumption. The relative impacts of climatic parameters – air temperature, relative humidity, solar irradiance and wind speed - were numerically determined using a dynamic energy simulation and different types of buildings. It is obvious that air temperature has a strong effect on the energy demand in winter, but on the other hand, solar irradiance is the primary climatic parameter in summer. The energy demand caused by the dehumidification in summer should be considered with solar irradiance and air temperature for the climate of South Korea. Wind speed has a minor effect on the energy demand all year round.
Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design. The purpose of our work is to predict the surface temperature on inclined surfaces based on ISO-TRY typical weather data. To reach this goal, three studies were performed. They consisted of quantifying the accuracy of various well-known three models. The first type of models calculated diffuse horizontal irradiations from global ones and the second type models computed global irradiations on inclined planes from diffuse and global components on a horizontal surface. The third type of model calculated long-wave radiation and surface temperature from ISO-TRY typical weather data. The proposed model can provide an alternative to building designers in estimating the surface temperature and solar irradiation on inclined surfaces where only the typical meteorological data are available.
Computer simulation plays an important role in investigating the thermal/energy performance of buildings and energy systems. In order to reduce the computational time and provide a consistent form of weather data, simulation run with multi-year weather files is generally avoided. In contrast, representative weather data is widely adopted. For developing typical meteorological year (TMY) weather files, Sandia method is one of the commonly adopted approaches. During the generation of TMY, different weighting factors are assigned to some key climatic indices. Currently, the values of weighting factors mainly depend on the researchers' judgement. As these weighting factors can express the relative importance of impact of a particular climatic index on the thermal/energy performance of an energy system, computer simulation using different TMYs may lead to different conclusions. Therefore, it is inappropriate to apply one single TMY for all energy systems. In this study, a novel TMY weather file generator has been developed to link up an optimization algorithm and an energy simulation program. Through four application examples (one air-conditioned building and three renewable energy systems), this weather file generator demonstrated its capability to search optimal/near optimal combinations of weighting factors for generating appropriate TMY for computer simulations of different energy systems.
This paper reviews different methodologies available to generate typical meteorological year (TMY) data when long-term observed meteorological data is not available for a particular location, as is the case of most developing countries. The Type 54 weather generator incorporated in TRNSYS is the most common method to generate TMY data where observed meteorological data is not available. As a case-study to validate the Type 54 weather generator, average daily solar radiation generated by the Type 54 Weather generator was compared using two different inputs against known TMY data for the Australian cities of Canberra, Hobart and Melbourne. It was found that the percentage variation is less than 7% for inputs from long-term observed data and more than 7% for inputs from NASA as compared to the known TMY data.
Different meteorological data series called multiyear data, long-term average measured data series, or test reference years (TRYs) are required for solar energy system simulation. It is known that the use of the multiyear data approach requires a great effort in time and computation, long-term average measured data do not have the extreme values of weather data given along the year, and TRYs represent typical references rather than extreme conditions and facilitate the comparison in the performance of energy systems. In this paper, TRYs have been generated, using three different methodologies, from hourly meteorological data measured in two cities, Madrid and Valladolid (Spain). In order to evaluate them, the performance simulation of three solar energy systems (thermal, passive, and photovoltaic) with long-term measured meteorological data has been compared with estimated performance simulations with TRYs. Root-mean-square and mean bias errors and relative differences have been used as estimators to measure the performance deviation of TRYs from long-term measured meteorological data series. Results of the comparison show that the most appropriate method for generating test reference year depends on the characteristics of the station and varies from month to month. The Danish method (TRY5) gives better results in Valladolid than in Madrid for the photovoltaic and passive systems; the Argirious method (TRY6) gives better results in Madrid than in Valladolid for the photovoltaic system. The Pissimanis method (TRY4) is the best for simulating thermal and photovoltaic systems in summer.
This paper reports on the development of typical meteorological years (TMYs) for seven different locations in Oman based on measured meteorological data. Depending on the availability of data the TMYs developed using Sandia method used data covering 7–17 years. The method as implemented here in a step-by-step procedure with illustrations is made simple. The procedure described herein is computerized and can handle any number of data sets in an easy-to-use manner. This should facilitate the development of TMYs for any location where enough data is available. Sensitivity analysis of different weights assigned to different weather parameters shows that Sandia method is highly affected by solar flux even if its weight is reduced by half while the weights of other parameters such as temperature, wind, and relative humidity have less impact on the selection of TMY. Copyright © 2005 John Wiley & Sons, Ltd.
