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This paper analyzes electricity use in buildings and explores key research questions related to assessing, rating, labeling, and benchmarking building energy use. The analysis reviews annual Energy Use Intensity (EUI) data from 3060 fully electric-powered buildings across six categories: residential, educational, offices, hospitals, hotels and restaurants, and wholesale and retail trade services. One-fifth of the analyzed buildings is derived from an energy dataset that offers a higher time-scale resolution, providing more detailed insights into energy usage patterns. The analysis employs various factors, including the seasonal variation factor (SVF), daily variation factor (DVF), monthly factor (M f), and hourly factor (H f), to gain a detailed understanding of actual energy usage at different time scales. The findings indicate that electricity use is less sensitive to seasonal variations than other energy carriers that provide heating and cooling. The paper provides hourly and monthly peak load percentages (PPL), which can indirectly assist in assessing hourly schedules for building energy performance simulations. The monthly energy use pattern varies between building categories, with high-energy months alternating between winter and summer. For non-residential buildings, high-energy hours correspond directly to the daytime, while for residential buildings, there is a shift towards the evening, and the daily amplitude is lower. Furthermore, the analysis of daily and monthly variability of heating energy carriers, even if non-electric, sheds light on the implications of electrification for these loads. • The study evaluates annual electricity data from 3060 fully electric-powered buildings. • Median EUIs are meaningful energy performance benchmarks by building category. • Residential and educational buildings exhibit EUIs of 90-120 kW h/m2. • Offices and hospitals demonstrate EUIs of 160-170 kW h/m2. • Monthly and hourly factors establish energy benchmarks for finer time resolution. [ABSTRACT FROM AUTHOR]

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building policies must be rooted in a good understanding of the targeted population. • Building performance standards may benefit from creating databases of weather files. • A thermal performance analysis should consider multiple key performance indicators. • Building performance policies should address the thermal resilience of buildings. This paper presents the main challenges faced and lessons learned when developing a new method to assess the thermal performance of residential buildings for the recently updated Brazilian building performance standard, NBR 15575–1:2021. It touches on subjects related to the representation of occupant behavior, climate, and thermal acceptability in building performance simulation, as well as the use of reference models, key performance indicators, and performance levels in policies. NBR 15575 provides a thorough procedure to analyze thermal performance, allowing buildings to be assessed while in passive operation mode and also accounting for energy needs when active operation is necessary. Multiple key performance indicators are introduced to provide a comprehensive evaluation. The main difficulty found was dealing with diversity. Given the vast scale of Brazil's territory, a high variation in culture, climate and construction techniques is expected, thus requiring a compromise between detail and scalability. There are still many opportunities for improvement, especially regarding the representation of occupants, and adjustments of the characteristics of reference models to promote high thermal performance, considering the diverse climates, regional practices, and economic needs. This paper might help researchers and other stakeholders to develop and improve other local standards and protocols, especially for warm climates. [ABSTRACT FROM AUTHOR]

[Display omitted] • Unique preconditions, specific characteristics for use can be boundary conditions. • Energy use, users' behavior, lighting are correlated conditions within buildings. • Boundary conditions can help explain the occupants' behaviour and the use of spaces. • Knowledge of boundary conditions helps to understand how to design buildings. • Boundary conditions can be building/ user/ input-values and method-specific. This paper aims to review the boundary conditions (B/C) in specific categories (energy, building use, and lighting) within non-residential buildings to pave the way to a better understanding of users' requirements and needs of the built environment. For this paper, B/C are understood as unique preconditions, specific characteristics for use, determining specific features of buildings, enabling an accurate understanding of non-residential spaces concerning energy use, user behaviour, and lighting. This paper describes the results of an overall quantitative (1st method) review and a systematic review (2nd method) of boundary conditions and their factors within different types of non-residential buildings from the users' perception. Followed by a qualitative experts' literature review (3rd method) on B/C within offices, schools, and hospitals chosen by a team of international experts working together on Subtask A: User perspective and requirements, Task 61 IEA (International Energy Agency): Solutions for daylighting and electric lighting. The first review method led to the selection of 21 papers. The second method resulted in the selection of 7 papers out of 93,143 found in Scopus; during the 3rd review, experts collectively chose 74 additional papers focussing on the users' factors contributing to specific B/C. The scope of this paper is limited only to offices, schools, and hospitals. Based on the findings, the authors recognise a broad definition of boundary conditions from specific values, and conditions to interconnected factors, user profiles, functions of the building types, and operating hours. This paper is an overview of B/C factors found in the literature that can help explain the occupants' behaviour and the use of spaces. B/C are often type of building/user/location/situation/simulation input-values and method-specific. Therefore, they cannot be widely applicable but offer patterns and help to understand the correlations between various factors shaping the built environment. A better comprehension of the reasons for identifying B/C and their factors can help in developing a deeper knowledge of how we use buildings to find optimal ways to design them. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected people's behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems. [ABSTRACT FROM AUTHOR]

• Data about DHW in residential buildings were collected by survey in Southern Italy. • Contextual and personal factors influencing DHW usage were considered. • Descriptive statistics were performed to characterize DHW by climatic zone. • Inferential statistics were conducted to discover DHW usage patterns. • Four clusters of dwellings were identified considering DHW daily usage hours. In European efficient buildings, the share of energy use for DHW is a significant percentage of the heat requirement. The evaluation of DHW energy demand and consumption patterns has been neglected in the past and studies are located in Northern countries. This paper presents a survey conducted in Southern Italy. Descriptive statistics were performed to characterize DHW production by climatic zone. Inferential statistics were conducted to discover significant contextual and personal variables and identify usage groups. The most used energy source was biomass (49.3%), followed by methane gas (37.7%). Fireplaces were installed in the coldest zone, in detached houses, more in small municipalities (36%), and low-income families (32%). Solar systems were in 8% of dwellings having income higher than 28000€. Four clusters of dwellings were identified considering the daily usage hours. The eldest and less numerous households fell into the cluster with the lowest usage (3.5 h). Households with a percentage <25% of unemployed components had more probability to belong to clusters with more DHW usages. The paper contributes to fill the investigation in the Mediterranean area and provides a systematic approach to expand the limited knowledge about the influencing factors on the DHW production and usage. [ABSTRACT FROM AUTHOR]

