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Boxplots of the outdoor dry-bulb temperature (a) and the global horizontal solar irradiance during daily hours (b) for IWEC (Present), Weathershift (WS), Meteonorm (MET), CCWorldWeatehr-Gen (CCW), and F-TMY. All future weather files are for 2050s considering Representative Concentration Pathway (RCP) 8.5.
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The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools...
Citations
... A robust climatic data set is necessary to define the external boundary conditions that the building will face throughout its lifecycle. In general, a representative year of hourly weather data is used to capture the typical regional climate conditions [12]. ...
... Consequently, we might simulate buildings for climates that already exhibit different behaviors from the historical climatic data [16]. Understanding the behavior of such buildings in the future becomes crucial to mitigate issues like overheating and power outages [12]. ...
... With the availability of various methods for generating future weather files, some researchers have started delving into the differences in simulation outcomes among these methods. Tootkaboni et al. [12] compared the differences in building simulation results when different statistical methods are used to generate weather files with an RCM model considering the worst-case scenario (RCP8.5). The investigation reveals that various statistically downscaled future weather datasets created by weather generators yield similar predictions for the future energy performance and comfort analysis of the buildings. ...
Understanding the trends and uncertainties in Building Energy Simulation (BES) performance indicators under future climate conditions is crucial for mitigating issues such as overheating and power outages. To address this, we generated a set of weather files for all 27 state capitals in Brazil, considering six climate model projections (three General Circulation Models as driving models and two nested Regional Climate Models) and two distinct emission scenarios from the CORDEX project. We analyzed the variability in climatic variables and subsequently performed BES on a representative Brazilian social housing unit to evaluate its impact on the performance indicators outcomes. Consistent with previous studies, a substantial increase in cooling-related demands was observed in the more pessimistic scenario (RCP8.5) and mild increases in the more optimistic scenario (RCP2.6), with a trend toward stabilization after 2050. Regarding uncertainties, we found higher Relative Standard Deviation (RSD) values for the cooling degree hours indicator. The capitals in the Central-West, Southeast, and South regions exhibited greater uncertainty regarding temperature indicators, whereas the irradiation parameters displayed higher uncertainties in the Northeast region. For the BES outcomes, RSD values as high as 19.9% were found for cooling load values. It was also demonstrated that locations, periods, and scenarios exhibit different extreme climate model projections. Ideally, employing an ensemble of weather files developed from other models would help assess associated uncertainties in the building performance indicators.
... The Climate Change World Weather Generator (CCWorldWea-therGen 1.9) open-access tool was used to establish the weather information for the present and future scenarios. This software, previously used by numerous studies [95,96], considers the HadCM3 [82] global climate model based on the RCP8.5 scenario [97]. It was used to morph the original file into the weather of 2020 and 2080, following the climate change predictions presented by the IPCC [83]. ...
We are currently witnessing an unprecedentedly fast change in environmental parameters for which most cities worldwide are not well prepared. One of the most characteristic urban fabrics of many European cities is the residential neighbourhood typology built in the second half of the 20th century and influenced by the Modern Movement. This study aims to develop an assessment-and-upgrading methodology of the urban microclimatic resilience of these residential neighbourhoods, using a case study for the application considering past, present, and future climatic contexts. ENVI-met software is used to examine the original layout of the case study, its evolution up to the present time and the proposal for its adaptation to the worst climatic forecasts for the end of the century, suggesting solutions to enhance thermal comfort. The results show a significant reduction in extreme warm conditions of up to 6.3 °C in UTCI in the worst-case scenario at the century's end, proving the importance of passive strategies in achieving improved urban resilience in warm cities. The methodology applied in this study can serve as a starting point for a broader analysis to be applied to different urban typologies other than the residential one and in different climatic contexts.