Energy consumption causes a range of environmental pollution. Furthermore, with the increase in energy demand, the issue of energy shortage becomes increasingly serious. Since there is more and more concern on energy conservation and environmental protection, interest has been increasingly focused on the use of renewable energy. This is considered as a key source for the future, not only for South Korea but also for the world (Ulgen and Hepbasli, 2002). Especially, solar energy as a clean energy source and one kind of renewable energy is abundant in South Korea. Therefore, the precise measurement of the local solar radiation is required.
Simple radiation estimation models using meteorologically observed input parameters are often used in the applications requiring rough estimations of solar horizontal radiation. One of the parameters employed, cloud cover, is widely available from multiple regional and national weather stations. Kasten and Czeplak, Muneer and Gul, and Lam and Li, have proposed cloud-based models for the estimation of global and diffuse horizontal irradiance. In the current study, three new cloud-based models have been proposed. To compare the validity of the above models, statistical indicators have been developed for assessing the accuracy of estimation of global, diffuse and beam horizontal irradiance. The slope of the best fit line and the coefficient of determination between measured and calculated values, mean bias error, and root mean square error were calculated. In addition, the kurtosis and skewness of error histograms were also obtained. These parameters were used to obtain accuracy indicators for the models under validation. It was concluded that the proposed models outperformed the Kasten / Czeplak, and Lam / Li models, in both their original and modified forms. Practical application: Solar radiation data is an essential part of designing passive and active solar systems in building services. However, the solar radiation data is not always measured at the desired location, but other meteorological data are more available, therefore, tools have been developed to estimate the solar radiation from other measured parameters such as cloud cover. The models that are proposed are more accurate than their predecessors are, and provide global, beam and diffuse horizontal irradiance estimations. List of symbols
In recent years, the evaluation of the efficiency of the performance of solar energy units is not done by using long-term averages of weather data as input but preferably by using data sets representative of the climatological features of the site that are generated for this purpose. Such data sets, which are usually called “Test Reference Year” or “Short Reference Year,” consist mainly of solar radiation data, but they may also include other meteorological data, like temperature wind velocity etc, which may affect the response of the units. In this article, an attempt is made for the generation of such a representative data set for the city of Athens mainly by following a method that has been proposed by Hall et al. This data set, which includes global solar radiation data and six other meteorological parameters referring to temperature, dew point, and wind velocity, has been characterized by Hall as a “Typical Meteorological Year.”
Ten-year (1964–1973) continuous records at Hamburg of hourly sums of solar and terrestrial, downward and upward radiation flux densities have been evaluated with regard to simultaneous hourly cloud observations. The irradiance at given solar elevation is plotted vs total cloud amount for each season and for the whole year; in the same way, the ratio of the irradiance under clouded to that under cloudless sky is presented. Additional diagrams show the irradiance under cloudless and under overcast sky as function of solar elevation. The ratio of global radiation at total cloud amount N okta, G(N), to global radiation at cloudless sky, G(0), at the same solar elevation γ turned out to be indepenent of γ and can be parameterized by . The influence of cloud type is demonstrated by diagrams showing the irradiance under skies overcast by a specific cloud type as function of solar elevation for each season and for the year; also, the corresponding ratios “overcast” to “cloudless” are presented. In the case of global radiation, the ratios which may be interpreted as the transmittances of the specific cloud types for global radiation, turned out to be independent of solar elevation and have the following mean values: Ci, Cc, Cs 0.61; Ac, As 0.27; Sc, Cu 0.25; St 0.18; Ns 0.16.
With the increasing use of simulation for building design, test reference years (TRYs) are required for energy analyses and design summer years (DSYs) for assessing natural ventilation in the summer. Previously TRYs and DSYs only existed for three sites in the UK. Also the data was derived from weather data up to 1995. More sites were required and also updated data, as a number of warm years had occurred after 1995. The opportunity was also taken to improve the derivation of the TRY using just the Finkelstein-Schafer statistic and also to improve the algorithm for smoothing between months. New programs had to be written for filling missing values in the lower-quality raw data. This paper describes these programmes, the quality assurance procedures and analyses the years produced. A comparison is made between the 14 sites demonstrating the link between dry bulb temperature, solar radiation and latitude.
