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This paper presents the results of the validation of an experimental method for studying the thermal benefits of green façades. The main aim is to evaluate the impact of plants occultation on energy performance of a vertical wall in the temperate climate of the city of Lille in northern France. An experimental platform consisting of three identical prototypes (three boxes as thermal labs) has been designed and instrumented. Each prototype has been highly insulated from each face except from the vertical southern wall which was realized with a poor thermal conductivity material. The following measurements were taken: ambient temperature (outside and inside thermal labs), external and internal surface temperatures of southern walls, heat fluxes transferred through south verticals walls (reference and vegetalized walls), solar incident radiation and wind velocity variation under instantaneous real climatic conditions. This enabled the analysis of the variation of energy balance according to the presence or not of a shading effect on the south vertical walls as well as their coverage rate. Several tests were performed on the three experimental thermal labs by imposing a leaf coverage rate which differs during each measurement. The results have shown that masking reduces the outside temperature and enhances the interior thermal comfort especially during summer. [ABSTRACT FROM AUTHOR]

Material characteristics of Ground Heat Exchangers (GHE) greatly influence the heat transfer efficiency of Ground Source Heat Pump (GSHP) system, i.e. polyethylene (PE) and steel for the current study. Most of the relevant studies applied numerical simulation or small-scale experiments, which either simplified or neglected the effects of different GHE design on the energy consumption during the operation process of GSHP system. This paper aimed to investigate the seasonal performance (winter and summer) for both PE and steel GSHP systems by using the methods of full-scale experiment monitoring (buried tubes with the depth of 100 m) and life cycle assessment (LCA), which includes energy benefits, economic and environmental impacts. Heat exchange performance was evaluated by heat exchange per tube depth (Q L ) and real-time monitoring of system power consumption. Energy benefits, economic and environmental impacts were further indicated through four indices including coefficient of performance (COP), energy payback time (EPT), cost payback time (CPT) and CO 2 reduction ratio (CRR). In general, we found the seasonal performance of steel system was superior to PE one in the current study. We also noticed that both PE and steel systems showed better performance in summer than winter. For both winter and summer, the annual averaging inlet and outlet temperature difference of steel tubes was 1.4–1.7 °C higher than PE one and the Q L of steel tubes increased by 70%. The energy consumption and cost of steel system were decreased by 43.6% and 24.6%, respectively in the whole life cycle compared to PE one. The EPT, CPT and the CRR of steel system (compared to PE system) were calculated as 0.24 year, 1.83 years and 0.45 accordingly. This work further shows future promise of steel GHE application for GSHP systems. [ABSTRACT FROM AUTHOR]

Accurately predicting energy usage in buildings is of great importance in various efforts on improving building energy efficiencies such as fault detection and diagnostics, building-grid interactions, and building commissioning. Data-driven approach and first-principle approach are two commonly used methods in developing models for predicting building energy use. In this paper, several data-driven methods including multiple linear regression, adaptive linear filter algorithms (least mean square (LMS), normalized least mean square (nLMS), and recursive least square (RLS)), and Gaussian mixture model regression (GMMR) are employed to predict hourly energy usages in two buildings. One building is a synthetic large-size office building from DOE reference building models. The hourly building energy consumption was predicted using the energy simulation model for one year under Chicago climate. The other building is an existing office building located in Des Moines, Iowa. The actual hourly building energy consumption of the existing building was obtained through building submeters. The accuracies of these data-driven models for predicting energy usages of the two buildings are compared. The GMMR models outperform the adaptive filter methods in this study. Both the GMMR and adaptive filter methods meet the model calibration criteria defined by the ASHRAE Guideline 14. [ABSTRACT FROM AUTHOR]

This paper presents test methods and results of assessment data necessary for determination of building energy consumption, based on measurements in an occupied typical single-family house in Poland. Comprehensive on-site measurements included the building envelope, heating system, domestic hot water system, ventilation system with heat recovery and indoor environment quality. A new wireless measurement system was used. The results obtained from the thermal diagnostics were used to analyze the energy consumption of the house in four different successive building modernization stages. The steps undertaken included improvement of wall and window insulation, installation of mechanical ventilation with heat recovery, improvement of house airtightness and replacement of the boiler and the hot water storage vessel with more efficient ones. Such steps are typical activities undertaken without a prior energy analysis by owners of single-family houses. Before thermal renovation the indicator of non-renewable primary energy was 243 kWh/(m 2 y) and after renovation this decreased to 147 kWh/(m 2 y). Proper selection of thermo-modernization measures would allow to reach the lower value of this indicator. However, it still would be higher than the required for newly built single-family houses in Poland. The execution of thermal diagnostics both before and after thermal renovation of the house enable the correct performance of that process and the achievement of greater energy savings. [ABSTRACT FROM AUTHOR]

Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form. [ABSTRACT FROM AUTHOR]

The increased demand of energy in domestic applications necessitates the development of innovative engineering solutions in building heating, ventilating, and air conditioning (HVAC) systems. As the largest energy intensive sector is domestic buildings, more focus is currently directed to reduce air conditioning energy consumption. Double-pipe heat exchangers are considered one of the practical solutions in today’s HVAC industry. Nevertheless, a few studies focus on using double-pipe heat exchangers in air conditioning applications. This paper experimentally investigates the usage of double-pipe condenser and evaporator in an air conditioning system serving a 45 m 3 balanced calorimeter of 2.24 kW heat load. Deionized water (DIW) was used as the secondary heat transfer working fluid for both the evaporator and condenser units, and R-22 was used as the AC system refrigerant. Experimental results of the double-pipe heat evaporator/condenser setup showed a promising reduction in the compressor work and an increase in the system coefficient of performance (COP). The collected data showed that the system efficiency depends more on the evaporator DIW flowrate than on the condenser DIW flowrate. By increasing the DIW flowrate in the evaporator, the compressor work was shown to decrease, while the COP was shown to increase. In comparison with a standard rated air conditioning unit, using a double-pipe evaporator and condenser units with the maximum DIW flowrates resulted in a decrease of about 53% in the compressor work and a similar percentage of increase in the system COP. [ABSTRACT FROM AUTHOR]

