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Effectively utilizing low-grade thermal energy is a promising approach to mitigating greenhouse gas emissions while reducing the burden on centralized power grids. Current thermoacoustic heat pumps and power generators face challenges such as high onset temperature differentials and low performance. This paper addresses these challenges by introducing a gas-liquid resonator into a thermoacoustic combined heat and power systems to recover low-grade thermal energy in residential buildings. Through Sage modeling and calculations, the internal characteristics of the proposed system and its output performance under different operating conditions are explored. At a heating temperature of 350 °C, the system can generate 6.4 kW of output thermal power, 0.9 kW of electricity, and overall exergy efficiency is 79.3 %. Combining neural network models with case studies conducted in Spain and Finland, the system can annually save 5.6 MWh and 20.7 MWh in fuel energy, reduce emissions of 1374 kg and 5180 kg of carbon dioxide, and save a total cost of €611 and €2324, respectively. Furthermore, comparisons with other emerging micro-CHP systems highlight the efficiency of the proposed system. These results indicate the high potential of thermoacoustic combined heat and power systems in recovering low-grade thermal energy and achieving energy savings and emission reductions. • Introducing an innovative thermoacoustic micro-CHP system. • Achieving an exergy efficiency of 79.3 % under standard operating conditions. • One system can annually reduce household CO 2 emissions by 5180 kg. • Ideal for households, balancing lower output levels for both heat and electricity. [ABSTRACT FROM AUTHOR]

Recently, the policymakers, economy, and societies' primary and ambitious objective has been to make the world cleaner and carbon neutral. One critical problem in reaching these goals is building energy management, including using local energy sources. This paper provides a methodology based on a total sustainable performance index estimated as the "total life cycle costs," including externalities, that apply to a possible energy source, energy generation, energy storage, energy use, and management. The methodology is applicable to comparative analysis of the locally available energy sources, optimization of the energy generation mix, and real-time energy management of the building, especially for individuals and groups of small and private houses. The significant results of this research conducted in this paper show that (i) the effective, optimized energy mix includes 2 or 3 types of locally available energy sources, (ii) the real-time energy support of the building is effective in case of dealing with all the energy sources, energy generation, and energy use methods, (iii) the energy management system should be centralized and supported by a large number of sensors, (iv) developing local micro grid including 20–100 houses increasing the energy supply efficiency for 20–45%. • Residential buildings use 26% of total energy in the European Union. • All the applicable technologies and locally available energy sources must be optimized/managed. • New objective function: overall life cycle cost/unit of energy used (single parameter index). • Concept/methodology created for optimization/management of building energy support. • Developed system improves building energy support for efficiency by 12–26%. [ABSTRACT FROM AUTHOR]

The 2023 revision of the Energy Performance of Building Directive underlines the need to consider the whole life cycle emissions for buildings. The decarbonization targets could not be achieved if reduction of energy demand in the operational phase is accompanied by a high energy resource use in the whole life. This paper proposes a novel investigation of an energy renovation project, involving both building envelope components and space heating system. The adopted method is based on the life cycle assessment, in the variant cradle-to-gate with options, to analyze and compare the design data achieved through an analysis of product (embodied) and use (operational) stages. Different solutions for energy improvement of the building performance are identified, and the scenario with the lowest embodied energy was found to be more energy efficient globally. The most effective scenario, with energy and CO 2 savings of 39% and 40% is characterized by a weight of embodied energy and emissions of 6.5% and 6.9%, respectively. Given the rise of the weight of the embodied energy in the whole life cycle of future buildings, the life cycle assessment will become crucial for sustainable design and in the transition to an energy-efficiency built environment. [Display omitted] • The impact of life cycle analysis in the process of energy efficiency is investigated. • The "cradle-to-gate" with options approach is implemented. • The efficiency of walls and windows involves the greater use of energy resources. • The most energy-efficient scenarios from an operational and non-operational point of view are different. • The scenario identified in the analysis of A1-A4 phases allows a higher total energy saving. [ABSTRACT FROM AUTHOR]

The paper evaluates the potential energy efficiency benefits for dynamic sliding exterior shades suitable for windows of both new and existing buildings. In particular, the analysis outlined in the paper summarizes the energy performance of dynamic sliding shades when applied to windows for apartment units located in various US climates. A series of sensitivity analyses is performed using a detailed whole-building simulation coupled with modeling techniques of dynamic envelope systems to determine the optimal positions for the dynamic sliding shades to minimize the annual demand for US housing units. Several operation strategies are evaluated using continuous and stepped settings of the positions for the dynamic sliding shades using monthly, daily, and hourly adjustment frequencies. The results of the analyses indicate that the dynamic shades have significant energy efficiency potential for all US climates and design conditions with annual savings in HVAC energy end-use of over 50% especially in hot and mild climates. The performance of the dynamic shades is even higher for large and single-pane windows indicating that these systems can serve as retrofit alternatives to window replacements. • New and low-cost dynamic and insulated shading systems are introduced. • Energy benefits of the dynamic shades are assessed for residential buildings. • Optimal controls for the dynamic shades are determined for US climates. • Dynamic shades achieve over 50% reduction in heating and cooling energy use. [ABSTRACT FROM AUTHOR]