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

• We review sixteen models for Variable Refrigerant Flow System (VRF). • We develop three data-driven models for VRF. • Physics-based and data-driven models are compared on a real-world dataset. • The impacts of different input features on VRF efficiency are analyzed. Variable Refrigerant Flow (VRF) systems are gradually gaining popularity in small and medium-sized commercial and residential buildings owing to their high part-load performance, flexible control, and ease of installation and maintenance. Developing models of VRF systems to predict their performance are important for model-based control, fault diagnostic and detection. There are VRF models published in existing literatures, however those models were developed and validated in different datasets. As a result, the model accuracy cannot be directly compared. To fill this gap, this paper presents a comprehensive review of the existing VRF models, and summarizes the input/output parameters and mathematical formulas of 16 VRF models from literature (referred to as physics-based model). Next, we validate and compare the model accuracy of existing models using the same dataset. Additionally, we develop data-driven models using the state-of-art machine learning algorithms, and compare the model accuracy between existing physics-based models with data-driven models. We find the model proposed by Hu et al. in 2019, which regresses the VRF cooling capacity and COP as a linear combination of indoor and outdoor temperatures times a cubed polynomial function of compressor frequency, is the most accurate physics-based model, with a prediction error of 22.19% in the training dataset and 22.44% in the validation dataset. XGBoost is the most accurate data-driven model, with a prediction error of 19.29% in the training dataset and 22.02% in the validation dataset. The data-driven model is more accurate while the physics-based model is more generalizable. The findings of this study can help researchers to select the proper VRF model for building energy prediction, model-based optimization, and fault diagnostic and detection. [ABSTRACT FROM AUTHOR]

• Analysis of 15-minute sampled residential electricity consumption data of Dubai. • Households are classified using cooling consumption included in electricity bills. • K-Means clustering is used to group households based on their hourly consumption. • Consumption patterns helped to identify occupant behaviours in summer and winter. • Consumption patterns were cross-referenced to building type and occupant profiles. Analyzing residential load profiles and usage patterns is critical to making better decisions for demand-side management initiatives and designing strategies to get more people interested in energy savings. Therefore, analyzing load profiles, it is crucial to know how hourly consumption varies during summers, winters, weekdays, and weekends. In addition, determining the influence of occupancy, dwelling size, and building topologies on consumption is equally important to build predictive models. Therefore, this paper presents detailed research on the electricity consumption and profiles of the residential sector in Dubai based on the dwellings' characteristics and 15-minute resolution smart meter data. The data utilized includes the cooling systems, the number of occupants, and the physical characteristics of a dwelling, such as its typology, sizes, and number of bedrooms. First, using the K-Means clustering method, the authors grouped the households based on their consumption profiles. Second, the consumption patterns of each group of households were identified and organized based on similar consumption profiles over 24 h. Third, the authors applied several classification algorithms to assess the potential of using dwellings and occupants' characteristics to predict the patterns with which each household is associated. The analysis of consumption patterns showed that 43% of households with cooling included in their bills had the global peak demand at midnight during weekdays and weekends in summer. However, the global peak demand for cooling-excluded households occurs from 7:00 to 10:00 pm on weekdays and, in some specific cases, on the weekends, as early as 10:00 am. Finally, the methods used for classification were able to identify key characteristics driving the patterns of electricity demand and were well suited to this predictive modeling context. [ABSTRACT FROM AUTHOR]

• Analyzed residents' thermal demand characteristics under the global climate change. • A new thermal comfort evaluation model of traditional dwellings was established. • The new thermal neutral temperature and comfort range of residents were proposed. • Different measures to improve the old and new types of dwellings were put forward. Global climate change has led to increased differences in the indoor thermal environment of traditional dwellings and residents' thermal adaptability, but quantitative research in this area needs to be updated and improved. In this paper, taking the old and new types of traditional dwellings of Guangfu Ancient City in Hebei province, China as the example, we first investigated the current situation of indoor thermal environment and thermal comfort characteristics of residents by means of subjective questionnaire survey and objective data measurement. The measured data showed that the average indoor temperature of old-type traditional dwellings in winter was far lower than the thermal comfort range of human body, and it was also higher than the relevant standard in summer. Secondly, we established a new thermal comfort evaluation model of traditional dwellings and proposed a new thermal neutral temperature of residents: old-type traditional dwellings (18.1 °C in winter and 27.1 °C in summer), new-type traditional dwellings (19.8 °C in winter and 25.7 °C in summer). Although the measured data showed that the thermal environment quality of the old-type traditional dwellings was poor, the residents had strong thermal adaptability. Finally, we put forward the different living performance improvement strategies of old and new types of traditional dwellings: The old-type traditional dwellings should focus on strengthening the thermal performance of the envelope structure, while the new-type traditional dwellings should solve the problem of low indoor humidity in winter. This research improves the evaluation system of human thermal comfort of dwellings in cold region of China, and provides a good case reference. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of two benchmark models for post-world war II residential buildings. • Average energy use intensity per household (archetypes A and B) were 166 and 155 kWh/m2/year. • Models validated with four-year monitoring data on energy consumption. • Households are dominated by senior adults and their occupancy profiles are presented. • Findings on energy needs and use intensity are useful in temperate and continental climates. In the context of the European carbon neutrality targets, building benchmarks are a key issue for the renovation of existing buildings. Although there are various benchmark methods for energy efficiency characterization, their application to the residential sector is still limited. This paper developed two building simulation models for post-world war II houses in Belgium based on data from post-occupancy measurements and field survey campaigns. The study reports the energy characteristics and occupancy profiled of detached single-family houses. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted between 2016 and 2019. The benchmark model's validity has been further checked against public statistics and verified through model calibration and monthly energy bill comparison. Two reference models representing 633.702 post-WWII single-family houses in Belgium were created and validated. The first archetype has an average energy use intensity of 166 kWh/m2/year and represents detached single-family houses built between 1945 and 1969. The second archetype has an average energy use intensity of 155 kWh/m2/year and represents detached single-family houses built between 1970 and 1990. The paper provides a timely opportunity to evaluate the real performance of post-world war II most common archetypes concerning design assumptions and how building professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and intensity are useful for creating future renovation scenarios for similar archetypes in Western European countries. [ABSTRACT FROM AUTHOR]