The purpose of air-conditioning systems is to provide a suitable indoor environment with respect to temperature, humidity and fresh air. Liquid desiccant dehumidification constitutes an effective method for extracting moisture from humid air with less energy consumption. Current studies mainly focus on dehumidification system under certain heat and mass transfer ability (number of mass transfer units NTU m ). This paper will investigate NTU m of counter-flow packing tower and its influence on system performance. An experiment including a counter-flow packing tower was conducted. When the height of tower was 0.7 m, the volumetric mass transfer coefficient was between 1–4 kg/(m 3 ·s) and NTU m presented a range between 0.9–1.4, when air flow rate changes from 1 kg/(m 2 s) to 2.5 kg/(m 2 s). When the height changed to 2.1 m, the NTU m of the tower presented a variation from 3.3 to 4.3. Furthermore, a simulation model is developed, which is validated by experimental data and past research results. The influence of NTU m on total circulation flow path is mainly caused by heat and cold offset and an NTU m value approximately of 4 constitutes a suitable value of increasing heat and mass transfer ability. Comparing three typical flow paths, when NTU m is lower than 3.5, inter-stage circulation presents improved performance in system COP. When NTU m is large, total circulation is efficient. The system COP cross points of flow paths are caused by heat and cold offset and concentration difference of the solution circulation between the dehumidifier and the regenerator, which can be quantified by loss coefficient ε and χ respectively. The heat and mass transfer ability of the system should be considered in the designing process of flow path configuration to obtain an efficient performance for different range of NTU m . [ABSTRACT FROM AUTHOR]

A new photovoltaic loop heat pipe/solar assisted heat pump (PV-LHP/SAHP) water heating system was introduced in this paper. With the combination of solar energy, LHP and heat pump technology, the composite system could operate in the PV-LHP mode, the solar/air source heat pump (SASHP) mode and the air source heat pump (ASHP) mode. The mathematical model of the system was constructed to simulate operating performances in typical working conditions and in long-term run. The influence of main structure and operating parameters were also analyzed. To validate the accuracy of the built model, an outdoor test rig of the PV-LHP mode was established in Qinhuangdao City. Investigation results showed that the overall photothermal efficiency of the system is comparable to traditional photovoltaic/thermal (PV/T) water heating systems. The monthly average power consumption per liter hot water is 0.009 kW h/L, and the monthly average COP of heat pump modes is about 3.10. Besides, the annual solar heating ratio of the system is up to 57.8%. Compared with the traditional ASHP system, the life cycle cost of the PV-LHP/SAHP system could be reduced by 29.6%. [ABSTRACT FROM AUTHOR]

Addressing the large energy consumption of hotels requires an understanding of the factors that drive this consumption. This enquiry is crucial for South Africa which has experienced significant strain in meeting its domestic energy demand. This has occurred alongside increases in international tourists, adding to the pressure on already strained resources. This paper tests hypotheses on drivers of energy consumption in hotels using a novel panel dataset which presents daily consumption data for 22 hotels across South Africa. Findings from various specifications of the Dynamic Random Effects Model suggest that the number of rooms in a hotel, the services and facilities offered, and climatic conditions are strong drivers of consumption. While the role of occupancy could not be robustly ascertained due to severe data limitations, findings indicate that price regulation plays a significant role in curtailing electricity consumption, even in high-end hotels. Results further suggest that in the design of guidelines for energy efficiency in South African hotels, the energy consumption of the facilities and services offered should be the first point of call, and the strenuous impact of extreme weather conditions on energy consumption needs to be factored in at the phase of building design and construction. [ABSTRACT FROM AUTHOR]

With the increased complexity of air conditioning systems, the computational load and complexity of real-time optimization appear to be challenging for applications. To overcome the challenge, multiplexed optimization has been proposed in which multiple set-points are sequentially optimized and updated one by one. In such a way, it can achieve equivalently-good energy performance but with exponentially reduced computational load if compared with those traditional methods that optimize all set-points simultaneously. In fact, the time instant for each set-point optimization and the set-point optimization sequence have impacts on the overall performance of the multiplexed optimization. Considering such impacts, this paper proposes a new multiplexed optimization method for enhanced performance. In the proposed method, optimization is activated as the system energy efficiency is low. The set-point optimization sequence is selected according to their impacts on the system energy performance. The one with the most energy saving potential will be firstly optimized. The case study results demonstrate the improved performance of the proposed method in terms of energy, computation load and system stability when compared with the conventional multiplexed optimization. The proposed method can be applied in complex air conditioning systems for energy saving, computation load reduction and system stability improvement. [ABSTRACT FROM AUTHOR]

In the context of environmental policy, the EU has launched a series of initiatives aimed at increasing the use of energy efficiency, as it has pledged to reduce energy consumption by 20%, compared with projected levels of growth of CO 2 emissions into the atmosphere by 2020. In Greece CO 2 emission levels in the atmosphere have risen significantly over the past two decades [1] . For the year 2011, CO 2 emissions per person in Greece correspond to 7.56 t. According to the data, this increase in emissions is reflected to a 151.2% above from the levels of 1980 and a 756% increase from 1960 levels. The building sector consumes the largest amount of energy in Greece, therefore constitutes the most important source of CO 2 emissions. The energy upgrade of the building sector produces multiple benefits such as reduced energy consumption, which is consistent with the reduction of air pollution. Additionally, there is a significant improvement at the interior comfort conditions of the building, which promotes productivity and occupant health. Moreover, because of the large number of educational buildings in the country, the energy consumption of them present a significant amount of the country's total energy consumption and simultaneously has the effect of increasing the costs paid by the state budget for the operation and maintenance of public buildings. The investigation of alternative methods to reduce energy consumption in educational buildings is an important approach for sustainability and economic development of the country over time. The purpose of this paper is to study and evaluate the energy saving methods of a university building in Mediterranean climate with significant energy consumption. Additionally, through Building Information Modeling (BIM) and Computational Fluid Dynamics (CFD) software, studies considering the contribution of passive heating and cooling techniques were conducted, in order to minimize energy consumption in pursuit of desirable interior thermal comfort conditions. [ABSTRACT FROM AUTHOR]

Thermally activated building system (TABS) is a hydronic cooling system, in which cold water is circulated through pipes embedded in the building structure to remove the heat from the indoor space. This paper proposes a hybrid passive cooling system, in which TABS is integrated with a cooling tower. The performance of this integrated system greatly depends on the climatic conditions of the geographical area. Hence, the feasibility of the proposed system to provide thermal comfort in twelve Indian cities spread over five different climatic regions is investigated using COMSOL Multiphysics. Different cooling scenarios like Roof and Floor cooled TABS (RF) and All Surfaces cooled TABS (AS) are compared. The proposed system performs well in arid and semi-arid regions, and the RF scenario is sufficient to achieve thermal comfort. The system is able to reduce the indoor operative temperature by 14 °C for the arid climatic conditions of Jaipur. In the other three regions, i.e., humid subtropical, tropical wet and dry and tropical wet regions, the performance of the proposed system is relatively low and the AS scenario is required to achieve thermal comfort. [ABSTRACT FROM AUTHOR]

