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As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an 'archetype-in-neighbourhood' framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (T ex , °C), and (2) estimated indoor cooling temperature set-point (T in , °C) of the city' housing stock during the same period. Through a k -fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2 =0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design. [ABSTRACT FROM AUTHOR]

• Qualitative outcomes of energy poverty across Australian climate zones are compared. • Outcomes influenced by energy costs and local energy, housing and welfare policies. • Measures of energy poverty and policy responses need to reflect local contexts. Academic interests into energy poverty have been growing since the 1990s. These have ranged from quantitative measures and qualitative reflections, as well as ones that aimed to provide more nuanced definitions. These studies have, however, typically focused on single jurisdictions or across national borders, with very few examining the issue across different climatic and policy contexts within the same nation. This paper aims to address this gap by focussing on differences in climate conditions, local policies and quality of the housing stock across Australia and reflects on how these factors each played a role, to varying degrees, in the prevalence of energy poverty amongst lower-income households across eight metropolitan and regional settings conducted as part of a recent study in Australia. Through a review of focus group, interview and workshop data, it especially highlights the more qualitative impacts of energy poverty, and how each of these factors come to influence the extent to which lower-income households experience energy poverty and the support they were able to access. Additional to the need for more contextualised measures of energy poverty, this paper calls for localised responses to alleviate the negative outcomes reflected by our participants in regard to both energy and housing policies. [ABSTRACT FROM AUTHOR]

The micro-grid with combined cooling, heating, and power supply system (CCHP) is a complex integration that assembles various distributed generators (DGs). The motivation of this paper is to select a reasonable configuration of DGs for a CCHP-MG by means of uncertainties models, improved operation strategy and algorithm. The DGs in a CCHP-MG are composed of wind turbine (WT), photovoltaic (PV), battery energy storage (BES) system, biomass gasification (BG), Stirling engine, heat exchanger, absorption chiller, electric heater and cooler. In terms of uncertainties models, the uncertainties of variations in wind speed, solar irradiance, temperature, and load demand are considered with 8760 h of a whole year. The paper proposes a multi-objective optimization model for the CCHP-MG system under three objectives associated with the minimized system cost, greenhouse gas (GHG) emissions and power waste. In this study, the double-layer operation strategy is raised for replacing the conventional objective of loss of energy supply probability (LESP) in the CCHP-MG system. Based on the non-dominated sorting genetic III (NSGA-III) algorithm, methods of 1/8 unit sphere and angular distance are proposed for an improved NSGA-III algorithm (INSGA-III) to increase computational efficiency and avoid local convergence. Finally, the multiple analysis and comparisons demonstrate that the cooperation of the double-layer operation strategy and INSGA-III has better performance with superiorities of economy and reliability. The results in the case study show that the system cost, GHG emission, and energy waste of the selected CCHP-MG system respectively reached 85.23, 72446.7, and 7690.46. The study confirms that the improved operation strategy, INSGA-III algorithm, and multiple analysis framework can be extended to design CCHP-MG system in other places. [ABSTRACT FROM AUTHOR]