This paper evaluates a parametric analysis towards compliance with the nZEB standard, which in Spain is defined by the latest update of the Technical Building Code. This regulation is critically assessed regarding its ability to promote the concept of cost optimization, promote renewable energy sources and minimize primary energy consumption in the residential sector. To this end, a virtual building was defined and multiple designs were evaluated using DesignBuilder software. A set of 170 alternative scenarios was established and parametrically evaluated for five cities representing the five climatic zones of inland Spain (Bilbao, Burgos, Seville, Madrid and Almeria). The results were evaluated focusing on cost-cost effectiveness and primary energy consumption values for the different scenarios, evaluating them in relation to the minimum requirements set by the regulation. The great potential of photovoltaic energy is highlighted, which allows negative values to be obtained for the two Equivalent Uniform Annual Costs due to the cost avoided through self-consumption. This fact makes the optimal designs tend to electrification, specifically through air and ground source heat pumps. It is worth mentioning that the new Code has only established a minimum target for renewable self-consumption in the residential sector, which should be reinforced in future updates. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

• Self-consumption in residential sector is studied under Spanish regulatory framework. • An average-day model versus a whole-year model is benchmarked. • Scheduling household appliances is analysed in a residential building. • Ex-ante and ex-post renewable energy sharing is investigated. Studies on self-consumption in residential use are constantly evolving, and society claims new solutions in response to changes to new paradigms. This paper aims to optimise the sizing of a self-consumption installation for collective self-consumption in a residential building under the Spanish regulatory framework. The study considers the current price volatility in the retail electricity Spanish market and focuses on the techno-economic analysis of self-consumption in a building consisting of 12 dwellings using photovoltaic energy and the possible support of a storage system. The three aspects analysed are the sizing of the self-consumption facility, the use of smart appliances to shift their consumption to more cost-effective times and the sharing of the renewable energy generated by the facility among the consumers participating in the collective self-consumption. This study employs two models, an average-day model and a whole-year model. The techno-economic analysis yields that between the two self-consumption modalities for residential consumption, the modality with surplus simplified compensation is more profitable than the one without surpluses. Finally, the study focuses on sharing the total renewable energy generated among the participants. The results indicate that both ex-ante and ex-post dynamic sharing brings few benefits. [ABSTRACT FROM AUTHOR]

An automated methodology that generates sufficiently accurate building models is essential for both the adoption of advanced control strategies like Model Predictive Control, and the estimation of building energy flexibility required for building-grid interaction. This paper presents such a methodology for the generation of multi-zone resistance-capacitance (RC) thermal network models of residential buildings. The models are calibrated using smart thermostat data and predict the indoor air temperature up to 24 h ahead. The methodology starts with a very simple model and iteratively adds one parameter at a time; the parameter that increases the quality of the model the most, as measured with the Bayesian Information Criterion. When the quality of the model cannot be improved any further, the procedure is reversed to delete any redundant parameters. The algorithm concludes when neither adding nor removing a parameter increases the model quality. The algorithm is applied on measured data from an experimental facility, a bungalow house in Québec, Canada. The resulting model can accurately predict all 9 zone temperatures 24 h in advance with an RMSE of 0.44 °C. An examination of the estimated parameters shows that they reflect the layout of the house, previously unknown to the methodology. [ABSTRACT FROM AUTHOR]