Airtightness testing is widely undertaken to assess the as-built performance of dwellings, in support of achieving energy and ventilation strategies. Mandatory schemes operate in some countries, such as the UK, to ensure that dwellings are built in accordance with their design air permeability. However, testing is only useful if the results give a true picture of the airtightness of the building. Previous literature has investigated factors which could influence airtightness test results but has not questioned data quality, despite the pressure on builders to achieve design targets. This paper presents air permeability results from the largest UK dataset, comprising 144,024 dwellings tested under the Air Tightness Testing and Measurement Association (ATTMA) scheme. The data show an unexpected distribution of test results with narrow peaks just within test targets. Such results were not expected theoretically but do reflect findings in other fields where performance-based targets are in place. Such a close match between design and tested airtightness may be achieved by remedial works taking place during the test rather than afterwards. Recommendations are made with respect to quality assurance systems, design guidance and on-site sealing practices to increase the likelihood of long-term airtight buildings being constructed first time. [ABSTRACT FROM AUTHOR]

This paper presents the results of a long-term, detailed electricity end-use analysis of hotel guest rooms in order to provide a better understanding of the electricity use patterns in guest rooms and their explanatory factors. To accomplish this, a one-year of sub-hourly electricity measurements for an entire floor and major electricity end uses of fourteen guest rooms in a case-study hotel in Washington, D.C. were analyzed along with coincident weather and occupancy data collection. A regression analysis was performed on the monthly and daily end-use electricity data to identify key variables affecting different electricity end uses of the guest rooms. As a result, it was found that a regression model with a single temperature variable did not fully describe the electricity use behaviors of hotel guest rooms. The addition of the occupancy variable significantly improved the model fit for both monthly and daily total floor electricity use and was statistically significant. A review of the electricity end uses showed strong correlations between outdoor temperature and the electricity end uses for space conditioning, especially the DOAS unit and the VRF outdoor unit. However, for other end uses such as plug loads, lighting, and the VRF indoor units, the observed correlations to outdoor temperature were not significant but were moderately correlated to occupancy. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to suggest a virtual water flowmeter applicable in hydraulic systems with low-cost sensors. Practical procedures for realizing virtual water flowmeters for constant flow pumps (i.e., Cases 1 and 2) and variable flow pumps (i.e., Cases 3 and 4) are proposed in this paper. In Case 1, for measuring the water flow rate through a constant flow pump, the pump head was measured and the water flow rate was estimated using the pump characteristic curve obtained from the manufacturer or by deriving an empirical model based on the field measurement data. In Case 2, the water flow rate through a valve with a constant flow pump was estimated by measuring the pressure head between the valve and the valve opening position. The valve characteristic curve can be obtained by deriving an empirical model based on the initial field test results. The water flow rate through the variable flow pump was calculated, using the pump affinity laws and an empirical model, by measuring the frequency (i.e., Case 3) or input power (i.e., Case 4). The initial setup and test procedures for virtual flowmeters are also proposed in this research for practical use in the field. To verify the proposed procedures, experiments were conducted using constant and variable flow pumps in a pilot system. It was found that the virtual water flowmeters showed 2.9% to 6.7% of root mean square error from Case 1 to Case 4, compared with an ultrasonic water flowmeter, through long-term experimental verification. [ABSTRACT FROM AUTHOR]

District heating system brings with it the problem of achieving a fair billing service for heating of apartment buildings in the case of uneven temperature in them. The problem is most pronounced when the individual apartments are completely excluded from the heating system of the building. The model of an existing building with 24 apartments was simulated in the software EnergyPlus, for different characteristics of the thermal envelope and different conditions of use, when the apartment with the lowest energy consumption is excluded from the heating system. It was simulated the exclusion of this apartment in which people are present and the lights and electrical appliances are turned on and in which there is no presence of people and electricity consumption (the apartment is empty). As a measure of energy flow between unheated and adjacent apartments, the parameter “stolen” energy is defined. The results of the conducted simulations show that the stolen energy is reduced by the exterior and interior insulation of the building. In this paper, data on energy flow when there is interior insulation in apartments, in particular according to the current regulations in Serbia, are especially analyzed. The results show that, depending on the case and scenario, the stolen energy ranges from 27.23 to 7.97 kWh/m 2 . The insulation of interior walls of the building can significantly mitigate the problem of energy flow due to temperature differences between apartments. [ABSTRACT FROM AUTHOR]

In the case of highly efficient buildings the solar and internal gains have a higher impact on the energy balance than on classical constructions, with lower insulation. In this context, a model was developed which considers the three dimensional heat transfers through the walls. It simulates the transient behavior of rooms with the use of a refined spatial and temporal discretization and considers the projection of solar radiation through a window onto interior walls, referred to as sun patch. Validation of the model was carried out using experimental data from a low energy cell operating in a natural climate. Shorter sampling steps seem necessary to consider accurately the fluctuations of the weather data and the short dynamics of the systems such as the regulated heaters. In this paper, simulations with weather data at different time steps (1 min, 10 min and 1 h) are going to be analyzed. The impact of the contribution regarding the adaptive and variable time step of the differential equation solver will also be shown. Finally, the impact of the different time steps on the accuracy of the low energy cell’s temperatures and heating loads will be discussed. [ABSTRACT FROM AUTHOR]

This paper aims to analyse the changes that the new Hellenic Regulation on the Energy Performance of Buildings (REPB) has brought to the Hellenic residential buildings, which is in line with the European Directive 2010/31/EC. These changes refer to construction techniques, construction costs and energy performance. Our assessment is based on the numerical analysis of two typical residential buildings, a single and a multi-one. Both buildings were studied according to the requirements set before and after the application of the REPB for all (four in total) climate zones of Greece. The cost for implementing the two regulations was evaluated, as was also the energy consumption cost. Based on these costs we estimated the operating costs for heating, cooling and domestic hot water of the two buildings of this study. The implementation of the REPB significantly reduced the required heating loads for space heating in all climate zones by 37%–48% for both building types; this is mainly attributed to the improvement of the thermal properties of the building envelope and the improved energy performance of the electromechanical systems. The required cooling load was low and the implementation of the REPB did not change it significantly. Significant energy savings, in the range of 51%–58% were also achieved by installing flat plate solar collectors for Domestic Hot Water heating. [ABSTRACT FROM AUTHOR]

In this paper, the performance of direct evaporative air cooling (DEC) was investigated with chilled water. This may increase the performance of DEC, especially in humid weather, as the air wet-bulb temperature reduces during the cooling process. The efficiency of DEC was determined by a performance factor instead of the saturation efficiency. A prediction model based on heat and mass balance between air and water was developed. For this model, the condition line of cooling process was divided into segments for which the enthalpy of air was gradually decreased. The influence of dry-bulb temperature of entering air, relative humidity, water subcooling and air-water mass flow rate ratio on the performance factor was taken into account. Experiments were performed for the verification of the predicted data. It could be shown that the mass flow rate ratio is the most effective parameter on the performance of DEC. The performance of DEC can be significantly increased by chilling water and by decreasing the mass flow rate ratio even when the air humidity is relatively high. This may enable the potential application of DEC in very humid weather areas. The mean absolute percentage deviation between experimental and predicted performance factor was 15.1%. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of lighting control systems (LCSs) in 57 individual office rooms of an educational building located in Lund, Sweden. The study uses simulations based on actual occupancy data. The simulations, performed with Daysim via the Honeybee interface, focus on the portion of standby energy use on total lighting energy use considering different combinations of LCSs and lighting power density (LPD). The results show that standby energy use accounts for about 30% of the total lighting energy use, but it can raise up to 55% in extreme cases. The portion of standby energy use increases with lower occupancy rates and lower LPD. As a conclusion, a complete switch-off of systems during unoccupied hours is fundamental to preserve savings from LCSs. In addition, the article argues that, when standby energy use cannot be minimized or eliminated, daylight design and very efficient light sources may reduce the need for complex LCSs in individual office rooms or the like. [ABSTRACT FROM AUTHOR]