Electricity is an essential resource for human production and survival. Accurately predicting electrical load consumption can help power supply companies make informed decisions, such as peak load shifting, to maintain a reliable power supply and reduce CO2 emissions. However, forecasting electricity consumption is challenging due to the nonlinear and nonstationary time series data that is correlated with climate change. To address this challenge, this paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM. EMD is a solid and robust instrument for time–frequency analysis and signal preprocessing, which separates the time series into components at different resolutions. The proposed model predicts the future 24 h with a resolution of 15 min by creating many stationary component sequences from the original stochastic electricity usage time series data (IMFs). To predict each Intrinsic Mode Function, a hybrid model BI-LSTM is employed. The results of each component's forecast are then merged to give the overall forecast. Two comparative studies are conducted to justify the choice of the signal processing method and the prediction algorithm. The proposed model demonstrates a minimal MAPE of 0.28% and a better R 2 close to 1 of 0.84 compared to other papers. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• An optimization strategy model of district energy system under the direct reduction/increase goal of power load is presented. • The grid can directly control the energy consumption strategy in the form of instructions. • The proposed goal pays a certain price, but a large additional flexible utilization coefficient is obtained. • The energy compensation coefficient of the multi energy coupling system is mostly negative. With the wide application of regional centralized cooling (heating) system in the world and the growing maturity of energy supply technology, the focus of system optimization gradually transforms from technical optimization to strategic optimization to attain the common goals of economy and energy saving. Aiming at the operation strategy model of power grid interactive district energy system based on time-of-use (TOU) price proposed in previous research, this paper proposes new objective equations and constraints to explore the strategic deployment of district energy system when accepting the power grid requirements to reduce or increase the power load directly to improve the economy and energy efficiency of centralized cooling (heating) system. The modified optimization strategy model is used to respond to sudden demand response or power accidents. The strategy model can be directly controlled by the power grid, and the TOU price is no longer the main factor affecting the formulation of the strategy. In the face of sudden demand response or power accidents, the operation strategy optimization model proposed in this paper can guarantee the energy supply demand of users and optimize the economic benefits and energy efficiency of the systems. The operation strategies under various optimization objectives can be obtained through the simple input of energy loss of the pipeline and user cooling or heating load. The proposed model is suitable for the load allocation of energy system with single energy supply form and for the strategy optimization of multi energy coupling system. The accuracy and validity of the proposed model under various optimization objectives are verified based on experiment and simulative research. [ABSTRACT FROM AUTHOR]

The variable differential pressure (DP) set-point control strategy has become the main energy-saving measure for central air conditioning water system variable volume adjustment. In optimal operation control, it is critical to use the right reference object to implement global, accurate, and online control. In mainland China, the most unfavorable loop is typically used as the reference object for variable DP set-point control in the majority of cases of variable volume air conditioning system adjustment. However, its concept and feasibility require further clarification. First, this paper analyzes the difference in the characteristics of a most unfavorable loop under air conditioning design work conditions versus under operational conditions. Under design operational conditions, the referenced most unfavorable hydraulic loop only reflects the distribution of the pipe network's hydraulic characteristics, whereas under system operational conditions, the variable volume adjustment strategy should be chosen according to each user's load requirement, i.e., a most unfavorable thermodynamic loop. Then, this paper provides definitions of the most unfavorable thermodynamic loop and online identification method and proposes a variable DP set-point optimal reset strategy based on the maximum valve position loop's optimal reset valve position domain and best valve position domain. Finally, the variable DP set-point control of a primary pump variable volume air conditioning water system in an industrial factory is used as an example to conduct a test studying 6 variable DP or constant DP control strategies, including the method proposed in this paper. The test results show that the most unfavorable loop-based variable DP control strategy saves 47–58% water pump power consumption (up to 985 kWh electric power consumption per day) when compared with the conventional variable DP control strategy, whereas the most unfavorable thermodynamic loop-based variable DP control strategy is more advantageous in ensuring the overall cooling energy supply for each user. [ABSTRACT FROM AUTHOR]

This paper presents a residential energy hub model for a smart multi-carrier energy home consisting of plug-in hybrid electric vehicle (PHEV), combined heat and power (CHP), solar panels, and electrical storage system (ESS). The energy hub inputs are electricity and natural gas that provide electrical and heat demands at the output ports. In this paper, an optimization-based program is proposed to determine the optimal operation mode of the energy hub, to manage the energy consumption of responsive appliances, to schedule charging/discharging of PHEV and the storage system, and to coordinate solar panels operation with household responsive demand in response to day-ahead time-varying tariffs of electricity. The objective function is to minimize customer payment cost considering vehicle to grid (V2G) capability. Different case studies are conducted to probe the effectiveness of the proposed method and study the impacts of different electrical time-differentiated tariffs on the optimization results on daily and yearly basis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the energy and exergy performance of a social dwelling of a multi-family building from the 1960s in Bilbao (Spain) is presented and various improved energy concepts based on exergy principles are proposed and investigated. The aim of this paper is to explore and demonstrate the usefulness of the exergy approach in the assessment and development of an energy system for the dwelling under consideration. The total energy supply system is analysed, including the demand (space heating, domestic hot water and electricity), the system components (for conversion, storage and distribution) and the energy input from energy resources (primary energy and renewable resources). The study includes a comparison of the primary energy input of all cases considered and an analysis of the energy and exergy losses of each system component. The study has shown that the exergy analysis reveals thermodynamic losses that are not revealed using energy analysis and secondly, that the development of an improved energy system based on exergy principles has resulted in a significantly reduced primary energy input compared to the reference situation. [Copyright &y& Elsevier]