Solar air heating is very promising in residential buildings on Tibetan Plateau, and effective heat storage is indispensable for continuous heating. Exterior wall is often used for the heat storage, whereas the interior wall is seldom exploited. This paper investigates a heating system combining solar air collector with hollow ventilated interior wall for residential buildings on Tibetan Plateau. A simplified model based on discrete mathematics is established by EnergyPlus and is validated for modeling this system. With this model, a case study is performed to evaluate thermal performances of this system. Moreover, influencing factors of this system are analyzed as well. Results show that compared with the other two typical systems this system increases the minimum indoor air temperature by 6.5 °C and 3.2 °C with night heating need reduced by 79.9% and 60.7%, respectively. For the interior wall material, time constant and thermal inertia are dominant. The optimum window-wall ratio for the south façade is within 0.3–0.4, while the best circulation air flow rate is within 300–500 m³/h. The outdoor air temperature has few influences on the heating performance of this system. Overall, this system is applicable to the heating of residential buildings on Tibet Plateau. • A simplified method for modeling the SAC-HVIW heating system is proposed. • Influences of different factors on the system thermal performance are studied. • Thermal performance of this SAC-HIW system is analyzed. [ABSTRACT FROM AUTHOR]

The paper provides a systematic evaluation of the optimal optical characteristics of smart windows to maximize their energy efficiency benefits when deployed for residential buildings. The evaluation accounts for a wide range of design specifications and operation conditions of housing units including building orientation, window sizes, and climatic zones. Using integrated analysis aimed to minimize energy use required to heat and cool housing units, the optimal design and control settings are determined for two types of smart windows including those that can switch between only clear and dark tint states and those that can transition to multiple intermediate tint levels. The results from a series of sensitivity analyses show that the required design optical specifications for the smart windows could depend on several variables including primarily climate characteristics and window features including size, orientation, and U-value. Significant energy savings can be achieved by smart windows relative to the code-compliant static fenestration systems ranging from 21% in cold climates to 5% in warm climates. These energy savings can be achieved using stepped controls to set the tint levels of the smart windows. Additional albeit slight savings can be obtained through gradual controls for those smart glazing that have the capabilities to transition to intermediate tint levels. • A new analysis approach is presented for selecting optical properties of smart glazing. • Optimal optical properties for smart glazing can be identified for any residential building. • Energy efficiency benefits of smart glazing are enhanced using optimal optical properties. • Over 20% energy use can be saved by smart glazing relative to code-compliant systems. [ABSTRACT FROM AUTHOR]

The energy retrofitting of residential stock is not always straightforward where heritage protected housing is concerned. In southern Europe, many examples built under the Modern Movement codes are now obsolete in terms of energy efficiency. The aim of this paper is to assess the potential for improvement of comfort conditions and energy efficiency in Mediterranean heritage residential buildings, through the incorporation of passive strategies in keeping with preserving heritage values. Long-term monitoring and field tests in representative flats of the case-study building were carried out and, focusing on a single example, adaptive comfort equations were used to assess results. The passive strategies considered are the thermal isolation of the air cavities of walls, changing window frames, optimizing window glazing, and setting up regular mechanical ventilation rates for indoor air changes. Although simulation results generally predict a better energy performance after testing retrofit strategies, the potential decrease in energy demand is more noticeable than the potential improvement in comfort conditions. • The improvement of comfort and energy efficiency in a heritage building is assessed. • Long-term monitoring and field tests were carried out in representative flats. • Passive strategies in keeping with preserving heritage values were examined. • Adaptive comfort equations were used to assess results focusing on one flat. • Energy efficiency improvements are more noticeable than those of comfort conditions. [ABSTRACT FROM AUTHOR]

Building orientation is one of the driving factors that impacts building energy consumption, which deserves to be studied. The Sizhai traditional dwellings located in Zhejiang province were chosen as the study buildings in this paper. The geometric model was rotated at 20° increments clockwise to create a total of 18 test scenarios. Integration of virtual simulation in software, the thermal and daylighting performances in 18 test scenarios were simulated to assess their indoor comfort levels in terms of the indoor temperature, air velocity and illuminance. Furthermore, the indoor comfort level has an impact on the energy consumption of building. To this end, the electricity consumption of air conditioner (AC), electrical ventilation and artificial lighting, as well as the total annual electricity consumption including AC, electrical ventilation and artificial lighting were computed by software to explore the optimum Building Azimuth (BA). The results show that a total annual electricity cost difference of approximately 150 kW h throughout a year between the best (S) and worst (N-W80) BA of the building was achieved. By performing BA measurement for all dwellings in Sizhai village, the conclusion is that this village has a high energy saving potential. • Building model was rotated at 20° increments clockwise to create 18 test scenarios. • Three evaluation parameters were adopted to assess indoor comfort level. • Electricity use difference of 150 kW h between the best and worst BAs was achieved. • Energy saving potential was evaluated for Sizhai integrated with Google Earth. [ABSTRACT FROM AUTHOR]