In the last years, multi-objective optimization techniques became into one of the main challenges of the building energy efficiency area. The objective of this paper is to develop and validate a computational code for multi-objective building performance optimization by linking an evolutionary algorithm and a building simulation software in a powerful cluster. A sophisticated version of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was implemented in Python code to determine the optimal building design, which allows working with categorical and discrete variables, and the objectives were evaluated using the building energy simulation software EnergyPlus. NSGA-II was implemented to run in a high-performance cluster for the parallel computing of the fitness of each population (set of possible designs). In this work, the strengths of the proposed method were demonstrated by its application to the optimal design of a typical single-family house, located in the Argentine Littoral region. This house has some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). The most influential design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size, type, and windows shading of this house among others were studied in two complex cases of 10 8 and 10 16 possibilities to obtain the best trade-off (Pareto front) between heating and cooling performance. Finally, a decision-making method was applied to select one configuration of the Pareto front. Optimal simulation results for the study cases indicated that is possible to improve up to 95% the thermal comfort in naturally ventilated rooms and up to 82% energy performance in air-conditioned rooms of the building with respect to the original configuration by using a design that takes simultaneous advantage of passive strategies like thermal inertia and natural ventilation. The methodology was proved to give a robust and powerful tool to design efficient dwellings reducing the optimization time from almost 12 days to 4.4 h. [ABSTRACT FROM AUTHOR]

This paper presents a study of the thermal performance of a single-family house located in a coastal city in northern Spain that was built according to Spanish standards during the 1990s. The study estimates an annual energy demand of around 95 kWh/m 2 and a thermal power rating of the heating system of around 16 kW. The potential energy savings that would be obtained by implementing different retrofitting measures frequently used nowadays are also quantified: installing a ventilation system with heat recovery, improving the insulation of opaque construction elements, replacing the existing windows with high-thermal performance windows, and reducing air infiltration rates. These integrated actions on the house would reduce the annual energy demand below the requirements of both the Technical Building Code (24.24 kWh/m 2 year for this case of study), which is the regulation currently in force in Spain, and the well-known Passivhaus standard (15 kWh/m 2 year), which is one of the most widespread building standards in Europe. Compliance with these requirements means that the annual energy demand and the size of the heating system would respectively be potentially reduced by 81% and 57% of the present values. [ABSTRACT FROM AUTHOR]

The recent global financial and economic crisis is responsible for the significant decrease of heating energy consumption, especially in low income population that mostly live in non-thermally-performing houses. The decrease of the residents’ income results in lower internal temperatures, hence lower thermal levels and lower indoor environmental quality, which are responsible for health problems and inadequate quality of life for the residents. This paper deals with the problem of energy poverty. The aim is to investigate whether the economic crisis, which affects the heating energy consumption and the indoor thermal quality, has consequences on the social and health problems in low income families of the Republic of Cyprus, during the winter season. To this aim, an extensive continuous monitoring study in terms of indoor air temperatures was carried out, for one year since December 2013 in 38 low income households in Limassol and Paphos, two of the main districts of the island after the capital of Nicosia. Additionally, questionnaires regarding energy, environmental, social, and health data were designed and collected from each household. Consequently, a cluster analysis, based on the monitoring data related to the first two months of 2014 and the survey’s responses, was performed. Poor households of Cyprus were found to live in low indoor thermal quality, i.e. their average indoor air temperatures (ranging from 16 to 19°C) are lower than the accepted limits of the comfort zone for the island (18–21°C in winter). However, the thermal comfort and health conditions were found between the “acceptable” and “good” levels for each cluster. Additionally, a strong correlation is detected between the average internal temperature of the dwellings and the average income of the selected families. Finally, the heating energy consumption was found to be lower than the country’s average for the clusters characterized by high and partial deprivation. [ABSTRACT FROM AUTHOR]

The anthropogenic heat, which primarily consists of emissions from human metabolism, and also the rejected or waste heat from electricity consumption in buildings due to human activities and the requirement for maintaining the thermal environment by human being, is considered as an important contributor to the urban heat island phenomenon and has been studied worldwide. High energy consumption in the urban buildings impacts the urban surface energy budget and causes the emission of anthropogenic heat flux into the atmosphere. Today, with the growing population and growing activities in buildings, much of the concern is focused towards the decrease of anthropogenic heat through low energy consuming and more energy efficient buildings. This study, utilized a validated occupancy probabilistic input model to estimate different building retrofitting solutions on the impact of building energy consumption and further as the basis for building cooling associated anthropogenic heat study. A holistic evaluation of passive, active and combined strategies is tailored for application with high occupancy variation situation such as campus buildings. With the best retrofitting solutions, the campus anthropogenic heat is reduced from 150 W/m 2 to 128 W/m 2 , which is considered as very effective. Although the campus buildings are used as an example, this paper demonstrates a generalisation method for the application of retrofitting strategies and efficient building systems in estimating the reduction of anthropogenic heat. The results demonstrate high potential for the campus building retrofitting and the values can contribute as a database for future urban heat island effect estimation. [ABSTRACT FROM AUTHOR]

This paper presents the energy and exergy studies of a ground source heat pump (GSHP) system for a public building in Wuhan, China under five different control strategies to improve the system performance. Original system was under manual operation with constant frequency heat pump, constant speed circulation pumps, and constant water flow rate. The five control strategies were: 1) Automatic ON/OFF control based on Building Load Ratio (BLR); 2) Optimum circulation flow rate control; 3) Optimum ON/OFF control with variable speed heat pump units; 4) Variable flow control by adjusting valve position; 5) Variable flow control by variable speed pumps. The results of the system exergy efficiency, exergy loss, COP, and energy consumption under different operation scenarios are presented. By comparing original operation with the best case (scenario five), it is found that during cooling and heating season, the system exergy efficiency was improved from 9.0% to 10.4% and from 6.1% to 6.9%; exergy loss dropped by 31% and 51%; COP increased from 3.2 to 3.7 and from 2.7 to 3.8; and energy consumption was reduced by 37% and 60%, respectively. [ABSTRACT FROM AUTHOR]