Abstract: This paper reviews crucial role of building performance simulation (BPS) – dynamic analysis of the inextricable linkage of building''s energy demand for HVAC and other building''s technical systems sustainable energy supply and renewable energy sources (RES) availability to reach building''s zero energy status. Reviewed are BPS advances in buildings energy efficiency optimization, solar, geothermal and other renewable energy sources integrated implementation, as well as hybridization and mixed distributed energy generation, co- and tri-generation for building''s greening and sustainable neighborhoods, settlements, as well as “high performance” Zero Energy Cities Planning. As an introduction, outlined is the intrinsic harmony of the traditional village houses balanced use of renewable materials and energy sources and presented are feasibility study results on the sustainable Zero Energy ECO Settlement at the Mediterranean Sea proceeded by the description of the site''s sustainability''s constrains assessment. Architectural design and building''s dynamic behavior and energy efficiency optimization resulting in minimization of thermal and lighting loads, precede to the multidisciplinary engineering investigation and preliminary design of sustainable Zero Energy Buildings confirming feasibility of settlement''s sustainable energy and water supply by the reliable implementation of solar PV and wind technologies, including PV powered sea water desalination. Further, interwoven with presentation of a few case studies, paper outlines advances in BPS and Co-simulation. Reviewed are optimization of mixed, natural and mechanical ventilation via CFD integrated with architectural modeling; BPS and Co-simulation approach to the “total building performance optimization” encompassing multifunctional facades, day-lighting, IE&AQ and HVAC systems operation offering an enormous potential for energy saving by residential/municipal RES integrated renovation. Stressed has been that it is impossible to reach sustainability without harmonious interdisciplinary interacting, without balance between materiality and spirituality, science and art, technology development and cultural and other human values improvement, without ethics of sustainability. [Copyright &y& Elsevier]

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities. [ABSTRACT FROM AUTHOR]

Abstract A novel power management for nanogrids is presented. In this paper, effect of resident location in residence is taken into account for evaluation of power consumption in nanogrids. Depending on the resident location, operating conditions of heater, ventilation fan, and air-conditioner are adjusted. Electric appliances that allow delayed use, i.e., shiftable, are scheduled for efficient power management. Considering massive deployment of energy storage system (ESS) as a supplementary power source, effect of the ESS on power management is investigated. Demand response program for reduced electricity cost is also considered for power management. As a result, an objective consisting of power consumed by non-shiftable and shiftable electric appliances, power supplied/consumed by the ESS during discharging/charging, and time-varying electricity cost is formulated. Another objective for power management is a function of delays in scheduling of shiftable electric appliances. Variation of resident location combined with temporal use of electric appliances according to the resident location are considered as the resident behavior and incorporated into these two objectives. Using these two objectives, a multi-objective optimization is performed for nanogrids at each time interval. It is demonstrated by simulations that leveraging resident behavior is beneficial for power management of nanogrids. In addition, effects of the number of residents in each apartment and combined activity of each resident on the power management are presented with related simulation results. [ABSTRACT FROM AUTHOR]

Abstract: Building-Integrated Photovoltaics (BIPV) are one of the best ways to harness solar power, which is the most abundant, inexhaustible and clean of all the available energy resources. This paper discusses issues concerning BIPV in architectural design in China, including how to choose between BIPV and building-attached photovoltaics (BAPV), whether it is necessary for photovoltaic components to last as long as buildings and how to design BIPV structures. The paper shows that we should consider the function, cost, technology and aesthetics of BIPV, rather than solely the high integrations. According to developments in technology and markets, photovoltaic structures and design should be focused on the maintenance and replacement of photovoltaic cell modules, rather than simply prolonging their lives. To solve problems associated with the existing photovoltaic structures in China, we design a building photovoltaic structure that allows convenient maintenance and replacement of photovoltaic components. [Copyright &y& Elsevier]