The typical meteorological database for 57 Chinese locations was developed for building simulations and air-conditioning design. The database consists of three parts: the typical meteorological years (TMY), the typical meteorological days (TMD) and the design temperature and humidity (DTH). The typical meteorological year (TMY) is the main part of the database. Because there are not data on solar radiation in the observations, a method to estimate solar radiation with dry bulb temperature difference, relative humidity, total cloud cover and wind speed was developed. Methodologies of interpolations were developed to produce 1h data with the 3h data. The global solar radiation on the horizontal surface was separated into direct and diffuse components with the Gompertz function. The typical meteorological day (TMD) consists of the monthly average values of dry bulb temperature, solar radiation, relative humidity, etc. for each hour of the day. The design temperature and humidity (DTH) was developed for the purpose of air-conditioning design. The frequencies of 2.5% and 5.0% were selected to decide the design temperature and humidity for the 57 Chinese locations.
In recent years, the evaluation of the efficiency of performance of solar energy units and the digital simulation of the thermal system of a building are not done by using long term averages of weather data as input, but preferably are done by using data sets representative of the climatological features of the site that are generated for this purpose. The present study is concerned with the generation of such a representative data set for Damascus zone, mainly by following a method that has been proposed by Hall et al. This data set, which includes global solar radiation data and 10 other meteorological parameters referring to ambient temperature, dew point, relative humidity, atmospheric pressure, wind velocity and direction, global cloudless degree and cloud type and daily sunshine duration, has been characterized by Hall as a “Typical Meteorological Year”. The typical meteorological year was generated from the available hourly meteorological data recorded during the period 1981–1990, using the Filkenstein–Schafer statistical method.
In this paper, we discuss the possibility of using the daily sunshine duration or the daily sunshine duration over day-length data instead of daily global solar radiation in order to develop a Typical Meteorological Year, TMY2, for localities with abundant data of daily sunshine duration. The analysis is done using the latest methodology proposed in the literature. The number of coinciding months is about 40% when daily sunshine duration or the daily sunshine duration over day-length data is used instead of daily global solar radiation as one of the parameters in obtaining TMY2. Such a relatively low figure is probably due to the unreliable solar radiation data recorded by Robitzch-type actinographs used in the present work.
The generation of a typical meteorological year is of great importance for calculations concerning many applications in the field of thermal engineering. In this context, method that has been proposed by Hall et al. is selected for generating typical data, and an improved criterion for final selection of typical meteorological month (TMM) was demonstrated. The final selection of the most representative year was done by examining a composite score S. The composite score was calculated as the weighed sum of the scores of the four meteorological parameters used. These parameters are air dry bulb temperature, relative humidity, wind velocity and global solar radiation intensity.Moreover, a new modern software tool using Delphi 6.0 has been developed, utilizing the Filkenstein–Schafer statistical method for the creation of a typical meteorological year for any site of concern. Whereas, an improved criterion for final selection of typical meteorological month was employed. Such tool allows the user to perform this task without an intimate knowledge of all of the computational details. The final alphanumerical and graphical results are presented on screen, and can be saved to a file or printed as a hard copy. Using this software tool, a typical meteorological year was generated for Damascus, capital of Syria, as a test run example. The data processed used were obtained from the Department of Meteorology and cover a period of 10 years (1991–2000).
A test reference year for the city of Ibadan, Nigeria has been generated from a 10 yr global solar radiation data set using the Finkelstein-Schafer statistics. The representative year was identified for each month and the corresponding global solar radiation tabulated for ease of use in such applications as the performance analysis of solar systems.