During the master planning of neighbourhoods, design decisions related to the urban layout and typology affect the building form and availability of solar radiation considerably. The influence of those decisions on the energy demand for heating is often neglected however because appropriate energy simulation tools are lacking. This paper proposes a simple and accurate design tool to assess the solar gains and heating energy demand in buildings during the master planning phase of neighbourhoods. Detailed information on building geometry, constituting building elements and solar obstructions, is extracted from a 3D neighbourhood model using a plugin implemented in the modelling software SketchUp. This information is used to assess the heating energy demand of the buildings in the neighbourhood based on the dynamic Equivalent Heating Degree Day (dEHDD) method. Furthermore, the associated financial and environmental impacts are calculated based on an integrated life cycle approach, combining Life Cycle Costing (LCC) and Environmental Life Cycle Assessment (E-LCA) respectively. The design tool proposed is implemented for the Belgian context and used to analyse a number of schematic residential neighbourhood models with diverse built densities. The analysis reveals substantial differences in heating energy demand, life cycle financial and environmental costs. Neighbourhoods with a high built density and compact building types have a lower heating energy demand with potential reductions of up to 40% compared to neighbourhoods with a low built density and detached building types. [ABSTRACT FROM AUTHOR]

Energy consumption of the public building stock represents an important cost of the balance of a state. Moreover, public buildings, in particular schools, should be buildings with elevated comfort levels because student and teachers spend much time in these rooms. The wellness and productive capacity of students and teachers are primarily affected by the comfort inside and air quality of school rooms. Regarding energy use, school buildings waste much energy because most buildings were constructed before the 1991 and energy saving measures were only implemented in a few schools. This paper analyses the energy performance of eight different schools located in Matera city, southern Italy. The aim of this research is to analyse energy requirement utilizing dynamic analyses with a time step of one hour (using Energy-Plus method). Next, the values of the dynamic analyses were compared to the effective energy consumption. Using the results of this comparison, we validated the numerical model, and then, we analysed different energy auditing actions for these buildings. We included the energy auditing works in three categories: energy restoration of the envelope, of the plant and of both. For each of these categories, we calculated the energy savings. Ultimately, we analysed the environmental benefits of the three different categories in terms of CO 2 reduction. This research confirmed that the dynamic method is the best method to achieve a good energy analysis of these complex buildings. [ABSTRACT FROM AUTHOR]

This research paper collects the outcomes of an experimental study focused on the energy benefits achieved by coupling an energy efficient building with a sunspace in winter season and Mediterranean climate. Acting as passive Solar heating elements, sunspaces are intended to be a valuable tool among sustainable and energy-efficient constructive and/or retrofitting strategies. In order to assess the energy saving potential of the above-mentioned coupling and relate it to the main design parameters, two operative steps were carried out: the numerical step allowed different simulated configurations to be evaluated in order to identify the optimal one from an energy perspective and physically realize it; the monitoring phase served the double purpose of calibrating the simulation model and provide on-site validated data for subsequent assessments and comparisons. Three main sunspace configurations were tested: irradiative single-glazed, irradiative double-glazed and convective double-glazed. The convective double glazed configuration was found to outperform the others since the daily energy consumption reduction exceeded the 27% (compared to the irradiative, double glazed counterpart) for all weather conditions. Besides, the activation of a mechanical ventilation system was slightly counterproductive in terms of discomfort phenomena since a less stable indoor air temperature was guaranteed. Comparing the two irradiative configurations, it was found that the addition of an extra glass layer was responsible of a quite constant reduction of 1 kW h/day, thus increasing the percent heating energy demand saving achieved through the insertion of the sunspace from approximately 3,3%–8,7%, while minimizing indoor air temperature fluctuations too. [ABSTRACT FROM AUTHOR]

This paper presents the SOLEN integrated online tool, dedicated to citizens and local authorities. This methodology, developed to allow precise energy assessment (heating, cooling, ventilation, lighting, appliances, and cooking but also local production of renewable energy) of household energy uses, is firstly introduced. SOLEN uses a typological classification of buildings and thermal simulations. Many parameters are defined and taken into account to capture the specificities of numerous types of buildings exhaustively (e.g. type of buildings; number of floors; common ownership; orientation; thermal performances of the walls, floors, roofs, and windows; and ventilation type). These results related to building energy consumption are then crossed, in an integrated approach, with several indicators of urban sustainability, to take into account in the balance of the impact of the location of buildings on transportation energy consumption or the impact of the urban form on the production of solar renewable energy. This tool makes accessible to a large non-specialized audience the results of a three-year scientific research study in Wallonia (Belgium) and was awarded an Energy Globe Award (Belgium) in 2014. The first feedback from users is presented to conclude this contribution. [ABSTRACT FROM AUTHOR]

Accurate modelling and prediction of energy consumption of the air conditioning system is crucial for improving decision making. A method for predicting the energy consumption of air-conditioning systems is proposed in this paper. Based on the same weather type (sunny, cloudy, overcast, or rainy) and day type (workdays or holidays), the similarity errors using the combined weight method and the baseline errors of similar working conditions are calculated with this method. These conditions include outdoor temperature and lighting and plug power, then, similar days are determined within a certain similar error range. In addition, the air-conditioning energy consumption in these similar days is regarded as that in the predicted days. The similarity errors in selecting similar days are acquired by efficiently combining subjective weights, objective entropy weights, and correlation coefficients. To verify the accuracies of the predicted energy consumption using similar days method based on combined weights, a simulation was performed by eQUEST. According to the simulation example of measured data in an office building, it is proved that the proposed prediction method with high forecast accuracy can select similar days with a high degree of similarity under non-catastrophic weather conditions, and offer promise for wider engineering application. [ABSTRACT FROM AUTHOR]

Occupants affect energy consumption in buildings by contributing internal heat gains, increasing internal carbon dioxide levels and adapting their behaviour. Estimated occupancy schedules are used in building energy models for regulatory compliance purposes and when empirical data are not available. Metadata, such as personal location data, is now collected and visualised on a global scale and can be used to create more realistic occupancy schedules for non-domestic facilities, such as large retail outlets. This paper describes a protocol for extracting and using freely available metadata to create occupancy schedules that are used as inputs for dynamic simulation models. A sample set of twenty supermarket building models are used to demonstrate the impact metadata schedules have when compared with models using the estimated schedules from regulatory compliance. Metadata can be used to create bespoke occupancy profiles for specific buildings, groups of buildings and building archetypes. This method could also help reduce the gap between predicted and actual performance. In the example models, those using the regulatory compliance schedules underestimated heating demand by approximately 10% and overestimated cooling demand by over 50% when compared to models using the metadata schedules. Although this work focuses on UK facilities, this methodology has scope for global application. [ABSTRACT FROM AUTHOR]