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility. • An ANN-MPC was developed and tested in a residential building. • ANN models and an MPC framework were validated against measurements in buildings. • An EMPC to maximise the demand flexibility of residential buildings was developed and tested. [ABSTRACT FROM AUTHOR]

Abstract South Africa's political history, unique demographic profile, and economic growth drivers, present a challenge in adopting universal strategies and business models to plan for the impact of battery electric vehicles in the residential sector. This paper investigated battery electric vehicle (BEV) substitution of internal combustion engine vehicles (ICEVs) on a provincial level, to better plan for additional electricity requirements. This was achieved by developing the E-StratBEV model, using system dynamics. The model results show that nationally, although the Low Growth scenario indicates about 233,700 BEVs by 2040, the adjusted number (based on the spending behaviour within deciles) may result in 44,155 BEVs while the High Growth scenario's expected 2.39 million BEVs by 2040 could be adjusted down to 451,736 BEVs. For a coal heavy supply mix, the cumulative carbon emissions from 2019 until 2040 resulted in negligible benefits, so provinces may not be able to leverage on passenger electric vehicles to reduce provincial environmental emissions. Lastly, the BEV growth drivers introduced an additional 1589 BEVs to the Low Growth scenario and an additional 16,257 BEVs to the High Growth targets from 2019 to 2040, with the purchase price being the dominant driver. Highlights • Households may purchase 80% less than expected passenger EVs. • Negligible benefits for passenger EVs to reduce environmental carbon emissions. • Reducing purchase price has the biggest impact to increase EV sales. [ABSTRACT FROM AUTHOR]

Abstract The present paper explored the potential of the seasonal solar thermal energy storage (SSTES) system using ammonia-based chemisorption for domestic application in the UK. The dynamic charging/discharging performance of the SSTES has been simulated using the real weather data with the solar thermal collector models, the domestic heating demand model and the chemisorption model. The selection of working salts has significantly influence on the system design and dynamic performance. The CaCl 2 -4/8NH 3 chemisorption can satisfy almost 100% of space heating demand when using low temperature hating facility during discharging stage, however, due to its relatively higher desorption temperature and limited sunlight available in the Newcastle-upon-Tyne the required solar collectors area exceeds the commonly available space of dwelling roof. The NaBr-0/5.25NH 3 chemisorption is only able to contribute 18.6% of heating demand because the temperature of the discharged heat cannot reach the required level for most of the time in the heating season. The best scenario studied was using BaCl 2 -0/8NH 3 chemisorption SSTES (45.2 m3 storage volume) combined with low temperature heating facilities and a 30.5 m2 solar collector, which can cover about 57.4% of space heating for a dwelling with a heat loss coefficient at 150 W/K. Highlights • Feasibility of domestic seasonal solar thermal storage using NH 3 chemisorption. • Integrate simulation of solar heat, domestic heating demand and NH 3 chemisorption. • Solar collector area, storage volume size and storage ratio using different salts. • BaCl 2 -0/8NH 3 was identified as the preferable working pair. [ABSTRACT FROM AUTHOR]

Abstract As a large part of primary energy consumption in different countries is related to the residential sector, a reduction in energy consumption at the " household-scale " may determine a significant reduction in the carbon emission at the " country-scale ". Hence, the precise knowledge of the relationships between the household characteristics and the energy consumption/expenditure may serve as basis for policymakers when planning investments aiming to reduce the primary energy consumption in the residential sector. This paper contributes to the existing discussion, focusing on the Italian case study, by analyzing the relationships between the residential energy expenditure and the building factors, socio-demographic variables and appliances. In particular, the impact that the different household characteristics have on per–capita family energy expenditures has been examined commented and compared with the previous literature. The analysis is performed by coupling (a) the ordinary least squares method, to determine the relationship between the variables, (b) the variance inflation factor, to check for multicollinearity issues, and (c) the least absolute shrinkage and selection operator, to select suitable variables. Subsequently, a segmentation of the Italian households has been proposed, by using a segmentation-tree approach and the outcomes of the previous analysis. Highlights • The relationships between the energy expenditure and the household characteristics have been analyzed. • Building, socio-demographic and appliances variables are likewise significant predictors. • A segmentation of the Italian households is proposed, by using a segmentation-tree approach. [ABSTRACT FROM AUTHOR]

