Figure 1 - uploaded by Jaakko Vihola
Content may be subject to copyright.
Terminology: concept of heat loss rate and different levels of power in the buildings. 

Terminology: concept of heat loss rate and different levels of power in the buildings. 

Source publication
Article
Full-text available
This paper presents a bottom-up model for studying the heat loss rateof the building stock.The model is a step towards more complex building-stock power modeling,whose goal is topredictthe sources and the amount of demand response potentialunderdifferent conditions. The heat loss rateisthefractionof thermal power needed to compensate for the heat l...

Contexts in source publication

Context 1
... efficiency is a central issue in the fields of both politics and academic studies. The European Union has set targets to significantly reduce the annual consumption of primary energy and greenhouse gas emissions by 2020 (Directive 2009/28/EC). Legislation supporting this development has been widely enacted on national levels. Furthermore, the amount of regulation related to energy efficiency is continuously growing. A number of studies have been carried out regarding the energy consumption of building stock (Balaras et al. 2005; Balaras et al. 2007; Dascalaki et al. 2011; Howard et al. 2007; Magalhães and Leal 2014; Mata et al. 2013; Tommerup and Svendsen 2006). In the Nordic climate, such as in Finland, the peak power consumption maxima occur in winter when the use of heating energy is at its highest. Although the highest power peaks appear during winter, the peaks are not only a phenomenon of the coldest season. Power use varies as a function of time every day throughout the year. The power and energy use of buildings is influenced by different factors, such as the weather, the physical characteristics of the building, HVAC systems and their settings, building automation and the behavior of users (Zhao and Magoulès 2012). The capacities of both the power distribution network and the power production infrastructure are sized based on the estimated maxima of power usage. Thus, the investment costs of the energy distribution and production systems depend on the expected future peaks. For example, in Finland there are reserve electric power plants on standby for exceptional power demand situations. These power plants have a low annual utilization rate. The additional fuels used during the power demand peaks consist mainly of fossil sources. Thus, if the power demands of the buildings could be managed more successfully, the need for additional reserve power plants would be lower, possibly leading to both economic and environmental benefits. To better manage the power use, there have been theoretical discussions - and currently even practical applications - on so-called demand response, in which the timing of energy consumption is managed. Energy consumption is managed with the help of signals based on prevailing conditions outside of the place of energy consumption (Jota et al. 2011). In demand response, at least part of the energy consumption is timed based on an indicator of the demand situation, such as using the hourly energy price in the energy markets or the current electric power frequency as an input signal. The demand response can be an automatic or manual process. It can also be used to even out the differences in power demand in both peaks and gaps and to help adjust the energy systems. Energy users can participate in reducing the peaks of energy systems by cutting their consumption. This can be done, for example, by changing the timing of the energy use or by changing the energy source (Albadi and El- Saadany 2008). When energy efficiency is examined, the focus in the building sector has often been on the total energy consumption during the investigated period of time, such as one year. Current construction regulations guide the evaluation of energy consumption in buildings at the annual level. For example, in the calculation of the E-value, the target of the inspection in energy calculations for construction permits or energy certificates is currently stated as the annual energy consumption. However, from the perspective of the entire energy system, not only the total annual energy consumption but also each instance of consumption is important. The situation may be changing. In the Energy Performance of Buildings Directive (2012/27/EU), power management and its demand response were seen as a part of energy efficiency. This mode of thinking is progressing toward national legislation of the EU’s member states. We have developed a model for estimating the heat loss rate in building stock with the goal to predict the amount and the sources of demand response potential in different situations. By the heat loss rate we mean the thermal power needed to compensate for heat loss via the walls, roof, floor and ventilation in the buildings ( fig. 1). This method is introduced and demonstrated in this paper. Building-related power studies can be executed at different levels. For a better understanding of the concept of “heat loss rate”, three of these levels are described in the very simplified figure 1. The major scale in the building level can be considered to be purchased power, the amount of power that the building owner buys from a utility company. Purchased power includes the power required for heating and electricity used in other operations. The next level of power studies is of heating power production. These can be considered system-level studies. Heating power demand is covered by utilizing thermal loads in the buildings as well as the heating power from the heating systems. However, there are always losses due to inefficiencies in the heating and heat distribution systems. The heating power consumed is used to replace