Directive 2012/27/EU on energy efficiency has set the obligation to install individual meters or indirect systems (e.g. heat cost allocators) for heat accounting in multi-apartment buildings supplied by district heating or common central heating. Unfortunately, despite the need of consumers’ protection, neither legal metrology requirements are available for indirect heat accounting systems nor applicable technical standards specify the related on-field maximum permissible errors. In fact, the peculiarity of such systems is the high number of interconnected devices and the influence of installation and operative conditions on their on-field metrological performances should be significant. In this paper, a statistical model to estimate and predict the on-field reliability of heat accounting systems is proposed. The developed model has been applied and validated in three different case studies (i.e. a two-family house, a small building, a large multiple building) showing a good agreement with experimental data. The uncertainty of indirect heat accounting systems in different operating conditions ranges from about 2.7% (i.e. in a large multiple building in optimal conditions) to about 11.7% (i.e. in a two family house in critical conditions). The developed model allows to assess the influence of installation conditions with particular reference to the number and type of radiators and their installation, also in respect with single apartments’ peculiarities and usage. Therefore, the model can be adopted to design appropriate accounting systems in new buildings and to assess the system reliability in the existing ones. [ABSTRACT FROM AUTHOR]

The energy retrofit of the existing building stocks is considered a preeminent strategy to lowering the energy consumption of the building sector. Other than the definition of the most successful combination of energy saving measures, the definition of a suitable ranking of all the buildings of the stock, on which implementing established energy retrofit measures, is quite a relevant issue. The most eligible criterion for prioritizing the buildings seems their energy consumption because it combines the building energy performance and their size. To investigate the role played by the building dimension in determining the order of priority of all the buildings of a given stock, this paper presents and compares different hierarchical orders of 25 existing buildings that were obtained with and without consideration of the size of the buildings. Results show that, when the size of buildings is covered in the calculations, the generated prioritized order differs significantly. In addition, it has found that the entity of the variations compared to the hierarchy that does not contemplate the size of buildings, is logarithmically correlated with the net surface. Additionally, some concerns have arisen regarding the role of the EP index for the building energy classification. [ABSTRACT FROM AUTHOR]

In this paper, it has been attempted to present a temperature control method for the building and, simultaneously, reduce costs of providing energy in hybrid heating systems. In the present work, a building in Tehran city has been investigated as a sample during a single day and applying intelligent control methods in the presence of two gas and solar heat sources. Furthermore, the influence of each of these methods has been studied on reducing costs as well as regulating indoor temperature. In the next step, the utilized controllers have been redesigned for the laboratory model and their capability has been evaluated through experimental tests. Based on the acquired results, it is deduced that, compared to other methods, utilizing a PID controller optimized by genetic algorithm not only reduces 50% of energy providing costs, but also regulates the inner temperature of the Lab model with an error lower than 1%. This method is faster than the others in regulating the model temperature. However, in the real building modeling case, the efficiency of this method has been reduced relative to the laboratory model, mostly because of variable conditions such as variable solar emission intensity during different hours of the day. Yet, acceptable results have been acquired by performing MPC and correct modeling. These results show that, as the variable parameters increase, the MPC presents higher capability compared to other methods In this case, the MPC has similar costs to the genetic algorithm while it regulates the temperature faster and with lower error. [ABSTRACT FROM AUTHOR]

In EU member states, the consumption of electricity per dwelling has remained more or less constant, although the consumption of large appliances has decreased considerably in the last 2 decades. This stabilization is caused by the increased ownership, usage and consumption levels of Information, Communication and Entertainment (ICE) appliances. This paper aims to analyze electrical appliance use in the Dutch housing stock, and identify behavioral patterns and profiles of electricity consumption. The analysis is conducted by applying descriptive, correlation, and exploratory factor analyses on data collected from 323 dwellings in two neighborhoods in the Netherlands. Our results show that behavioral patterns could be found based on actual occupant behavior of lighting and appliance use, especially depending on household activities like cooking, (personal) cleaning, etc. Behavioral profiles could be determined based on household and dwelling characteristics, i.e. household size, income, education, dwelling type, age, hours of working outside. The 4 profiles set up in this research are explained as ‘family,’‘ techie,’ ‘comforty,’ and ‘conscious.’ These profiles showed statistically significantly differences in terms of their electricity consumption levels. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic modelling and optimizing of energy consumption and indoor thermal comfort for air-conditioning and mechanical ventilation (ACMV) systems. The models of extreme learning machines (ELM) and neural networks (NN) are established and evaluated. These well-trained models are then integrated with the computational intelligence techniques of sparse firefly algorithm (sFA) and sparse augmented firefly algorithm (sAFA). The sFA and sAFA aim to locate the global optimal operating points of the ACMV systems in real-time and predict energy saving rate (ESR) with a third order polynomial regression based on minimizing the mean squared errors (MSE) of the cost functions. This study also covers different indoor scenarios, such as general offices, lecture theatres and conference rooms. Given the well trained models, the maximum prediction of potential ESR can be −30% via the sparse AFA optimizations while maintaining indoor thermal comfort in the pre-defined comfort zone. [ABSTRACT FROM AUTHOR]

The Solar Decathlon is a competition for universities from all over the world which focuses on designing, building and operating experimental, solar-powered houses. Participating in the project offers universities a unique and interdisciplinary platform for teaching, learning and research which combines practical experience with research. In 2022, the European edition was held in Germany for the first time. The event took place with a new urban profile to increase both the relevance of the competition and the learning experience. Its main topic was the further development of the European city, and specifically focused on the existing stock of residential apartment buildings. A total of 18 teams from 11 countries with over 500 students took part, with 16 teams ultimately building their houses on a shared solar campus. Demonstrating a balanced or a positive energy balance in practice was one of the essential goals of the competition. This was achieved by 13 of 15 projects in the energy contest. The prerequisites for this were a high level of energy efficiency and the consistent use of solar energy. Both strategies were embedded in convincing architectural concepts. These ranged from the minimised visibility of standard systems on rooftops, through to custom-built systems with full architectural integration in façades. Hybrid solar systems also became a focus, with the goal of making optimum use of the surfaces on the building envelope. This paper focuses on the energy engineering and technical and architectural integration of the solar systems. It also includes the results achieved in the competition linked to the learning experience. [ABSTRACT FROM AUTHOR]

[Display omitted] Reinforcement learning (RL) algorithms have shown great promise in controlling building systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn a control policy by interacting with the physical or simulated environment that represents building systems, occupants, and the outside world. Yet, a large amount of data is needed to learn a near-optimal control policy in the physical building, which requires months or years to collect. Moreover, an agent's performance while training can be quite poor, causing occupant discomfort and additional costs. Learning in simulation does not have such real-world impacts, but differences between buildings, and indeed between simulation and physical buildings, potentially lead to poor performance when a policy learned in simulation is deployed in a physical building. This paper addresses part of the sim-to-real problem by training on one set of simulated (source) buildings and then deploying to a novel simulated (target) building. This approach significantly reduces the training cost of RL on the target building by 1) learning a large number of policies on prototype buildings, 2) evaluating these policies on historical data obtained from the target building's environment and selecting the best ones according to the evaluation result, and 3) using the best policies to control the target building while continuing to learn. The proposed approach involves learning a diverse population of control policies using a novel diversity-induced RL algorithm, and policy clustering, evaluation, and selection techniques. Three case studies show our approach assigns policies to the target building that outperform the default controller by 4.0–30.4%, without sacrificing thermal comfort. Similarly, they outperform policies that are learned only on the target building (i.e., without transfer) by 24.9–74.9% and 16.2–72.2% before and after 500 months of training, respectively. [ABSTRACT FROM AUTHOR]