The generation of a typical meteorological year (TMY) is of great importance for calculations concerning many applications in the field of thermal engineering. The need of an accurate TMY for simulations has been well recognized over the years. Various methods for deriving TMYs have been developed, but their final results can be significantly different. In this paper, the major methodologies reported in the literature were applied to 10 year hourly measurements of weather data from Damascus, Syria. The TMYs obtained were evaluated according to their impact on the typical Syrian building’s thermal system in order to decide which method should be recommended for generating typical meteorological years and for predicting the performance of thermal systems in buildings. Based on simulation results for seasonally, monthly and daily building thermal loads, three widely used statistical estimators, namely, root mean square difference RMSD, total standard error SEE and chi square χ2 were calculated to assess the performance of each TMY. The findings showed that the TMY giving the closest performance to the average performance of the building’s thermal system as predicted using the 10 year weather data is the one generated by using the modified Sandia method. This method gives sufficiently accurate results compared with the other methods reported in the literature.
In this paper, a new method for generating test reference year (TRY) from the measured meteorological variables is proposed. Hourly recorded data of air temperature, relative humidity and wind velocity for two stations, Valladolid and Madrid (Spain) were selected to develop the method and a TRY was obtained. Monthly average solar radiation values were calculated taking into account the temperature and solar radiation correlations. Four different methodologies were used to evaluate hourly global solar radiation from hourly weather data of temperature and, as a consequence, four different TRYs with common data sets of temperature, relative humidity and wind velocity were generated for Valladolid and Madrid (Spain) stations. In order to evaluate the four different methodologies, TRYs data were compared with long-term measured data series using statistical estimators such as average, standard deviation, root mean square error (rmse) and mean bias error (mbe). Festa and Ratto and the TAG model, from Aguiar and Collares-Pereira, respectively, turned out to be the best methods for generating hourly solar irradiation data. The best performance was shown by the TRY0 year which was based on the solar radiation models mentioned above. The results show that the best reference year for each site varies with the season and the characteristics of the station.
A procedure is presented which can be used to create, from many years of available past weather data, a Reference Year consisting in 8760 hourly values of a few chosen meteorological quantities. Such “year” corresponds to a “typical” year for the considered locality, regarding both the occurrence and the persistence of different meteorological situations, in all months. This approach is a modification of the procedure used in the production of Test Reference Years or of Design Reference Years. In this approach, the frequency distributions relative to single months in the database are compared with the long-term frequency distribution of all the months “with the same name.” Furthermore, the correlation between subsequent subsequent daily values is taken into account.
Energy consumption and performance investigations of environment-dependent systems such as building HVAC and refrigeration systems, solar collectors, cooling towers, usually require weather information. This introduces a problem because there may be significant variances between the recurrent days or years. In this work, typical hourly weather data for the selected 23 provinces that represent demographic and climatic conditions of Turkey are obtained by using actual recordings. The results are stored as computer files ready to be used by simulation programs. By using these typical meteorological years, heating and cooling degree-days, dry-bulb temperature bins and winter and summer design dry-bulb temperatures are calculated. Sample typical-year simulations show for example that energy savings of about 11 and 16 per cent could be expected in Ankara by 3 and 5 K night-setback, respectively. Copyright © 2000 John Wiley & Sons, Ltd.
Hourly data of global and diffuse solar radiation in combination with cloud type, cloud cover amount and sunshine duration records are analysed for two sites in Germany. The global solar radiation for overcast conditions at urban, maritime influenced Hamburg and at the rural mountain station Hohenpeissenberg decreased by an average rate of 8 per cent per decade between 1964 and 1990, whereas no significant trend was found under clear skies at either station. However, under partly cloudy conditions the diffuse portion of global solar radiation also declined. Although the total cloud cover amount changed little during the interval analysed, the frequency of cirrus cloud increased in Hamburg and Hohenpeissenberg by 12 per cent and 14 per cent respectively. This may have led to the decline of diffuse solar radiation in cloudy conditions. A shift from stratiform to more frequent convective clouds also occurred at both stations. At Hamburg and Hohenpeissenberg the mean direct solar radiation income was 10 W m−2 higher during hours with 100 per cent sunshine duration and observed clouds than during hours with totally clear skies. Possible reasons for the observed cloud changes include natural weather variability of convective and stratiform clouds, increase of aircraft traffic increasing the occurrence of cirrus clouds and an indirect aerosol effect on the maritime stratiform clouds. © 1997 Royal Meteorological Society.