Abstract To reduce the heat flow between building interior and exterior environments using thermal energy storage without causing problems related to moisture transfer, this paper presents residential building walls enhanced with pipe-encapsulated phase change materials (PCM). Experimental investigations are conducted to identify thermal performance of building walls with pipe-encapsulated PCM in typical summer conditions. A dynamic wall simulator was designed and built to reproduce residential building indoor and outdoor conditions in a laboratory setting. Two pipe sizes, based on diameter, installed in a horizontal arrangement and placed at various wall depths were investigated. The heat transfer through building walls with pipe-encapsulated PCM was evaluated based on peak heat flux reductions and peak heat flux time shift. The peak heat fluxes of the PCM-outfitted walls were reduced by a maximum of 22.5% for what was referred to as "next to wallboard" configuration and 36.5% for "middle depth" configuration, respectively, compared to standard walls. The corresponding daily energy savings were 27.4 W-hr/m2 and 51.2 W-hr/m2. PCM encapsulated in smaller pipes installed in the "middle depth" of the wall cavity is recommended to realize complete solidification and melting for larger peak flux reduction and energy savings. Highlights • Pipes filled with PCM were placed inside walls under various arrangements. • Heat fluxes and temperature variations were monitored and analyzed. • The peak heat fluxes of the PCM-outfitted walls were reduced by a maximum of 36.5%. [ABSTRACT FROM AUTHOR]

Based on a household energy use survey, this paper explores the technical and economic potential of residential energy savings in a Chinese city, Xiamen. The survey adopted a similar questionnaire used by the U.S. EIA's Residential Energy Consumption Survey (RECS), covering the end-uses of cooking, water heating, plug-in appliances, lighting and space cooling. The analysis shows that the technical potential of energy savings in Xiamen's residential buildings is significant, around 20%. Of the technical potential, about two-thirds to four-fifths are cost-effective from a whole society perspective. The cost-effectiveness was evaluated by comparing the Levelised Cost of Conserved Energy (LCOCE) of advanced technical measures with the actual cost of conserved energy. The actual cost of energy is defined by adding the carbon emission cost and hidden government subsidies over the retail prices of energy. About three-quarters of the technical energy-saving potential in Xiamen come from adopting efficient household appliances, therefore, further tightening the energy efficiency standards for key household appliances and promoting wide diffusion of efficient models of appliances by various effective financial incentives are essential for achieving residential energy savings in China's “Hot Summer and Warm Winter” region where Xiamen locates. [ABSTRACT FROM AUTHOR]

Data-driven models can estimate the buildings' energy consumption using machine learning algorithms. This approach works based on the correlation between energy consumption and various inputs such as weather data, occupancy schedules, heating, air conditioning, and physical properties of buildings. Seasonal changes affect buildings' energy use. Hence, the required data for data-driven models (DDMs) during the heating and cooling days could be different. Selecting the most impactful inputs can help to choose the type and quantity of sensors for deployment that improve the model's accuracy and minimize the costs. This paper performs feature selection for heating, cooling, hot water, and ventilation loads in residential buildings under the mixed-humid climate zone. Filter method, wrapper backward elimination, wrapper recursive feature elimination, Lasso regression, linear regression, and Extreme Gradient Boosting (XGBoost) regression are adopted for heating and cooling days, separately. We use twenty-five outputs from a computer model, and the results show that the key features for a DDM are different for heating and cooling days, and XGBoost provides the most accurate forecast. The findings of this paper are useful for selecting proper models, sensors, and inputs for model-predictive control systems during the heating and cooling seasons. •The energy loads in residential buildings can be predicted successfully by data-driven models. •Data-driven models need different inputs to predict the loads during the heating and cooling days. •Features such as air density, represent multiple factors that can reduce the number of inputs. •The eXtreme Gradient Boosting led to a high-performance data-driven model. •Depending on the predictive model, the number of required inputs might vary between 2 and 15. [ABSTRACT FROM AUTHOR]

Controlling Heating, Ventilation and Air Conditioning (HVAC) systems is critical to improving energy efficiency of demand-side. In this paper, a model-free optimal control method based on deep reinforcement learning is proposed to control the heat pump start/stop and room temperature setting in residential buildings. The optimization goal of this method is to obtain the highest comprehensive reward which considering thermal comfort and energy cost. Firstly, the randomness, learning process, thermal comfort and energy consumption of the model-free controller are systematically investigated by a simulation system based on measured data. The results show that randomness has a significant impact on the initial performance and convergence speed of the model-free controller; The model-free controller has a linear accumulation of comprehensive rewards during the learning process, and the slope of the accumulated comprehensive rewards can be used to determine whether the controller converges; The model-free controller coordinates monitoring data, weather forecasts and building thermal inertia to achieve the highest comprehensive reward. Afterwards, the model-free controller was verified in a nearly zero energy residential building in Beijing, China. The results show that model-free controller improves the comprehensive reward by 15.3% compared to rule-based method. • A model-free controller based on deep reinforcement learning is developed. • Problem formulation including the state, action, and reward is carefully designed. • Randomness affects the initial performance and convergence speed of the controller. • The controller improved the comprehensive reward by 15.3% over the baseline. [ABSTRACT FROM AUTHOR]