Industry is stuck in reactive mode regarding the maintenance of their heating, ventilation and air conditioning (HVAC) systems, which leads to inefficient operation. To avoid wasteful use of natural resources a proactive approach is needed. To enable this transition combined knowledge-based and data-driven approaches such as the Industrial Data Analysis Improvement Cycle (IDAIC) framework have been proposed, but there is a lack of practical implementation in the literature. In this paper, we implement the IDAIC framework on an Air Handling Unit (AHU) in a real industrial facility to address the lack of practical and reproducible real world analysis. The lack of domain knowledge-driven data mining solutions is addressed primarily in two parts. Firstly, data and control issues are addressed to enable the application of knowledge-based techniques to detect faults that are easily identifiable. Secondly, data-mining based techniques are applied which focus on the detection of more subtle faults which are indicative of component degradation. This approach enabled the development of data-driven analysis that visualises the effect of the different modes of operation on the energy consumption of the AHU, which allow the onsite facilities team to make more informed decisions regarding the maintenance of this AHU. [ABSTRACT FROM AUTHOR]

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions. [ABSTRACT FROM AUTHOR]

Very little work has been done on the feasibility of Machine Learning (ML) for predicting buildings energy demand right at the design stage. This feasibility, if proven, would help to avoid the construction of inefficient buildings. This paper uses dataset from 7559 buildings, and estimates their energy consumption using nine ML models. Results show that deep neural network (DNN) is the most efficient ML model with MAE, MSD and RMSE of 0.93, 1.12 and 1.06 respectively achieved in less than 7 s despite the huge data size. Its r 2 is also the highest (0.96) which means that the DNN approach manages to explain 96% of the energy consumption in buildings and only 4% remains unexplained certainly due to the limitation of independent variables. Also, this result is not affected by building clusters nor by data from a particular climate zone. As an innovation, this study proposes a model that professionals could use in the design phase of a construction project. This model will allow them to take into account all crucial aspects of the design of an energy efficient building. The model will then serve as a decision-making tool to control and optimise the project and to anticipate energy consumption even before the building is constructed. [ABSTRACT FROM AUTHOR]

Solar energy utilizations in buildings are contingent on efficient shading models. This paper presents a new trigonometric model that was simple in application and robust in handling diverse site environments. The developed model was experimentally validated and compared to existing shading techniques such as pixel counting and polygon clipping. Additional analyses were performed to examine building façades ranging from flat to free-form surfaces in both linear and non-linear urban forms. This study integrated two submodels into the shading model to estimate the reduction in cooling demand and the energy production from solar applications. On annual basis, elliptical urban grids provided less normal beam radiation by a mean of 4.7 % or 22.8 W/m2 than the linear grids tangent to the curvilinear ones. South-facing façades gained more radiation by a factor ranging from 1.26 to 1.55 due to wider street grid conditions. Shade-less protrusions of the free-form façades allowed the incidence factor to reach up to 0.91 compared to 0.35 in flat façades. Although the developed model had an average error of 5.2 %, the computational time was reduced by more than 40 %. [ABSTRACT FROM AUTHOR]

• A new urban building energy modeling tool-AutoBPS was developed. • AutoBPS calculated urban building energy demands, analyzed energy retrofit and rooftop PV potential via EnergyPlus. • AutoBPS automatically generated multi-zone models, considering shading from surrounding buildings. • A downtown district of 3633 buildings in Changsha, China, was simulated to demonstrate the tool's features. • All three energy conservation measures and PV installation could save 38.6% of total energy demand. China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation. [ABSTRACT FROM AUTHOR]

• This paper conducts assessment of hybrid renewable systems for urban buildings. • A new demand model for heterogeneous building types is developed. • Time series forecast of electricity market is proposed. • Novel renewable supply and balance scenarios are developed. • Techno-economic assessment of hybrid renewable systems shows feasibility. This research conducts a technical and economic feasibility study of multi-energy hybrid systems (MEHS) combining different renewables for a northern climate city of Finland to address issues of replacing fossil energy by renewable energy sources (RES) to achieve zero carbon emissions. The renewable energy systems MEHS include geothermal, biomass and solar energy. The selected city-study site is the Olympic Training Center occupied by heterogeneous buildings which presents a good representative urban city for the assessment of the energy supply and demand. A generic and integrated modeling framework grounded in a combination of novel forecasting models for the heterogeneous building energy demands, the splitting of space heating and hot water use, the coupled renewable energy generation, and the time series electricity market is developed to investigate optimal MEHS configurations based on the technical and financial analysis. Innovative scenarios of MEHS are developed. The analysis results show that MEHS can flexibly meet the energy demand for the heterogeneous buildings and the optimal configurations can be realised by an innovative integration of geothermal and solar sources as well as electricity as complementary energy. Model uncertainty and its impacts on the analysis are also considered and discussed. [ABSTRACT FROM AUTHOR]

• Circulation heat loss data without hot water demand were collected and analyzed. • A novel method for calculating dynamic heat loss was developed and deduced. • The proposed dynamic heat loss-based operation scheduling model minimized energy consumption within comfort constraints. • The proposed solution saved 16.42% in energy consumption and 24.76% in operational costs. Building energy systems with circulation loops have high circulation heat loss, resulting in low energy utilization efficiency. It requires energy-saving operation strategies to eliminate unnecessary circulation heat loss, especially in domestic hot water (DHW) systems. The objective of this paper is to improve the energy efficiency of building energy systems and reduce operational costs. This is done by dynamically adjusting the target temperature set point. This is the first study to present a computationally inexpensive method for accurately predicting the temperature drop of hot water in environments with changing ambient temperatures. The outcome indicates that energy usage efficiency could be increased by approximately 16.42 %, while operational costs could be lowered by approximately 24.76 % for over 90 days in the fall. The proposed model is an implementable approach for predicting temperature drop and optimal operational scheduling as part of robust and reliable building energy management in real-world scenarios. [ABSTRACT FROM AUTHOR]