Building Archetypes (BAs) are often used in modeling urban-scale building energy consumption profiles to represent the energy consumption behavior of typical buildings within the modeled urban environment. One current approach to the development of BAs involves the creation of Building Energy Models (BEMs) using surveyed data coupled with codes, standards, or engineering experience. Another approach is the modification of existing standardized BEMs using available data to produce a suitable BA for the representation of the building stock. The adoption of either of these methods has been treated as an axiom or a step that did not need much consideration. However, the results produced by BEMs created according to different methodologies present several differences. The discrepancy in the produced results is shown not only in the results of the BEMs developed according to different methodologies but also in the degree to which these results vary when changing input values. Limited studies analyzed the sensitivity of these models to uncertain inputs, even though it is essential for the development of BA libraries. This paper comparatively investigates the Region-Specific (RS) and Department of Energy (DOE)-based BEM creation methods for BA library development. The models are developed by following the two methodologies, and their predictions are compared to the metered data from a high-rise residential building in the Marina district of Lusail City, Qatar. A Global Sensitivity Analysis (GSA) method is used to identify the most influential parameters contributing to the energy consumption variation of these models. Further, a parametric simulation campaign is conducted by varying the inputs determined earlier to quantify the BEMs' output variability. According to the results, the baseline RS BEM and DOE BEM yield an Energy Use Intensity (EUI) of 211.03 kWh/m2 and 137.9 kWh/m2, respectively. The deviation of these values from the metered EUI of 185,37 kWh/m2 indicates that the RS BEM development methodology is preferable to the DOE BEM for the BA development. Based on the GSA results, regardless of the BEM development methodology used, the most important parameters contributing to output variation are the Cooling Setpoint, the Outdoor Air Requirements, the Equipment Power Density, and the Lighting Power Density. Further, the RS BEM's parametric simulations produce a mean cooling EUI of 324.83 kWh/m2, closer to the literature benchmarks than the mean cooling EUI of DOE BEM, 341.62 kWh/m2. The results obtained from the baseline model and the uncertainty analysis support the conclusion that a Region-Specific Model developed from the ground up serves to better represent high-rise residential buildings in regions with extreme climate conditions. The impact this work achieves is providing decision-making support for urban scale building energy modelers for future building stock representation efforts in such areas while also providing an expected uncertainty range for the developed high-rise residential archetypes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Simulation of residential building energy supply with heat pump or hydrogen systems. • 2050: Hydrogen economically-viable if H 2 -/Elec.-price is less than 34–61 % per kWh. • For conservative price predictions: Electric heat pumps are most economical solution. • Energy carrier switch for single-family houses: H 2 -/Elec.-price below 44 % per kWh. • Energy carrier switch for multi-family houses: H 2 -/Elec.-price below 58 % per kWh. • Fuel cells are emerging as an economically-viable option for multi-family houses. Fossil-fueled heating in the building sector is responsible for 16 % of Germany's total CO 2 emissions. Options for the climate-neutral heating of buildings include renewable electricity or renewable hydrogen. In this paper, we conduct a bottom-up study to investigate the role of hydrogen in the climate-neutral energy supply of ten selected residential buildings in comparison to electricity-based systems. Based on a demand simulation and linear optimization of the supply system, sensitivity analyses identify the threshold values of the hydrogen price for the use of hydrogen technologies (hydrogen boilers or fuel cells) in building energy systems and make the quantities of hydrogen consumed visible. The results specify the first installations of hydrogen-operated fuel cells in multi-family houses if the hydrogen price reaches 0.17 €/kWh H2 in 2050 at an electricity price of 0.31 €/kWh el. Generally, for different building types and electricity supply prices, it indicates that the use of hydrogen becomes economically-viable if the price of hydrogen supply is in the range of 34–61 % of the price of the electricity supply per kWh and below. For the case of green hydrogen obtained using renewable electricity, it is highly questionable whether hydrogen supply will be that much cheaper than direct electricity supply, making its economical use in buildings uncertain. [ABSTRACT FROM AUTHOR]

Heating and cooling of residential buildings is accountable for a significant share of the EU's final energy consumption. In order to determine the current condition of the building stock and to implement appropriate measures and requirements to improve energy efficiency of buildings, a detailed analysis of existing buildings is essential. This paper aims at describing characteristics of the building stock of Salzburg by analysing energy performance certificates. Therefore, the building stock is grouped into archetypes based on the construction period, building condition (not-retrofitted and retrofitted) and building type (single-family house and multi-family house) and then analysed regarding energy need for space heating, energy carrier, heat emission system, share and U-values of building elements and window to wall ratio. The analysis of the data demonstrates that the structure and naming conventions within the EPC-database pose hurdles for a correct investigation of the data. A comprehensible and distinct notation as well as versioning is inevitable for the sake of avoiding a loss of information and enabling international comparability. An analysis of energy carriers of the archetypes reveals, that a significant share of buildings still uses fossil fuels like oil and gas for energy supply, although electricity (heat pumps) and district heating are on the rise for buildings constructed in recent periods as well as for retrofitted buildings, with heat pumps being the most important energy carrier for new single-family houses amounting to a share of 65 % and district heating for new multi-family houses amounting to a share of 46 %. A detailed observation of the thermal performance of buildings and building elements reveals high potential and practicability not only for energy efficient new buildings, but also retrofitted buildings throughout all construction periods and for both, single-family houses and multi-family houses (e.g. median U-values for retrofitted ceilings/roofs range from 0.17 to 0.26 W/(m2K) compared to a median of 0.14 W/(m2K) for not-retrofitted ceilings/roofs from the most recent construction period). The conducted analysis provides detailed statistical information on the Salzburg building stock and hence a robust basis for further studies regarding the thermal performance of the building stock and future scenarios by means of urban building energy modelling approaches. Furthermore, based on this information, policies for energy efficient buildings can be improved and adapted according to current circumstances. [ABSTRACT FROM AUTHOR]

One of the main required actions for moving forward in zero-carbon cities' mission is to increase building energy efficiency. Building energy simulations are key tools for HVAC control, building energy management, testing different building energy efficiency scenarios, and providing feedback to the occupants. Unfortunately, these tools have a significant weakness in occupant behavior modeling and prediction. Several recent studies used different occupancy measurements and methods to predict occupant behavior. However, they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. This paper aims to leverage passive WiFi sensing methods for occupant behavior estimation using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role in zero carbon and smart cities in the future. Different machine learning algorithms have been applied to the collected, denoised, and preprocessed Channel State Information (CSI) data to estimate the occupant behavior. Five schedules of occupancy, occupant activity, lighting, electrical equipment usage, and thermostat set point temperature could be extracted and estimated from CSI data. In the last step, all estimated schedules were fed into EnergyPlus, an energy simulation software, along with other required data for accurate energy demand calculation to estimate heating and cooling demand, which can be used for HVAC control and occupancy feedback. [ABSTRACT FROM AUTHOR]