This article presents a new methodology to disaggregate the energy demand for space heating (SH) and domestic hot water (DHW) production from single hourly smart heat meters installed in Denmark. The new approach is idealized to be easily applied to several building typologies without the necessity of in-depth knowledge regarding the dwellings and their occupants. This paper introduces, tests, and compares several algorithms to separate and estimate the SH and DHW demand. To validate the presented methodology, a dataset of 28 Danish apartments with detailed energy monitoring (separated SH and DHW usage) is used. The comparison shows that the best method to identify energy demand data points corresponding to DHW production events is the so-called "maximum peaks" approach. Furthermore , the best algorithm to estimate the SH and DHW separately is a combination of two methods: the Kalman filter and the Support Vector Regression (SVR). This new methodology outperforms the current Danish compliances typically used to estimate the annual DHW usage in residential buildings. [Display omitted] • The current smart heat meters installed in Denmark only measure total heat usage. • The total heat usage comprises space heating (SH) and domestic hot water (DHW). • The novel method estimates SH and DHW usage from total heat and local weather data. • A labeled dataset of 28 Danish apartments is used to validate the method. • The estimation method outperforms the current Danish DHW compliance calculation. [ABSTRACT FROM AUTHOR]

The green building assessment emphasizes the indoor environment quality (IEQ) by looking into the indoor air quality, lighting quality, acoustics, ventilation and thermal comfort conditions, which can be enhanced by effective initiatives at the early design stage. Designers and engineers usually consider exploiting passive designs to achieve a sustainable goal in building projects. In such background, this paper presents a holistic passive design approach by incorporating a robust sensitivity analysis to an efficient multi-objective optimization process to assess a typical high-rise residential building in hot and humid regions like Hong Kong. EnergyPlus and jEPlus are adopted to conduct modelling experiments with an input parametric matrix generated by the Latin Hypercube Sampling (LHS). All related indoor environment performance indices including the daylight, natural ventilation and thermal comfort are treated as optimization objectives and constraints to fulfil the local green building guidance. The non-dominated sorting genetic algorithm (NSGA-II) is coupled with jEPlus to obtain the Pareto frontier by thoroughly searching the problem space constructed with screened out significant input variables from the sensitivity analysis. Furthermore, different post-optimization analysis methods are applied to decide the final optimum solution, where the total unmet time decreased by 11.2% in contrast with the baseline case. [ABSTRACT FROM AUTHOR]

Monitoring of heat fluxes in residential buildings needs to comply with specific conditions and requirements associated with this sector. Although the heat flux sensors usually come with a standard factory calibration, experimental conditions do not always correspond with those acting during the calibration. Typically, when low conducting materials or well-insulated building walls are subjects of investigation, the anticipated values of measured heat fluxes significantly increase demands for the sensitivity of used probes. Also, the indoor conditions may be significantly distorted due to seasonal regimes of occupation. Therefore, a dedicated calibration experiment might be required to address the sensitivity issue that could devalue the experimental outputs otherwise. In this paper a design of a semi-virtual calibration experiment is presented which can be implemented without interfering with the ongoing measurement. As a result, the sensitivity of the sensors might be enhanced to reflect local experimental conditions at zero additional costs as there is no need for sensor manipulation or acquisition of certified equipment for the calibration. The application of the proposed design is demonstrated using an in-situ heat flow measurement in a residential building, which is accompanied with both data verification and independent validation of the proposed technique. • A smart calibration experiment for heat flux sensors sensitivity enhancement is proposed. • The method is based on creation of semi-virtual environment. • The method allows to calibrate sensor without interfering with ongoing measurement. • The procedure can improve sensor accuracy by more than 80%. [ABSTRACT FROM AUTHOR]

Energy simulations of buildings complement or replace the static calculations used so far and provide detailed answers to questions such as: what is the energy demand for individual purposes in a building and how does it change over the course of a day, a month, a year and also enable a comparison of several design variants and the selection of the optimal one in terms of energy consumption. Therefore, energy simulations of buildings help make decisions to optimise architectural and installation solutions, leading to a reduction in electricity, gas and water demand for the designed building. They indicate to what extent individual factors affect the demand for heating, cooling and electricity. The results of the analyses can be used as a basis for design and system decisions, and also provide interesting feedback to the investor. This paper focuses on the year-round analysis of a three-zone building in TRNSYS. Attention is given to the values of the heat transfer coefficients through the envelope, heating and cooling demand, the effect of heat gains/losses on the energy demand of the building and thermal comfort. The article points out that the correct determination of the energy needs of a building influences the correct choice of renewable energy source and the lowest cost of installation. [ABSTRACT FROM AUTHOR]