the heat losses caused by differences between the outside and indoor temperature as well as needs for warm water. As outlined in a review by Zhaos & Magoulès, methods for predicting building energy consumption include engineering methods, statistical methods, neural networks, support vector machines and grey models (Zhaos & Magoulès 2012). The approach of this study is a bottom-up engineering modeling method similar to that of Mattinen et al. (2014), where building characteristics and weather data have been utilized as input data, but user behavior is not taken into account. The reason for this is that the heat loss rate is dependent only on physical characteristics of the building envelope and ventilation. It well known that weather parameters used in these types of models are some of the most important factors when analyzing the power and energy demands of buildings (Bhandari et al. 2012; Fumo 2014). In our developed model the weather data used can be chosen freely. For the analysis, the building stock was divided into different building-type categories similar to those used by Statistics Finland (see Figure 2). Within the building types, the data are further divided into different cross-section years (in 5-year age groups) to describe the physical attributes of buildings from different eras. This provides an opportunity to exploit official building stock data. It should be noted that the statistics do not include free-time residences, separate sauna buildings belonging to residential buildings or buildings of the Armed Forces. In the model, the heat loss rate is calculated by taking account of thermal conductivities of different building types and cross-section years, the difference between outside and inside temperatures and the volume of the stock. Thermal conductivity is calculated by utilizing the thermal transmittance factors (U-values), specific areas of different structural elements (A) as well as the technical specifications of the ventilation. As an example, the calculation method for computing the thermal conductivity for windows is presented below (equation ...
Context 2
... efficiency is a central issue in the fields of both politics and academic studies. The European Union has set targets to significantly reduce the annual consumption of primary energy and greenhouse gas emissions by 2020 (Directive 2009/28/EC). Legislation supporting this development has been widely enacted on national levels. Furthermore, the amount of regulation related to energy efficiency is continuously growing. A number of studies have been carried out regarding the energy consumption of building stock (Balaras et al. 2005; Balaras et al. 2007; Dascalaki et al. 2011; Howard et al. 2007; Magalhães and Leal 2014; Mata et al. 2013; Tommerup and Svendsen 2006). In the Nordic climate, such as in Finland, the peak power consumption maxima occur in winter when the use of heating energy is at its highest. Although the highest power peaks appear during winter, the peaks are not only a phenomenon of the coldest season. Power use varies as a function of time every day throughout the year. The power and energy use of buildings is influenced by different factors, such as the weather, the physical characteristics of the building, HVAC systems and their settings, building automation and the behavior of users (Zhao and Magoulès 2012). The capacities of both the power distribution network and the power production infrastructure are sized based on the estimated maxima of power usage. Thus, the investment costs of the energy distribution and production systems depend on the expected future peaks. For example, in Finland there are reserve electric power plants on standby for exceptional power demand situations. These power plants have a low annual utilization rate. The additional fuels used during the power demand peaks consist mainly of fossil sources. Thus, if the power demands of the buildings could be managed more successfully, the need for additional reserve power plants would be lower, possibly leading to both economic and environmental benefits. To better manage the power use, there have been theoretical discussions - and currently even practical applications - on so-called demand response, in which the timing of energy consumption is managed. Energy consumption is managed with the help of signals based on prevailing conditions outside of the place of energy consumption (Jota et al. 2011). In demand response, at least part of the energy consumption is timed based on an indicator of the demand situation, such as using the hourly energy price in the energy markets or the current electric power frequency as an input signal. The demand response can be an automatic or manual process. It can also be used to even out the differences in power demand in both peaks and gaps and to help adjust the energy systems. Energy users can participate in reducing the peaks of energy systems by cutting their consumption. This can be done, for example, by changing the timing of the energy use or by changing the energy source (Albadi and El- Saadany 2008). When energy efficiency is examined, the focus in the building sector has often been on the total energy consumption during the investigated period of time, such as one year. Current construction regulations guide the evaluation of energy consumption in buildings at the annual level. For example, in the calculation of the E-value, the target of the inspection in energy calculations for construction permits or energy certificates is currently stated as the annual energy consumption. However, from the perspective of the entire energy system, not only the total annual energy consumption but also each instance of consumption is important. The situation may be changing. In the Energy Performance of Buildings