This paper models scenarios for energy label based retrofit targets for Switzerland's residential building sector for 2050. It takes into account the magnitude and uncertainty in the energy performance gap (EPG) between labelled and actual energy consumption using actual energy performance data, as previous work highlighted that the EPG is not evenly distributed across different building labels. A building stock retrofit model is developed to estimate the potential energy savings from improving building performance through increasing energy label rating. A Monte Carlo analysis is applied using real EPG distribution data. The uncertainty in total energy savings is calculated for business-as-usual case and for three scenarios based on the different retrofit incentive policies of different Swiss Cantons. This allows to quantify the uncertainties in energy and emission outcomes of different policy targets arising from the variable EPG. The results show that with the business-as-usual approach it is impossible to achieve the objectives set (9.7 Mt CO 2 -eq/y vs 0.4 Mt CO 2 -eq/y by 2050). On the other hand, to reach the targets, a more aggressive policy aiming at renovating all buildings to the highest standards is necessary, that translates into a renovation rate that seems unrealistically high (3.4% per year until 2050 vs the proposed 1.4%). The contribution is to inform stakeholders designing new policies (notably the EU Renovation Wave policies) of the risk related to energy policies. [ABSTRACT FROM AUTHOR]

Reliable building simulation models are key to optimizing building performance and reducing greenhouse gas emissions. Informed decision making requires simulation models to be accurate, extrapolatable, and interpretable, all of which require calibrating model simulations to ground truth. Complicated building dynamics and highly uncertain exogenous disturbances make the model calibration process challenging and expensive; hence, a scalable and efficient calibration approach is needed to enable actual application. Current automatic calibration algorithms do not leverage data collected from multiple sources: for example, data obtained from previous calibration tasks on other buildings. In this paper, we employ probabilistic deep learning to meta-learn a distribution using multi-source data acquired during previous calibration. Subsequently, the meta-learned Bayesian optimizer accelerates calibration of new, unseen tasks. The few-shot (that is, requiring few model simulations) nature of the proposed algorithm is demonstrated on a Modelica library of residential buildings validated by the United States Department of Energy (USDoE). The proposed algorithm is compared against classical Bayesian optimization-based calibration, and it is shown that ANP significantly sped up the calibration procedure: the optimal model parameters are identified with 40–60% less simulations compared to the baseline. [ABSTRACT FROM AUTHOR]

[Display omitted] • A hierarchical approach for automatic layout of an underfloor heating system is proposed. • The routing problem is formulated as a shortest path problem in a proper routing graph. • An order relation is defined to separate the intersecting paths. • A depth-first layout algorithm based on recursion is developed to solve the coverage path planning problem. The layout of an underfloor heating system determines its heating performance. Conventionally, the design is completed by qualified experts using professional software, but this method is inefficient and heavily relies on the experts' previous experience. This paper proposes a hierarchical approach for the automatic design of underfloor heating systems aimed at residential buildings, and the underfloor heating layout problem is decomposed into a routing problem and coverage path planning problem. First, the routing problem is formulated as a shortest path problem in an appropriate routing graph and solved to provide the routing of pipes for further coverage. Subsequently, a depth-first layout algorithm is developed to recursively simplify the coverage path planning problem to obtain the basic rectangle layout, achieving uniform coverage of the given layout zone. Simulation results on two houses show that, unlike one popular commercial platform, the proposed approach generated feasible solutions in all 13 test instances. The new method improved the minimum temperature, average temperature, average percentage of nodes greater than 30 °C, and total energy efficiency by 3.18 °C, 0.79 °C, 3.23%, and 4.19%, respectively, with a lower variance on average. These improvements lead to a higher coverage rate, more even thermal distribution, and stronger robustness under flexible parameter selection. [ABSTRACT FROM AUTHOR]

• In 2017, renewable heat used in residential buildings in AT and CH was 48% and 30% • Renewable heat must be increased by 131% in AT by 2040 and 210% in CH by 2050. • Public subsidies contributed to 51% and 36% of total heat savings in AT and CH. • AT relies more on public subsidies and has a lower cost-effectiveness of policies. • CH obtains similar heat savings with eight times lesser public subsidies. Reducing heat demand and decarbonising heat supply are essential if countries have to meet net zero emission targets. This paper presents a comparative assessment of the impact of energy efficiency polices for heat demand reduction and heat decarbonisation in the buildings sector (2000–2017) in Austria (AT) and Switzerland (CH). It assesses the heat demand, heat supply, effect of different factors on changes in total heat consumption, impact of energy policies that provide public subsidies for heating and compares the effectiveness of different policies and measures. The assessment shows that renewable heat supply in residential buildings has to be increased by about 150 PJ (130% increase from 2017) in AT by 2040 and by 120 PJ (210%) in CH by 2050, which is challenging. Energy savings from improvements in energy efficiency from 2000 to 2017 are 83 PJ in AT and 97 PJ in CH, but these are largely offset by increase in activity and wealth effects thereby resulting in small changes in final heat consumption. The average (2014–2017) annual public subsidies for heating were about 2500 million Euros and 310 million CHF and contributed to 51% and 36% share of energy saving in AT and CH respectively. Both countries achieved similar energy savings, albeit following different policy approaches. Public subsidies for heating are an important policy instrument in AT but have lower cost effectiveness. On the other hand, CH minimised the use of public spending by deploying other policies and funding measures that leveraged private investment to achieve similar energy savings. [ABSTRACT FROM AUTHOR]

• Building stock modelling is implemented through a bottom-up method. • Dynamic simulations are applied through calibrated and parameterized stock models. • Optimal retrofit solutions under a 2050 future weather scenario are proposed. • Results are provided for four different climatic regions in southern Spain. • Results can be interactively accessed by stakeholders and the general public. Retrofitting the existing building stock is widely accepted as a crucial factor to reaching 2030 and 2050 climate and energy targets, given that the building sector is among the top three most dominant energy consumers. This paper presents a bottom-up study which uses calibrated and parameterized energy stock models (building archetypes), while also incorporating building stock information from a large database. The thermal performance of the existing social housing stock of southern Spain is assessed through dynamic simulation under present and future climate change scenarios. Subsequently, several passive and low-cost operation-related strategies are numerically optimized through genetic algorithms to determine the best retrofit solutions, taking into consideration global warming scenarios. A multi-objective decision analysis is carried out by optimizing annual overheating hours (%), annual undercooling hours (%), and investment costs (€/m2). Among the conclusions reported, it is important to note the feasibility of implementing low-cost retrofit strategies considering investment costs of up to around 200 €/m2, which would lead to average annual overheating and undercooling hours below 55 % and 45 %, respectively. However, retrofit solutions exclusively based on passive and low-cost operation measures were proven to be significantly limited to improve thermal comfort results in the social stock. [ABSTRACT FROM AUTHOR]