In recent years, haze becomes a choking problem in northern China in winter. A HRV (heat recovery ventilator) is an effective device to provide clean indoor environment by supplying fresh outdoor air to residential and commercial buildings, but the HRV system must be combined with an air filter to remove the fine particular matter of the outdoor air. There is limited information regarding this integrated system. This paper investigates the influences of an air filter on the performances of the HRV system in three modes by experiment and the application potential is also reported for this integrated system in five climate zones of China. The experimental results show that the integrated system can meet the required threshold value of 75 μg/m 3 , but the installation of an air filter reduces the air flow rate and increases the power of the fan. Compared to the HRV system, the sensible heat efficiencies of the integrated system are increased; and the total sensible heat transfer capacities are slightly decreased in the three modes. The case study shows that in Harbin and Beijing, the integrated system can recover more energy than that in other three cities in winter. [ABSTRACT FROM AUTHOR]

The ‘prebound effect’ characterises how average heating energy consumption in older homes is consistently lower than these buildings' calculated energy ratings, and helps explain why energy savings from thermal upgrades are often lower than anticipated. This paper explores the conceptual links between prebound and rebound effects and aims to quantify these behavioural effects. It applies the resulting mathematical model to empirical examples of actual and calculated energy consumption at scales of individual dwelling and national housing stock. These show that the rebound effect, as defined in econometrics literature, can only indicate proportionate reductions in energy consumption and can mask high levels of absolute consumption. The prebound effect, however, can identify under- and over-consumption regardless of rebound effects. A combination of high prebound effect and low income suggests fuel poverty, and the rebound effect here is less relevant regarding total energy consumption. Policymakers should identify housing with high prebound effects in order to eliminate fuel poverty, and be aware of inaccuracies in calculating payback time where economic viability of retrofits is mandated. Further research is needed to understand motivations and practices in households that have high prebound effects and to identify specific priority groups for thermal retrofit policy. [ABSTRACT FROM AUTHOR]

Both new and existing buildings need to be adapted to climate change, in order to keep providing a comfortable and healthy indoor climate. Preferably, the adaptation measures applied at the building level scale do not require additional energy (i.e. passive measures). Previous studies showed that passive climate change adaptation measures can have a positive effect on thermal comfort in summer and its shoulder seasons in non-air-conditioned residential buildings. In this paper, the effect of these passive climate adaptation measures – applied at building component level – on the cooling and heating energy demand of a terraced house is analyzed using building energy simulations. It is shown that for this particular case the required cooling energy can be limited to a large extent (59–74%) when external solar shading or additional natural ventilation is applied. In addition, it is shown that for a well-insulated terraced house the energy cost for heating is not strongly affected by the application of passive climate change adaptation measures. [ABSTRACT FROM AUTHOR]

The recast of EU (European Union) Directive on EPBD (Energy Performance of Buildings) requires nZEBs (nearly zero energy buildings) as the building target from 2018 onwards and the establishment of cost-optimal levels of minimum energy performance requirements in buildings. This paper presents the results of the application of a methodology to identify cost-optimal levels in new residential buildings located in a warm climate. Mono-residential buildings have been considered as virtual reference buildings in this study. Different energy efficiency measures have been selected for the envelope and the systems. A combination of technical variants has been then applied to the reference case in order to obtain several configurations to be compared in terms of primary energy consumption and global costs. The cost-optimal solution is identified assessing technical features and energy performance. Standard and high efficiency buildings are analysed to show how the selected configuration allows a decrease of primary energy consumption and CO 2 emissions at the lowest cost. Results are useful for comparison with other climates and building types. They also show the feasibility of the methodology to comply with EU requirements and to support the choice of economically efficient nZEBs solutions at the design stage. [ABSTRACT FROM AUTHOR]

Venetian blinds are widely used to improve the performance of window systems, while their energy-saving potentials vary with different materials. This paper aims to undertake a standardized evaluation of the annual energy-saving potential of venetian blinds in residential buildings, and identify the most influential parameters for design optimization. To achieve this goal, nine parameters were chosen to establish venetian blinds models in WINDOW. Then, 9072 shading models were coupled with building models for annual energy consumption simulation in EnergyPlus, where the shading schedule was derived from questionnaires. Further, an experiment was conducted to verify the reliability of the joint simulation between WINDOW and EnergyPlus. Finally, annual energy performance (AEP) was adopted for a standardized energy efficiency evaluation of various venetian blinds products, and the sensitivity analysis of AEP to various parameters was conducted to identify the key parameters. The results indicated that by adjusting the key parameters of the venetian blinds, cooling energy saving can reach 44% and the heating energy saving can be up to 65%. The significant energy saving potential of venetian blinds indicates the prospect and importance of their use in residential buildings. • A standardized evaluation method for venetian blinds was proposed. • The accuracy of joint-simulation of WINDOW and EnergyPlus was validated. • Conductivity of venetian blinds has few impacts on their energy performance. • Solar reflectance and transmittance are two key parameters for energy savings. • 65% heating energy consumptions can be saved by adjusting material parameters. [ABSTRACT FROM AUTHOR]