... Presently, researchers have three trusted weather morphing tools at their disposal: WeatherShift, Weather Morph, and CCWorldWeatherGen particularly those engaged in the study of building performance [51][52][53]. WeatherShift modifies key meteorological variables such as dry bulb temperature, dew point temperature, relative humidity, atmospheric pressure, global horizontal radiation, direct normal radiation, diffuse horizontal radiation, and wind speed. Moreover, the modified weather variables exhibit similarity to data obtained from dynamic downscaling models [51,52]. ...
... WeatherShift modifies key meteorological variables such as dry bulb temperature, dew point temperature, relative humidity, atmospheric pressure, global horizontal radiation, direct normal radiation, diffuse horizontal radiation, and wind speed. Moreover, the modified weather variables exhibit similarity to data obtained from dynamic downscaling models [51,52]. Therefore, WeatherShift™ is used to generate projections for the years 2035 ...
With climate change, the Mediterranean region is expected to be exposed to extreme conditions in the coming years and the mid-term. This study aims to assess and mitigate adaptive overheating risk in archetype residential buildings in three climate zones (2A, 3A, and 2B) in Palestine, and under historical and future climates (2035, 2065, and 2090) using ASHRAE 55 standards. The outcomes revealed that the present design of the building, characteristic of low-energy-efficient structures common in Palestine, exposes occupants to a risk of overheating, particularly in climate zone 2B. Enhancing the energy efficiency of building envelope parameters, coupled with additional mitigation measures such as exterior shading and cool roofs, proves effective in attaining the thermal comfort threshold in the warmest rooms within climate zones 2A and 3A, even in the face of future climate changes. However, for climate zone 2B, achieving the thermal comfort threshold might need the incorporation of mechanical cooling in addition to passive mitigation strategies.
... However, it is equally true that, being based on physics-based meteorological models, they often require higher computational costs and a more accurate representation of topographical conditions within the specific scope (P. Tootkaboni, Ballarini, Zinzi and Corrado, 2021). ...
Urban microclimate prediction is crucial for various fields, including Building Performance Simulation (BPS), outdoor thermal comfort, building life cycle, and residential health. Existing methods involve using classical weather file data, such as Typical Meteorological Years (TMY), or machine learning techniques for time-based forecasting. However, the incorporation of both spatial and temporal dimensions and land use/land cover (LULC) data is seldom considered. This paper proposes a novel approach to predict microclimate: the Geo-LSTM-Kriging model, which is applicable for fine-scale microclimate prediction within a few hundred meters around weather stations. The Geo-layer processes and learns from LULC data, the LSTM layer learns from historical data, and the Kriging layer extracts spatial distance information. This comprehensive combination integrates spatial, temporal, and environmental conditions, providing accurate results with higher spatial resolution (1 m × 1 m) and shorter time intervals (10 min). These prediction results were achieved by employing statistical downscaling calculation and utilizing data from 14 weather stations located within our university campus. Upon the analysis of these prediction results, we found that the proposed model can accurately predict temperature and humidity at high spatial and temporal resolution. Compared to traditional interpolation models, the RMSE of temperature decreases from 1.59°C to 0.64°C, and the RMSE of relative humidity (RH) decreases from 7.70 to 3.23. A thorough analysis of the model prediction results reveals the varied impacts of different LULC features on microclimate predictions, highlighting the value of the proposed model and the importance of incorporating LULC data.
... Using high-frequency and high-resolution RCMs' projections is the most suitable approach for climate change impact studies (P. Tootkaboni et al. 2021;Mauree et al. 2019), so Future Typical Meteorological Year (F-TMY) have been created based on the RCMs' projections (e.g., Nik 2016;Bravo Dias et al. 2020a;P.Tootkaboni et al. 2021). Nevertheless, Bracht et al. (2023) highlight the need for updated approaches involving different RCMs and emission scenarios to define robust building energy policies. ...