Detailed hourly energy simulation was conducted for office buildings in the five major climate zones – severe cold, cold, hot summer and cold winter, mild and hot summer and warm winter – in China using multi-year (1971–2000) weather databases as well as typical meteorological years (TMY). The primary aim was to compare the energy simulation results from the TMY with those from individual years and their long term means. A total of 154 simulation runs were performed. Building heating and cooling loads, their components and energy use for heating, ventilation and air-conditioning were analysed. Predicted monthly load and energy consumption profiles from the TMY tended to follow the long term mean quite closely. Mean bias errors ranged from −4.3% in Guangzhou to 0% in Beijing and root-mean-square errors from 3% in Harbin to 5.4% in Guangzhou. These percentages were not always the smallest compared with the 30 individual years, however, they are at the lower end of the percentage error ranges. This paper presents the work and its findings.
Typical meteorological years (TMYs) for 60 cities in the five major climatic zones (severe cold, cold, hot summer and cold winter, hot summer and warm winter, mild) in China were investigated. Long term (1971–2000) measured weather data such as dry bulb and dew point temperatures, wind speed and global solar radiation were gathered and analysed. A total of seven climatic indices were used to select the 12 typical meteorological months (TMMs) that made up the TMY for each city. In general, the cumulative distribution functions of the TMMs selected tended to follow their long term counterparts quite well. There was no persistent trend in any particular years being more representative than the others, though 1978 and 1982 tended to be picked most often. This paper presents the work and its findings. Future work on the assessment of TMYs in building energy simulation is also discussed.
Solar radiation models based on meteorological parameters serve as the substitute to measured irradiation and illuminance data. Models originating from sunshine or cloud cover information constitute the two main classes of meteorological radiation models. Further improvements in the accuracy of these models is under research. One such approach, attempted by Page, has resulted in the formation of a model that utilises a combination of the above mentioned variables. This article briefly discusses the new combined approach and evaluates it against other models developed by the authors: the Meteorological Radiation Model (MRM) based on sunshine fraction and the Cloud Cover Radiation Model (CRM) based on cloud cover. Results show that Page’s approach is quite successful under overcast conditions. The MRM, however appears to provide better estimates under part clear and clear sky conditions.
Simulation packages for predicting building performance in terms of energy and comfort are becoming increasingly important in the planning process. However, current industry standard weather files for building simulation are not suited to the assessment of the potential impacts of a changing climate, in particular summer overheating risks. In addition, no bespoke climate change weather files are readily available that can be loaded directly into environmental simulation software. This paper describes the integration of future UK climate scenarios into the widely used Typical Meteorological Year (TMY2) and EnergyPlus/ESP-r Weather (EPW) file formats and demonstrates the importance of climate change analysis through a case study example. The ‘morphing’ methodology published by the Chartered Institution of Building Services Engineers (CIBSE) is utilised as a baseline for transforming current CIBSE Test Reference Years (TRY) and Design Summer Years (DSY) into climate change weather years. A tool is presented that allows generation of TMY2/EPW files from this ‘morphed’ data and addresses the requirements related to solar irradiation, temperature, humidity and daylighting beyond the parameters provided by CIBSE weather years. Simulations of a case study building highlight the potential impact of climate change on future summer overheating hours inside naturally ventilated buildings.
Accurate prediction of building energy performance requires precise information of the local climate. Typical weather year files like test reference year (TRY) and typical meteorological year (TMY) are commonly used in building simulation. They are also essential for numerical analysis of the sustainable and renewable energy systems. The weather year file of one city is often employed by the nearby cities for such purposes. In this paper, the developments of customized weather year formulation are reviewed. The key issues are discussed making reference to two neighboring cities, Hong Kong and Macau, using their weather data records over a century, and the typical weather year files developed. The findings support the preference of TMY over TRY. It is also suggested that the TMY selection process should include the most recent meteorological observations, and should be periodically reviewed to well reflect the long-term climate change.