Local and state governments find it challenging to adopt aggressive residential building codes that require energy-efficiency upgrades beyond those with a reasonable payback. Thus, economic considerations inhibit the progress towards a more energy-efficient housing stock and often account for direct utility savings. A widely discussed solution is to look beyond energy costs and consider other impacts of energy-saving strategies that affect their financial attractiveness. In this paper, we examine the case of a public housing project in Phoenix, AZ, using several tools to calculate different economic, environmental, and health metrics associated with the three levels of energy efficiency. Our results show that while the payback calculated from direct energy costs may not be attractive, we should consider other savings. We demonstrate that avoided health and climate costs could total around 40% of the direct utility savings. In addition, we quantify how energy-saving strategies can cool the neighborhood, make buildings more resilient to heat, improve indoor air quality, and reduce the transmission of airborne disease. These benefits could be translated to avoid costs in the future. • We demonstrate looking beyond utility cost in assessing building energy efficiency. • Avoided health/climate costs are in the order of magnitude of direct utility savings. • We show that building efficiency can increase the hours comfortable for window use. • Thus reducing the risk of indoor airborne disease transmission per Wells-Riley method. • Efficiency reduces indoor & outdoor (shown via an urban canyon model) heat exposure. [ABSTRACT FROM AUTHOR]

The analysis presented in this paper evaluates the impact of both short-term and long-term stay home living on the energy consumption of Saudi residential building stock. The analysis combines monitored data obtained for a sample of housing units as well as the results from a validated Saudi residential building stock model. In particular, a bottom-up building stock modeling analysis approach is used to estimate the energy impact of the 2020 stay home order imposed due to COVID-19 in most of Saudi Arabia's regions between March 15 and June 15, 2020. Moreover, the potential influences of long-term stay home patterns on the cost-benefits of energy retrofit programs are investigated targeting Saudi existing housing stocks. The analysis results indicate that when normalized to weather, the 2020 lockdown has resulted in a 16 % increase in electricity consumption compared to the 2019 level specific to the entire Saudi housing stock. Most of this increase is associated with the higher energy end-use of lighting, appliances, and air conditioning associated with increased occupancy levels during daytime hours. For a transition to long-term stay home living, the results of the analysis show that the energy consumption of the Saudi housing stock is estimated to increase by 13.5 % compared to the current occupancy pattern. Moreover, the analysis indicates that the cost-benefits for energy efficiency retrofits are enhanced with long-term stay home living. • Households on average increased their energy demand during the COVID lockdown period. • Lighting and appliances are the main contributors to the energy consumption increase. • Energy consumption would increase by 13.5 % due to a yearlong home living pattern. • Retrofitting lighting and appliances are cost-effective options to curb energy demand. • Retrofitting AC units has beneficial effects on demand regardless of the living style. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach in order to accurately calculate the savings due to heat metering. The approach is based on a detailed dynamic simulation of building-plant systems. The building is geometrically modelled in Google Sketchup and linked to the TRNSYS environment, including an extremely detailed model for the simulation of building thermo-physical behavior. All the models are validated using the data provided by the occupants. The model allows one to evaluate the yearly energy demand, energy supplied by radiators, heat gains, etc. A specific case study is developed for a residential building located in Naples (South Italy). The developed model is used to calculate the building energy demand for 3 scenarios: centralized heating system not equipped with heat metering; centralized heating system with thermostatic valves and not equipped with heat metering; centralized heating system with thermostatic valves and equipped with heat metering. Results show that in case of centralized heating systems equipped with thermostatic valves and heat metering devices, thermal energy savings up to 64% can be reached mainly when the system operates for many hours per day, leading to discounted pay back periods lower than 4 years. • Dynamic analysis of energy performance of building adopting thermostatic valves. • Dynamical analysis of heat metering devices for a residential building. • Dynamic analysis of building energy performance. • Thermostatic valves and heat metering devices are able to reduce by 64% building thermal energy demand. [ABSTRACT FROM AUTHOR]

In this paper, the energy performance of an integrated adaptive envelope system (AES) is evaluated when applied to detached houses in four US climates. Three main technologies are part of the AES including cool roofs, movable PV-integrated shading devices (MPVISDs), and switchable insulation systems (SISs). For this study, the AES is operated to minimize annual heating and cooling energy uses. The analysis results clearly indicate that the integrated AES have high potential for cooling energy savings for residential buildings. Specifically, MPVISDs offer the highest contribution followed by attic and walls integrated SISs. Overall, the integrated AES allows on-site electricity generation and offers savings between 234 kWh/yr and 949 kWh/yr in cooling energy depending on the US climate. The deployment of AES alone allows US homes to almost reach net-zero energy designs especially in mild and hot climates. • A novel integrated adaptive envelope system (AES) is evaluated for US homes. • AES provides both energy efficiency and on-site electricity generation benefits. • AES can save up to 33% in annual cooling energy use for US homes. • The deployment of AES alone can achieve net-zero energy status for most US homes. [ABSTRACT FROM AUTHOR]