We propose a comprehensive methodological approach to address uncertainties in building energy simulation (BES) studies within a climate change context. Drawing upon expertise from the climate community, our approach aims to improve the reliability of climate-dependent BES for sustainable building design studies. The methodology focuses on creating weather files that accurately retain the climate variability from CORDEX high-frequency climate data, and performing multiple BES (conducted with climatologies from various climate models and emissions scenarios) while removing the climate models biases. The robustness of the results is assessed through statistical analysis, and an uncertainty range is attributed to future energy demand estimations. This approach is illustrated using a representative prototype of a social house located in central-eastern Argentina. The evaluation specifically focuses on assessing the influence of climate change projections on cooling and heating energy demand. We systematically assessed uncertainties related to climate scenarios, seasonality, and building design sensitivity. Our exercise highlight that uncertainty levels rise with higher emissions scenarios. Within our case study, the cooling (heating) energy demand exhibits substantial variations, ranging from 27-37 (303-330) MJ/m² in a moderate emissions context to 51-70 (266-326) MJ/m² in a high emissions scenario. Notably, improvements in building efficiency correlate with reduced uncertainty and, in the context of higher emissions, the projected energy demand can range between 24-37 (201-243) MJ/m². Finally, a discussion is provided on the added value of the proposed methodology compared to solely utilizing a single climate projection file in BES, when uncertainties within climate projections remain unassessed.
... The University of Southampton's Energy and Climate Change group developed the Climate Change World Weather File Generator (CCWorldWeatherGen) tool based on this technique. This tool transforms original EnergyPlus weather files into 'climate change' weather files and has been widely used by researchers [31][32][33][34]. More details about this tool can be found in [35,36]. ...
Rising temperatures, increase in population, and dense urban morphology have resulted in increased cooling energy demands. The conventional degree-days method to calculate cooling energy demand considers only the sensible heat load of air and neglects the latent component. This study aims to estimate the cooling degree days based on the heat index (by considering both the sensible and latent loads) for the current and future years (2050 and 2080). Further, the ventilation load index for each of these cities has been established to unlock the impact of ventilation on the building’s total energy consumption for current and future years. The results show that heat index-based degree days have a stronger relationship with the buildings’ cooling energy consumption and, therefore, can predict the cooling energy demand of buildings with 20% higher accuracy than conventional temperature-based degree days. Analysis shows that cooling degree-days and frequency of temperature above the comfort range continue to increase in Pakistan, highlighting increased degree-days in the range from 11.0 to 41.6% by 2050 and from 28.4 to 126.5% by 2080. Prompt actions are essential to enhance the resilience of Pakistan’s national grid to meet these future cooling energy demands.
... It employs regression techniques and statistical models to downscale the available climatic data to the desired location, considering factors such as latitude, altitude, and geographic features to enhance data accuracy. The tool is based on global climate models (GCMs), as specified in the IPCC assessment report [57]. Meteonorm's final data represent a comprehensive compilation of measured, calculated, and interpolated data [58][59][60] to calculate typical years at selected locations with a hourly resolution for future climate scenarios, offering a valuable resource for climate-related research and analysis. ...
This paper delves into the potential impact of a changing climate on the energy performance of European buildings. Research aims to provide a comprehensive evaluation of current energy requirements focusing on the envelope , considering existing regulations in national policies. Energy simulations are conducted at 94 locations across the European Union to cover the climatic variability and Koppen climate classification. The research analyzes future climate scenarios for the years 2030, 2050, and 2070, using three different Representative Concentration Pathways (RCP 2.6, 4.5, 8.5). According to a comprehensive analysis of heating, cooling, and overall energy performance, climate plays a significant role in buildings' energy balance. In moderately cool climate countries, the demand for air conditioning is projected to decrease in the years ahead. Conversely, in countries with a warm climate, there is a projected increase in the overall energy demand. Consequently, a revision of current energy regulations should be a priority. Providing insights into the relation between building design, energy efficiency, and climate change, the research identifies policy adjustments to ensure buildings can effectively respond to changing climatic conditions. A holistic and dynamic approach can support building design accounting for long-term impacts of climate change to create resilient and energy-efficient structures.