Directive (2012/27/EU), power management and its demand response were seen as a part of energy efficiency. This mode of thinking is progressing toward national legislation of the EU’s member states. We have developed a model for estimating the heat loss rate in building stock with the goal to predict the amount and the sources of demand response potential in different situations. By the heat loss rate we mean the thermal power needed to compensate for heat loss via the walls, roof, floor and ventilation in the buildings ( fig. 1). This method is introduced and demonstrated in this paper. Building-related power studies can be executed at different levels. For a better understanding of the concept of “heat loss rate”, three of these levels are described in the very simplified figure 1. The major scale in the building level can be considered to be purchased power, the amount of power that the building owner buys from a utility company. Purchased power includes the power required for heating and electricity used in other operations. The next level of power studies is of heating power production. These can be considered system-level studies. Heating power demand is covered by utilizing thermal loads in the buildings as well as the heating power from the heating systems. However, there are always losses due to inefficiencies in the heating and heat distribution systems. The heating power consumed is used to replace the heat losses caused by differences between the outside and indoor temperature as well as needs for warm water. As outlined in a review by Zhaos & Magoulès, methods for predicting building energy consumption include engineering methods, statistical methods, neural networks, support vector machines and grey models (Zhaos & Magoulès 2012). The approach of this study is a bottom-up engineering modeling method similar to that of Mattinen et al. (2014), where building characteristics and weather data have been utilized as input data, but user behavior is not taken into account. The reason for this is that the heat loss rate is dependent only on physical characteristics of the building envelope and ventilation. It well known that weather parameters used in these types of models are some of the most important factors when analyzing the power and energy demands of buildings (Bhandari et al. 2012; Fumo 2014). In our developed model the weather data used can be chosen freely. For the analysis, the building stock was divided into different building-type categories similar to those used by Statistics Finland (see Figure 2). Within the building types, the data are further divided into different cross-section years (in 5-year age groups) to describe the physical attributes of buildings from different eras. This provides an opportunity to exploit official building stock data. It should be noted that the statistics do not include free-time residences, separate sauna buildings belonging to residential buildings or buildings of the Armed Forces. In the model, the heat loss rate is calculated by taking account of thermal conductivities of different building types and cross-section years, the difference between outside and inside temperatures and the volume of the stock. Thermal conductivity is calculated by utilizing the thermal transmittance factors (U-values), specific areas of different structural elements (A) as well as the technical specifications of the ventilation. As an example, the calculation method for computing the thermal conductivity for windows is presented below (equation ...
Context 3
... implementing the formula above for each part of the building envelope (except the base floor), the results are summed to find the total thermal conductivity ( C ) of the building envelope and ventilation (equation 2). (2) These calculation steps are implemented for all building types and different cross-section years. The heat loss rate can be calculated at any time (t) by utilizing the thermal conductivity (C all ), the volume of the stock (V stock ) and the difference between outside and inside temperature ( ǻ T) (equation 3). (3) The heat loss rate through the base floor (P heat losses, floor ) must be calculated separately because the temperature difference between the ground under the building and indoors is not equal to that of the other parts of the building envelope. The temperature is much more stable under the building. This discrepancy was taken into account by calculating the heat transfer through the base floor following the method described in the standard SFS-EN ISO 13370 “Thermal performance of the buildings. Heat transfer via the ground. Calculation methods” (2008). The research object of this study is the Finnish residential and service building stock (figure 1). The Finnish residential and service building stock is one of the youngest in Europe. Approximately 70 % of the stock was built between 1970 and 2010. The average location of buildings in Finland is the northernmost of European Union countries. 63 % of the stock (m 3 ) consists of residential buildings of which single family houses form the largest component with a share of 34 ...
Context 4
... have developed a model for estimating the heat loss rate in building stock with the goal to predict the amount and the sources of demand response potential in different situations. By the heat loss rate we mean the thermal power needed to compensate for heat loss via the walls, roof, floor and ventilation in the buildings ( fig. 1). This method is introduced and demonstrated in this paper. Building-related power studies can be executed at different levels. For a better understanding of the concept of "heat loss rate", three of these levels are described in the very simplified figure 1. The major scale in the building level can be considered to be purchased ...