• A probabilistic tool to synthetically simulate harmonic injections from electric devices in residences is developed. • The residential harmonic injection tool (named RESHAPES) is made freely available for any researcher. • Harmonic injection of many different residential devices is modelled with advanced statistical techniques. • New devices and technologies (electric vehicle, photovoltaics, LED lighting) are included in the models. • Validation of the residential harmonic injection model is done with real measurements. The use of modern loads in residential distribution networks has given rise to increasing concern and to the need for techniques that improve the understanding of their collective impact. To address this issue, this paper presents a simulation tool that models the stochastic profiles of harmonic currents injected by residential loads in modern smart grids. The simulator considers a bottom-up approach by taking into account the stochastic consumption patterns of each household device, and their harmonic injection spectra, modeled with Gaussian mixtures. In the presented tool, weekly harmonic injection profiles can be generated for a single dwelling or several dwellings. The model was validated by means of weekly current injection measurements performed in seventeen dwellings. The presented harmonic injection model named Reshapes (Residential Harmonic Probabilistic Emission Simulator) has been made freely available for researchers so that they can use it for their own purposes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Most occupants had good lighting conditions at the home office. • Students have poorer home office conditions compared to professionals. • Occupants satisfied with visual environment intend to continue home office. • Cultural and seasonal differences due to continents led to different lighting uses. • Most participants appreciate daylight as the primary light source. The global pandemic and physical distancing restrictions are forcing us to rethink how residential buildings are used regarding the visual environment. This paper describes home office lighting conditions within different countries and continents. The aim is to define the current limitations of home offices in providing a resilient visual environment. The work was developed by a team of international experts working together on Subtask A: User perspective and requirements, in International Energy Agency IEA SHC Task 61/EBC Annex 77 "Integrated Solutions for Daylighting and Electric Lighting". The method included an international online survey in 6 countries in South America, Europe and East Asia containing 37 questions about general data, physical characteristics of the home office and occupant's perception, behaviour and needs regarding the visual environments, and photos taken by the occupants. Descriptive statistics, inspection of the photos, and qualitative analysis from the open-ended question were performed. In total, 694 responses and 453 photos were analysed, making it possible to identify the occupants' perceptions about the lighting and visual environment in the home offices both for professionals and students. The results indicate cultural differences in styles of residential buildings and interiors, in the custom of using lighting, and between continents and occupational categories. Possible improvements are suggested. [ABSTRACT FROM AUTHOR]

• Occupants on-demand heating control behavior in a rural residential building is investigated. • Independently controlled split-type heating equipment were used to elucidate actual demand patterns. • Data collection over three years under an interference avoidance scheme. • Five representative intermittent heating patterns were obtained from a real-world case study. A better understanding of actual heating demands is crucial for optimizing operational heating systems, because disparities between expected load and actual demand are key factors in inefficient heating system design, implementation, and corresponding energy loss. Additionally, the discrete, energy consumption-based billing systems applied to freestanding rural residential buildings encourages reductions in energy waste but produces spatially and temporally distinct heating patterns. Conventional heating systems have only basic controls and slow response times that cannot reflect actual user demand. Hence, this paper presents an experimental scheme for measuring room-wise heating demands, and thus, the actual heating demand patterns of rural residential buildings. A case study was conducted on a typical rural residential building in Beijing to validate the method's feasibility. Real-world usage patterns were obtained using indoor environment sensors and independently-controlled heating devices that collected the operational parameters of each room over three years, in a long-term non-intervention study design. Clustering analysis and various indicators were used to characterize heating demand for each room: heating patterns, usage intensity, and temporal variation. In most case, the occupants heated only a few rooms, rather than the whole building. These results have potential real-word implications for predicting occupant heating behavior and energy conservation. [ABSTRACT FROM AUTHOR]

In this paper, a value-engineering based integrated design approach is presented to improve energy efficiency of residential buildings. The design approach is based on reducing construction costs from oversizing structural systems of the building to allow investing in energy efficiency measures. The integrated design utilizes optimized selection procedure to identify set of measures that can minimize life-cycle costs while maximizing energy consumption for the buildings without exceeding budget thresholds defined by the value engineering analysis. The integrated design approach is demonstrated and applied to design a prototypical villa in Kuwait considering both homeowner and government perspectives to optimize the selection of energy efficiency measures. The results indicate that significant reductions in construction costs can be achieved due to commonly practiced oversizing of the villa's structural systems. These cost reductions are more than sufficient to cover the incremental costs of implementing optimally identified energy efficiency measures with the benefits of lowering annual energy demand by 70% compared to the current design practices in Kuwait. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multimodal occupancy detection framework for residential buildings. • Two variants of Occ-STPN models with feature level fusion and decision level fusion. • Feature extraction and occupancy detection models for various data modalities. • Transferability of a trained occupancy detection model from one building to another. • Occupancy prediction performance evaluation on three open-sourced datasets. For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This paper proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