... Furthermore, this study incorporates future weather files to assess the impact of weather conditions on retrofit measures under various climate scenarios, aiming to achieve the energy policy targets set by policymakers, such as those for 2030 or 2050. The sources of these future weather files can vary, including resources like Meteonorm, WeatherShift, and CCWorldWeatherGen [46]. ...
... The results indicate a notable increase in air conditioning energy usage for office buildings and residential flats. P. Tootkaboni et al. [68] conducted a comparative analysis of different tools for generating future weather datasets, revealing that morphed weather files demonstrate similar performance in predicting future comfort and energy performance of buildings, albeit with discrepancies compared to dynamically downscaled weather files. These papers advocate for including future weather considerations in predicting building energy consumption. ...
Incorporating future weather predictions into building assessments is essential for enhancing resilience, energy efficiency, cost savings, comfort, and sustainable infrastructure development in response to climate change. This study investigates the interplay between climate change and building performance, primarily focusing on energy usage, cost implications, and occupant comfort. It examines how future weather conditions impact school buildings in different climates, analyzing energy, cost, and comfort aspects. The research underscores the significance of tailored climate adaptation strategies for various regions and emphasizes considering future performance, even for highly energy-efficient buildings. Employing a comprehensive simulation-based approach, the study implements and validates future weather data in a Turkish school building, incorporating envelope improvements and photovoltaic applications to boost energy efficiency. A distinctive feature is the rigorous validation of future weather predictions against current measured data, facilitating a regional-level assessment of climate change effects on building energy consumption. The study's novelty lies in its detailed evaluation of climate change's multifaceted impacts on buildings, innovative future climate data validation, and contribution to a more localized and climate-specific approach to addressing building energy-cost-comfort performance. Findings reveal that in hot climates, there is a potential for nearly doubling primary energy consumption, global costs, and CO2 emissions in the future for both cost-optimal and nearly zero-energy scenarios. Consequently, the savings would decrease from 53-63 % to 13–30 %. In contrast, in cold climates, the impact on these parameters differs slightly, with reduced primary energy consumption and CO2 emissions but higher global costs. Notably, a building retrofitted to a high energy efficiency level may experience a substantial increase in future energy consumption and global costs, approaching the levels of currently inefficient buildings.
... These results are in line with previous works, such as the one developed by Too kaboni et al. [18] in Rome. These authors found no significant differences between the BP results with the climate files for the 2050 scenario from CCWWG and Meteonorm (consid ering RCP8.5) when assessing annual discomfort hours and both heating and cooling en ergy demand. ...
... This study carries out a comparative analysis of three different methods for genera ing future weather data for energy simulation in southern Spain, evaluating their influ ence on the thermal and energy behavior of two different scale models: a test cell and These results are in line with previous works, such as the one developed by Tootkaboni et al. [18] in Rome. These authors found no significant differences between the BPS results with the climate files for the 2050 scenario from CCWWG and Meteonorm (considering RCP8.5) when assessing annual discomfort hours and both heating and cooling energy demand. ...
Climate change will have a great impact on the hottest climates of southern Europe and the existing residential stock will be extremely vulnerable to these future climatic conditions. Therefore, there is an urgent need to update this building stock considering imminent global warming by applying climatic files that predict future conditions in building performance simulations. This research makes use of the two most applied tools (Meteonorm and CCWorldWeatherGen) for generating future climate hourly datasets for 2050 and 2080 in southern Spain. The results predicted for outdoor and indoor thermal conditions and cooling and heating demands are evaluated for two different scale simulation models: a test cell and a multi-family building located in southern Spain. The main aim of this work is the development of a comparative analysis of the results to highlight their potential differences and raise awareness of the influence of the climate data projection method on the simulation outcome. The results show that the projection method selected for producing future climatic files has relevant effects on the analysis of thermal comfort and energy demand, but it is considerably reduced when an annual evaluation is developed.