Citations

... The energy use of buildings is influenced by different factors, such as (i) weather, (ii) the physical characteristics of the building, (iii) heating, ventilation, and air conditioning (HVAC) systems and their settings, and (iv) the behavior of users [4] . However, heat loss is to be attributed to wastewater, air leakages, inefficiency from the heating/cooling systems, and building envelope especially [5] . In particular, the main sources of heat loss in a residential home are walls (poor insulation accounts for 35-45% of heat dissipation), roof (20%), windows (15%), ventilation (15%), floor (10%) and doors (5%) [6] . ...
... For the technical criterion, thermal performance of the building was chosen as heat loss rate of the building. The heat loss rate was chosen as a parameter to assess the fraction of the thermal power needed to compensate for the heat loss via building elements (Vihola et al., 2015). Moreover, building area and a number of guests were chosen as parameters as these have great influence on the energy consumption in the building and provide a good indication of building physical size. ...
Article
This study provides the first comprehensive overview of the sustainability performance of the hotel sector in the Himalayan region: Sagarmatha National Park and Buffer Zone, using both environmental, economic, and technical criteria. In particular, the performance of 45 buildings in this region were measured and quantified in terms of life cycle based carbon footprint, life cycle costs, heat loss rate, number of guests, energy consumption, and area. Buildings were classified into three types: traditional, semi-modern and modern. The statistical analysis included testing for significant differences between such categories by means of ANOVA, and determination of the correlation between the same parameters. Results show a significant difference between the buildings’ total carbon footprint and operation stage carbon footprint while, there is no significant difference between the buildings’ life cycle costs. Traditional buildings have on average the largest carbon footprint and life-cycle cost over the typical building lifespan of 50 years of building lifespan. The ANOVA tests highlight how heat loss rate, size of the building and number of tourists in the hotels are significantly different across the building types. A strong positive correlation is observed between environmental impact, economic impact and energy consumption for the household activities, and a negative correlation with the number of guests and building size. By considering several buildings, this study allows to draw new and more general conclusions about effective sustainability strategies in the whole hotel sector in the Himalayan region. In particular, it shows that reducing impacts in the operation stage should be highly prioritized, focusing on reducing energy consumption and heat loss and shifting to the use of renewable energy sources.
... this was illustrated at the hourly level and was based on a bottom-up engineering modeling method applied on a Finnish residential and service building stock (Vihola et al., 2015). The authors found that the physical characteristics of the building envelope, ventilation, and weather conditions play a critical role in determining the HEL rate in buildings, while disregarding the height and the number of exposed openings of heated space. ...
Article
Heat energy loss in buildings occurs by two means, namely Fabric Heat Loss and Ventilation Heat Loss. Loss occurs when energy flows out through building envelope from inner warmer air to cooler air located external to the building. The purpose of this study is to identify and estimate the proportion of Heat Energy Loss that is directly caused by construction components from the building envelope. The novelty of this research is to propose a methodological framework that characterizes the Heat Energy Loss in buildings during the operation phase, taking into consideration the local climate data in which buildings are located. Reliance is on the use of a systematic approach that makes the work readily available to practitioners and experts in the area of energy efficiency. A case example of a single-family house is examined in three different climate classifications for validating the proposed method of this work. Results reveal that Fabric Heat Loss is the main factor of the Heat Energy Loss in buildings; responsible for more than 81% of the total Heat Energy Loss in buildings. Openings and exterior walls play a significant role in curbing such energy loss; accounting for around 70% of the total Fabric Heat Loss in buildings. This work points out that the percentage of energy efficiency improvement of Fabric Heat Loss is similar and directly proportional to the percentage of reduction in U-values of building components; as U-value reduces by 6.66%, the energy efficiency of Fabric Heat Loss improves by 6.66%. Besides, the analysis conducted indicates that lower air change rate would lessen the Ventilation Heat Loss in buildings. Finally, this work illustrates that Heat Energy Loss in tropical climates and dry climates could reach a value of 16% and 8%, respectively, compared to Heat Energy Loss in moist subtropical mid-latitude climates.
... The power and energy use of buildings is influenced by different factors, such as weather, the physical characteristics of the building, HVAC systems and their settings, building automation, and the behavior of users 4 . However, heat loss is to be attributed to wastewater, air leakage, inefficiency from the heating/cooling systems, and especially building envelope 5 . In particular, the four sources of heat loss in a home are walls (poor wall insulation accounts for 35-45% of heat loss), roof (20%), windows (15%), ventilation (15%), floor (10%) and doors (5%) 6 . ...
Article
Full-text available
Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.