Human influence is recognized as a major source of discrepancy between predicted and actual building energy use and has been a subject of extensive analysis for many years now. However, the impact that different users can bring to the actual performance of a building has not yet been studied in depth. This paper aims to contribute to this understudied dimension of the performance gap by providing a quantitative assessment of the impact of different users in residential heating consumption and indoor environmental conditions. For this purpose, heating energy consumption profiles for real users along with their corresponding thermostat settings were modeled in a building simulation model of a single dwelling thus allowing the study of the user factor in isolation. Remarkable differences were found in the resulting heating energy consumption and in the percentage of time that thermal comfort conditions defined by building regulations and standards were met for the different user scenarios. Results also demonstrate how the more complicated and varying the heating practices of a user are the more varying and unpredictable the impacts on heating consumption and comfort are. Overall, findings add to the growing body of literature that questions the standardized heating profiles and temperature settings used in energy simulation for residential buildings. [ABSTRACT FROM AUTHOR]

• Estimation of annual electricity consumption (EC) of more than 1,700,000 buildings. • EC models based on top-down, bottom-up, regression analysis, GRU and GIS mapping. • Contribution of building and socio-demographic variables on EC. • Effect of temperature on actual and predicted EC used for heating and cooling. • EC is spatialized on different territorial scales. This study financed by ERDF and the Wallonia Region estimates the annual electricity consumption (EC) of building stock, including the 3 building sectors namely residential, tertiary and industrial building. The estimation takes into account appliances, electrical heating, cooling, lighting, cooking and EC by m2 on a building level. The results are spatialized on different territorial scales. Using cadastral data of more than 1,700,000 Walloon buildings and annual EC data from a sample collected in 2012 from the energy reports, the paper assesses the EC of the whole building stock and tests to what extent different types of variables (building factors and socio-demographics) explain annual EC. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the research recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue. Three separate regression models were used to study the predictors of annual EC of residential, tertiary and industrial buildings. EC building factors (appliances, auxiliary and main heating, domestic hot water and cooking) explained the largest share which is 66.46% of the variability in EC for residential buildings whereas the EC usages share for tertiary buildings (lighting, heating and domestic hot water, air conditioning, cooling, etc.) is about 50.53% and 38.55% for industrial buildings. Socio-demographic variables on their own explained about 61.59%, 26.34% and 3.41% of the annual EC, respectively for residential, tertiary and industrial buildings. Hence, the building variables present the highest explanatory power for EC, presumably because heating and cooling EC are included in this study. The study highlights that when attempting to explain EC related to Walloon households, including heating and cooling EC, appliances usage has the strongest predictive power in residential buildings. On the other hand, the projected decrease in EC use for heating in existing residential buildings is −8.82% and −10% for existing tertiary buildings while the projected increase in EC use for cooling in existing tertiary buildings is + 11.94% from 2012 to 2050 on a regional scale. These trends follow the predicted regional heating degree-days (HDD) of −11.76% and cooling degree-days (CDD) of 14.04% for the same period based on the gated recurrent unit (GRU) an implemented deep learning (DL) model. In addition, the produced EC maps on different territorial scales show that the highest EC is seen in large and main cities in general. [ABSTRACT FROM AUTHOR]

• A systematic literature-approach to classify retrofitting is proposed. • An overview of Spain's previous guidelines on residential retrofits is presented. • A review of financial and subsidies mechanisms in Spain to renovate buildings is introduced. • A literature-based matrix on retrofitting actions is presented. • A classification of energy efficiency strategies for retrofitting is proposed. The building sector is responsible for more than 40% of final energy consumption in Europe; consequently, retrofitting existing buildings can significantly contribute to mitigate greenhouse emissions. Nevertheless, although the European Union has introduced relevant policy instruments, up to now the annual retrofit rates are considerably low with an average of 0.2% at European level. Understanding the past would support the success of the new strategies, such as the recent ones launched through the New Green Deal, which reinforces the importance of renovating existing buildings stock, as the main aim of the renovation wave. As a starting point for assessing the main challenges to allocate the Recovery funds, this paper provides a review of Spain's relevant literature to clearly define the characterization of the existing building stock, which actions have been typically considered under the scope of retrofits, and which strategies have been implemented until now, considering both, private and public initiatives and public financial schemes. The present work has a strong focus on energy-related strategies, although other interrelated actions are mentioned, such as accessibility measures and interior refurbishment of dwellings. Differences and similarities within these approaches are presented through the analysis of 27 relevant references of retrofitting actions and 52 references of financial mechanisms. As in Spain 93% of multifamily buildings were constructed prior to the national transposition of the first Energy Performance Building Directive (EPBD), and therefore are in need of deep energy renovations, the retrofitting of multifamily buildings topic has been widely addressed, even though often in a disjointed manner. The study suggests that although historically there have been several recommended strategies, few are conceived as integral actions, only 15% of the intervention packages refer passive and active energy-related measures and renewable energy incorporation. Furthermore, considering the antiquity of the building stock and the advantage of addressing the topic through clusters, only 63% of the resources directly refer these actions to specific clusters among the six distinctive periods, although in most cases focusing their recommendations on buildings constructed before 1980 where the worst energy performance buildings are concentrated. Concerning public financial and funding instruments, the research illustrates that there has been a substantial increment of programs over the last years promoting energy-related measures, renewable energy and other complementary aspects such as accessibility and interior renovations, mainly for older clusters. Nevertheless, the funds constitute only 8% of the necessary declared investments, which along with the dispersion between the different procedures and the complexity of the processes, have significantly reduced the global impact of these actions without generating robust and oriented market dynamics. Despite the several resources found, both financial instruments and guidelines tend to describe in more detail strategies related to heating demand and consumption and not delve into other relevant actions for warmer climates, fact associated both to the dynamics coming from other countries, and to a vision of the past in which people do not consume refrigeration. This clear focus on heating loads rather than cooling could present limitations for warmer regions in the near climate-change future. [ABSTRACT FROM AUTHOR]