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. In this paper, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k -means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics. • A new data analytics framework for detecting changes in occupant behavior. • A pathway towards an analytics feedback loop from behavior impact to incentive design. • A demonstration of the framework on a realistic HVAC use-case with 97% accuracy. • Examples of simpler machine learning approaches that fail to detect entire categories of behavior changes. [ABSTRACT FROM AUTHOR]

This paper aims to visualize how the planned electrification of heat demand together with the utilization of energy flexibility in buildings will influence the performance of local electricity network. Thereby, the work contributes to the research on smart energy system in the residential sector. The flexibility service is provided by two demand-side-management strategies a) activation of the thermal mass to modulate load profile of a heat pump, b) control of household appliances' starting times. Three configurations of load mix in the local electricity network are investigated: a) domination of non-renovated houses, b) with equal share of high and low heat demand houses, c) domination of energy efficient houses. The model is soft-coupled and anchored in existing low voltage (LV) network and existing residential buildings. The energy flexible buildings enhance the LV network performance, by decreasing the afternoon peaks, without compromising the occupants' thermal comfort. The highest impact is for the LV network dominated by energy efficient houses. There are also new challenges, namely the newly created peak loads and transformer overloading during night time. It is a consequence of uniform price signal sent to all flexible customers and electrification of heating demand without parallel improvement of energy performance. • Soft-coupled model of LV network anchored in existing elements is developed. • Introduction of HPs and utilization energy flexibility in buildings is studied. • Without scheduling the activation of flexible customers new peak loads occur. • New operational challenges are critical for LV networks with inefficient buildings. • Installation/replacement of HPs must be parallel with efficiently improvements. [ABSTRACT FROM AUTHOR]

It was found from monitored data from eight dwellings in a case study building in Quebec City (Canada) that there are clear differences in the window opening behavior between different households. This paper aims to develop from data a probabilistic window opening model that accounts for occupant behavior. Logit regression is employed to predict the state (opened/closed) of windows according to indoor and outdoor temperatures environmental and temporal parameters. To replicate the diversity of behavior, normal distribution functions applied to the logit regression coefficients are used so that simulated occupants respond differently to environmental stimuli. It was found that the model offers good prediction for the monitoring by only using the outdoor and indoor temperatures as predictors. The proposed methodology was tested by simulating 10,000 times a full validation year of the case study building and comparing the results with measured data. The agreement was good. The model overestimated slightly the total frequency of window opening in the dwellings and the number of window changes-of-state. A vast range of window opening behavior was generated by the model, showing its ability to reproduce both the aggregated window opening behavior and the diversity of behaviors of the case study building. • Strong variation of window opening behaviors are observed among 8 monitored dwellings. • Windows are opened on average 10% in winter and 36% in summer in these dwellings. • Logit regression with random weights can reproduce the observed variability. • Only indoor and outdoor temperatures are required as predictors in the model. • Number of events and their duration are the same between the model and dwellings. [ABSTRACT FROM AUTHOR]

This paper describes the development of a residential building stock model for the Kingdom of Saudi Arabia using an engineering bottom-up approach. The model is suitable for evaluating the impact of any energy efficiency or demand-side management program targeted for the residential building sector. The model accounts for the make-up and the characteristics of the existing Saudi housing stock using 54 prototypes representing KSA residential buildings including their type, vintage, and location. The model relies on a detailed building simulation tool to predict hourly energy consumption. The model predictions have been verified against actual reported data for energy consumption levels associated to main four Saudi regions. As an application of the building stock model, the benefits of a series of energy retrofit measures and programs are evaluated for both households and Saudi government. The analysis results clearly indicate that large-scale implementation of retrofit programs for existing KSA housing stock is cost-effective and has a wide range of economic, environmental, and social benefits. In particular, a full and targeted implementation of optimal retrofit programs can reduce the annual electricity consumption of the KSA residential sector by 50% in addition to similar decreases in carbon emissions and electricity generation capacities. The analysis results clearly indicate that for the retrofit program to be effective, the energy efficiency measures need to be tailored to the housing type, vintage, and location. Optimal sets of retrofit measures have been identified for the various categories of KSA housing stock as part of the based analysis carried out in this study. • A building stock model t represent energy performance of KSA existing housing units is developed. • The building stock model has been applied to evaluate the benefits of energy retrofit programs. • Over half of energy consumption can be saved through targeted energy retrofit programs of KSA housing stock. [ABSTRACT FROM AUTHOR]