• The proposed residential power scheduling strategy in smart grid is capable to achieve the optimal energy consumption profile. • The electricity payment and discomfort cost are coupled to constraint the DR to influence on users' consumption efficiency. • A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. • A day-ahead bidding price mechanism including real-time charging management is introduced to establish different household consumption patterns to guide the consumer to enjoy convenience while saving costs. • Various simulation experiments are performed to confirm that the renewable energy penetration is higher, consumers' consumption utility is higher, and the corresponding cost-efficiency is higher. Power scheduling is a major task for demand response (DR) to cope with the electricity consumption during the peak period in smart grid. In the residential sector, consumers trend to trigger their electrical appliances when the electric price is low. However, power scheduling including load curtailment, time delay and equipment power adjustment will change consumer's comfort level to produce discomfort cost. Thus, electricity payment and discomfort cost are coupled to constraint the DR. In this paper, we focus on renewable energy use by smart homes and consider cost-coupling constraints, taking an approach whereby we decouple the primary problem into several independent subproblems. A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. To that end, a day-ahead bidding price mechanism including real-time charging management is introduced, and a cost efficiency algorithm is designed based on electricity payments, discomfort and distributed generation. Simulation results demonstrate that the proposed method is effective and feasible when tested in real application scenarios. [ABSTRACT FROM AUTHOR]

• This paper presents a unique change-point model based on selected time periods. • Time-dependent variations in climate and user behavior are central in the selection of time periods. • The model allows for differentiation between thermal power characteristics. • The selected time period for prediction of Q total corresponds to the highest R2 value, 0.70. The building and service sector accounts for nearly 40% of total energy use in Sweden. The existing, historic building stock accounts for large part of this energy use and comprises an important part of the national pursuit to increase energy efficiency. One main problem for decreasing energy use in the existing building stock is the lack of data describing thermal performance characteristics. This paper presents a novel development of the change-point model for predicting the thermal performance of buildings using selected time periods based on time-dependent variations in climate and user behavior. The predicted thermal power characteristics include total specific heat losses (Q total), energy use for hot water circulation (HWC) and hot tap water (HTW), and balance temperature. A residential district with 73 historic buildings in Linköping, Sweden, has been used as the study object. The developed model is shown to be effective and robust for describing building thermal performance. The average R2 was 0.70 for predictions of specific heat losses. The sensitivity analyses conclude that the selected time steps and months correspond to the highest R2 value. The average variation width for prediction of the balance temperature is 0.9 °C for buildings in the interquartile range based on a three-year comparison of hourly heating power supply data. Moreover, from a property owner perspective, the model is shown to be useful for identifying deviating thermal power characteristics and can easily be used to get an overview of a district. [ABSTRACT FROM AUTHOR]

• Freely available online overview of district heating efficiency indicators. • Hourly district heating energy use data from approx. 43 000 buildings. • Building stock segmented into building typologies for analysis of: • -1) Heating energy use intensity, 2) load variation and 3) cooling efficiency This paper provides a state-of-the-art and freely available online overview of the district heating (DH) efficiency of Danish building typologies. The overview is based on DH consumption data from 42 969 buildings constructed up until 2017 that in combination with data from the Danish Building and Dwelling Register constitutes a dataset with a previously unseen level of detail, confidence, sample size, and research opportunities. The paper also presents an initial analysis of the dataset in terms of three heating efficiency indicators, namely 1) Annual heating energy use intensity, 2) Daily heat load variation, and 3) Cooling efficiency. The overall finding was that building typologies perform significantly different from one another concerning indicators 1 and 2, but not for indicator 3. It is therefore not possible to declare one building typology more efficient than the other as this depends on the building construction year and the considered efficiency indicator. The data indicate that further energy-efficiency improvements of the existing building stock are essential to the realization of 4th Generation District Heating technologies in Denmark and that these improvements most likely will lead to increased heat load variations that need to be addressed, for instance by enabling a larger degree of demand-side flexibility in the existing district heating systems. The large temporal variation of the buildings' cooling efficiency indicates that there is a potential for rethinking e.g. domestic hot water production in the summer or introduce differentiated billing based on the daily, weekly, or maybe seasonal cooling performance. The presented dataset can be subject to several future investigations which are also discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper reviews and analyzes technological design options, which have become available to date for BIPV systems on roofs and facades, independently of specific products or building projects. This means that this survey does not analyze existing products or realized buildings, but provides an overview of the technologies for BIPV. The starting point is an analysis of the relevance of BIPV technologies for the decarbonization of energy systems, providing energy for direct use of electricity and sector coupling together with an analysis of the German BIPV market. The paper presents the wide range of technical design options for BIPV systems and categorizes and analyzes them to provide a structured overview. This comprises a detailed analysis of the design options for BIPV modules, in which not only the design options for the PV cell layer were comprehensively investigated, but also the different variants of embedding materials, front and rear cover materials, additional interlayers and electrical module layout. Two fundamental module-level design options were investigated in particular detail: The use of PV cells as basic elements of patterns and the use of color to conceal the PV cells. Subsequently, options for the design of complete electrical systems are reviewed, ranging from sub-module level design parameters to building energy systems. Design options for the constructional integration of BIPV modules in the building envelope complete the review of technological design possibilities. [ABSTRACT FROM AUTHOR]

Thermal properties have significant impact on ecological footprint and life-cycle assessment of buildings. This is even more crucial aspect for glass buildings, which have been criticised as major factors in climate change around the world. Recent debates on glass facades, which culminated in plans for a "glass building ban" in New York City highlighted the importance of innovation in glass construction. The current solutions for mitigating energy consumption for glass facades are improving insulation (U-value) and solar heat gain coefficient (SHGC) with advanced glazing or using shading devices. A fourth approach is the recent technology of water-filled glass (WFG), which utilizes water layer to improve thermal comfort and energy performance of the façade. This paper introduces Smart Water-filled Glass (SWFG) control method, which enables the change of the opacity of the façade element by colouring the fluid over a year regarding seasonal changes. The impact of changing transparency of water layer on cooling and heating energy demand is evaluated by simulating and analysing the energy performance of a reference office room with large glazing facade in different climates. The significance of this energy evaluation is that a water-filled glass with switchable transparency is simulated and assessed here for the first time. The paper analyses the performance in seven cities from all relevant major climatic regions using LBNL WINDOW and TRNSYS (v.18) As a novel approach, the simulation study makes it possible to evaluate the overall performance of two proposed operational methods (insolation-based & storage-based) for adjusting water layer settings depending on climate conditions and reusability of the captured heat in water layer. Moreover, seven different base cases are presented in this study to compare the performance of proposed system, including conventional solutions (e.g. double or triple glazing with/without shading) and a state-of-the-art switchable technology (e.g. electrochromic glazing or EC). The results verify the hypothesis that the impact of SWFG compared to WFG varies depending on the climate with 0%-5.28% for hot climates and 0.25–3.39% for climates with heating demand. The discussion includes comparison with standard glass facades, where SWFG shows 47.41%-78.01% energy savings depending on climate. The climate-based approach of SWFG and WFG offers a significant shift in façade design that sees glass buildings as an opportunity for sustainability rather than liability for climate change. [ABSTRACT FROM AUTHOR]