The need of accurate Test Reference Years (TRYs) for simulations has been well recognised over the years. Various methods for deriving TRYs have been developed, but their final results can be significantly different. In this paper, the major methodologies reported in literature were applied to 20-year hourly measurements of weather data from Athens, covering the period 1977 to 1996. Seventeen TRYs were produced in total. The basis to select the “best” performing TRY includes meteorological criteria (inherent in the selection process used by each method) and comparisons of results from various simulations for typical energy systems (i.e. a solar water heater, a building, a large scale solar heating system with interseasonal storage and a photovoltaic system). Based on the results of each simulation exercise, a scoring system was developed and applied. The best performing TRY was found to be the one produced by a modified Festa-Ratto method.
This paper presents the comparison of methods for generating typical meteorological year (TMY) data set using a 10-year period of meteorological data from four stations in a tropical environment of Thailand. These methods are the Sadia National Laboratory method, the Danish method and the Festa and Ratto method. In investigating their performance, these methods were employed to generate TMYs for each station. For all parameters of the TMYs and the stations, statistical test indicates that there is no significant difference between the 10-year average values of these parameters and the corresponding average values from TMY generated from each method. The TMY obtained from each method was also used as input data to simulate two solar water heating systems and two photovoltaic systems with different sizes at the four stations by using the TRNSYS simulation program. Solar fractions and electrical output calculated using TMYs are in good agreement with those computed employing the 10-year period hourly meteorological data. It is concluded that the performance of the three methods has no significant difference for all stations under this investigation. Due to its simplicity, the method of Sandia National Laboratories is recommended for the generation of TMY for this tropical environment. The TMYs developed in this work can be used for solar energy and energy conservation applications at the four locations in Thailand.
The present study is concerned with the generation of a Typical Meterological Year for Nicosia, Cyprus. The above tool will be useful for the prediction and comparison of the performance and cost effectiveness of passive and active solar systems in the island. The Typical Meteorological Year was generated from available hourly meteorological data recorded during the period 1986–1992, using the Filkenstein-Schafer statistical method.
The paper discusses methods of selecting typical weather data and describes the selection of test reference years (TRYs) for Subang, Malaysia. The TRYs were selected using the Finkelstein–Schafer statistic, from 19 years of meteorological data recorded during the period 1980-98. Changing the weighting of the various meteorological variables (dry bulb temperature, solar radiation, relative humidity and wind speed) in the range appropriate for building performance studies had little effect on the selection, so it is recommended that equal weightings should be used unless there are particular reasons for using some other weighting. The replacement of relative humidity by moisture content had little effect on the selection.
The application of solar energy requires a knowledge of long-term solar radiation and daylight data. Because of the limited availability of measured data, various formulae have been derived to compute the solar irradiance using other, more commonly available, weather data. In this article two such models are presented, MRM (meteorological radiation model) and CRM (cloud-cover radiation model). MRM requires hourly data for sunshine duration, dry- and wet-bulb temperature; while CRM requires only cloud amount. Both models can generate hour-by-hour data for horizontal global, diffuse, and beam irradiance. A brief comparison of the two models is presented. Results showed that MRM has the advantage over CRM, on account of its consistency with the measured data. Both models are now available via the Internet in the form of electronic spreadsheets.
The generation of a typical meteorological year is of great importance for passive solar architectural applications. In this context, within the PASCOOL project, a software tool has been developed, utilizing the Filkenstein-Schafer statistical method for the creation of a typical meteorological year. Using this software tool, a typical meteorological year was generated for Athens, Greece. The data used were from the National Observatory of Athens and cover a period of 17 years (1977–1993).
The present paper presents the generation of a type 2 Typical Meteorological Year (TMY-2) for Nicosia, Cyprus. This tool may be useful for the prediction and comparison of the performance of passive and active solar systems and for building thermal analysis. The present TMY-2 is generated from a simple TMY created in the past from available hourly meteorological data recorded during the period 1986–1992 using the Filkenstein–Schafer statistical method. The present TMY-2 contains much more data which leads to more accurate predictions especially in building simulations. This includes in addition to solar radiation values, illuminance, and other meteorological elements such as visibility, precipitation and snowfall records.
North American and European hourly-based weather data and methods for HVAC building energy analyses and design by simulation
- Levermore
Levermore GJ, Doylend N. North American and European hourly-based weather data and methods for HVAC building energy analyses and design by simulation. ASHRAE Transactions 2002;108(2):1053–62.