• Occupancy-based HVAC control using dynamic conditioning time estimation. • Deep learning algorithms are employed to provide online estimations of heat-up time. • Profitability of smart thermostats is studied via comprehensive financial analysis. • Occupancy information might negatively affect peak energy demand. • Conservative setback provides relatively poor financial gains. This paper presents a rule-based (RB) heating, ventilation, and air-conditioning (HVAC) control system using a multi-layer perceptron network, a deep learning algorithm, for estimating dynamic preconditioning time in residential buildings. The proposed system takes advantage of occupancy, indoor temperature, and weather data to make control decisions in buildings. The system performance is evaluated in terms of financial, demand-side management, energy-efficiency, and occupants' thermal comfort. The proposed approach considers the perfect occupancy prediction assumption to remove the impact of the uncertainty associated with occupancy prediction and to estimate an upper bound limit for the system performance. The system performance is compared with that of conventional rule-based control approaches to show its effectiveness. To select the optimal control system, the TOPSIS method, as a multi-criteria decision-making approach, is employed. The sensitivity of the proposed system to the temperature setback is also assessed by considering conservative, medium, and deep setback bounds. It is demonstrated that the proposed system outperforms other alternatives when the deep and medium bounds are utilized. This study reveals two limitations of occupancy-based control systems by investigating their performance from financial and peak-demand points of view. First, these systems can cause peak-demand issues and increase the on-peak energy consumption by up to 10%. Secondly, employing a conservative setback can significantly decrease the financial merits of the system, leading to a discounted payback period of 10.87 years for implementing smart thermostats. [ABSTRACT FROM AUTHOR]

• Changes of building airtightness of 61 houses are evaluated on different time scales. • Airtightness of houses may deteriorate mainly during the first two years. • 2-Storey houses with wood frame roofs present a higher risk of airtightness deterioration. • The increase in the number of leakages is not correlated with changes in the airtightness. • The airtightness of the houses tested is not affected by seasonal variations. The main objectives of this paper are to quantify and qualify changes in building airtightness on different time scales, and to identify factors that may explain the variations observed. A special measurement protocol, based on ISO 9972 but with some additional requirements, was defined after a detailed literature review. For the mid-term campaign, 30 new detached houses were measured once per year over a 3-year period. It was observed that air permeability increases slightly during the first year (a mean increase of 18%), and then stabilizes during the second and third years. Five of them were measured twice per year but did not show any significant seasonal variation. For the long-term campaign, 31 detached houses built during the last 10 years were remeasured once. Air permeability shows a similar increase to that of the mid-term campaign after 3–10 years (a mean increase of 20%). The results show an overall increase in the number of leakages detected for all houses, but this increase is not always correlated with the change in air permeability. Results pointed to three factors that could explain the deterioration of airtightness: the number of levels, the type of roof and the type of building material and air-barrier. [ABSTRACT FROM AUTHOR]

The need for accurate balancing in electricity markets and a larger integration of renewable sources of electricity require accurate forecasts of electricity loads in residential buildings. In this paper, we consider the problem of short-term (one-day ahead) forecasting of the electricity-load consumption in residential buildings. In order to generate such forecasts, historical electricity consumption data are used, presented in the form of a time series with a fixed time step. Initially, we review standard forecasting methodologies including naive persistence models, auto-regressive-based models (e.g., AR and SARIMA), and the triple exponential smoothing Holt-Winters (HW) model. We then introduce three forecasting models, namely i) the Persistence-based Auto-regressive (PAR) model, ii) the Seasonal Persistence-based Regressive (SPR) model, and iii) the Seasonal Persistence-based Neural Network (SPNN) model. Given that the accuracy of a forecasting model may vary during the year, and the fact that models may differ with respect to their training times, we also investigate different variations of ensemble models (i.e., mixtures of the previously considered models) and adaptive model switching strategies. Finally, we demonstrate through simulations the forecasting accuracy of all considered forecasting models validated on real-world data generated from four residential buildings. Through an extensive series of evaluation tests, it is shown that the proposed SPR forecasting model can attain approximately a 7% forecast error reduction over standard techniques (e.g., SARIMA and HW). Furthermore, when models have not been sufficiently trained, ensemble models based on a weighted average forecaster can provide approximately a further 4% forecast error reduction. [ABSTRACT FROM AUTHOR]

• Linking top-down energy efficiency indicator analysis with bottom-up policy analysis. • Explaining the contribution of energy efficiency policies in Germany and Switzerland. • Analysing the counteracting effects to energy policy of rebound and non-compliance. This paper studies the factors underlying the evolution of the energy consumption for space heating in residential buildings by linking top-down analysis, based on meso -indicators, and bottom-up analysis, based on policy evaluations. The top-down analysis (i.e. the perspective from energy statistics) allows one to separate the change in total energy use into activity level, societal factors and energy efficiency gains. The explanatory power of the resulting meso-(statistical) indicators is often limited, if the underlying factors are not examined (e.g. changes in heating levels and patterns, weather effects, cost of energy and policies regarding insulation and heating system standards). We overcome most of these drawbacks by conducting a bottom-up analysis (i.e the perspective from single policy measures), which enables us to discern the contribution of energy efficiency policies to the changes observed with the meso -analysis. We focus on space heating consumption in the residential sectors for Germany and Switzerland. A major aim of this analysis is to show the contribution of energy efficiency policies (such as thermal building regulation, subsidy programmes, fiscal measures etc.) towards the changes in this indicator. The results show that the progress in energy efficiency (both autonomous and policy induced) in both countries had the greatest effect (-776 PJ for Germany, −42 PJ for Switzerland) regarding the change in energy consumption for space heating in the period from 2000 to 2016. However, the impacts of "technical and comfort" rebounds (+436 PJ for Germany, N/A for Switzerland) and other developments such as societal changes (+316 PJ for Germany, +35.5 PJ for Switzerland) were found to compensate for a significant part of the energy efficiency gains. In both countries, it was possible to link physical energy efficiency indicators to policy evaluation, but limitations were also identified which are primarily related to data gaps. [ABSTRACT FROM AUTHOR]