Abstract: This paper presents a study on energy performance of Singapore''s hotel buildings. Energy consumption data and other pertinent information were collected from 29 quality hotels through a national survey. Building features and operational characteristics contributing to the variations in hotel energy performance were discussed. The annual average total energy use intensity (EUI) in these hotels is 427kWh/m2. Electricity and gas are used in all sampled hotels, and some hotels also use diesel to power standby generator or hot water boiler. We also investigated relationships between electricity consumption and number of occupied rooms in individual hotels; the weak correlations found indicate it is necessary to improve energy management when occupancy rate is low. Besides, Pearson correlations between hotel energy use intensity and possible explanatory indicators revealed that three-star hotels differ from high class establishments in energy use. Worker density and years after the last major energy retrofit were also found to be highly correlated to hotel building energy use intensity. Also discussed in this paper is the effect of weather conditions on electricity consumption of the hotels. [Copyright &y& Elsevier]

Abstract: The Cooling, Heating, and Power (CHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CHP systems can be highly dependent on the operation of individual components. This paper presents an energy dispatch algorithm that minimizes the cost of energy (e.g., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CHP system is developed as part of the algorithm. The advantage of using a network flow model is that the electric and thermal energy flows through the CHP equipment can be readily visualized allowing for easier interpretation of the results. This algorithm has been used in simulations of a case study on the operation of an existing micro-CHP system. The results from the simulation are presented in the paper to demonstrate the economical advantages resulting from optimal operation. [Copyright &y& Elsevier]

Abstract: This paper takes a close look at the China national standard GB50189-2005, Design Standard for Energy Efficiency of Public Buildings, which was enforced on July 1, 2005. The paper first reviews the standard, then compares the standard with ASHRAE Standard 90.1-2004 to identify discrepancies in code coverage and stringency, and recommends some energy conservation measures that can be evaluated in the design of public buildings to achieve energy savings beyond the standard. The paper also highlights several important features of 90.1-2004 that may be considered as additions to the GB50189-2005 standard during the next revision. At the end the paper summarizes the latest developments in building energy standards and rating systems in China and the US. [Copyright &y& Elsevier]

[Display omitted] • Dynamic evaluation of economic and energy benefits due to thermostatic valve installation. • Dynamic evaluation of economic and energy benefits due to heat metering installation. • The adoption of both thermostatic valves and heat metering least to a payback period lower than 2 years. • Analysis carried out considering several shapes of typical building. The aim of this paper is to propose a novel approach in order to accurately calculate the energy and economic savings due to the use of heat metering and thermostatic valves in residential buildings. The paper aims at proving that the present simplified approaches used in European Legislation may lead to significantly underestimate the profitability of this technology. The study is developed for four different typologies of buildings located in the city of Naples (South of Italy), including their geometrical and thermophysical properties. The buildings are first modelled in Google Sketchup and subsequently linked to the TRNSYS environment, which also includes a detailed model of the piping systems. The models allow one to evaluate, for every period of the year, energy demand, energy supplied by radiators, heat gains, transmission, etc. The developed models are used to calculate energy demands for three scenarios for each type of building: centralized heating systems not equipped with heat metering; thermostatic valves without heat metering; heat metering and thermostatic valves. The possible savings related to thermostatic valves and heat metering are estimated using the developed model. The results are compared with the ones provided by the Italian Standards. Results show that the thermostatic valves adoption leads to a significant reduction, up to 50%, of the thermal energy demand of the residential buildings and that all the proposed systems exhibit a remarkable economic profitability, with a payback period lower than 1.7 years. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a new operating strategy for the outdoor, discharge, and recirculation air dampers of the economizer in VAV system, called split-signal damper control strategy. The strategy controls the outdoor air by only one damper while keeping the remaining dampers full open. The discharge or recirculation air damper is modulated to control the amount of outdoor air introduced into the system while keeping the two remaining dampers full open. Since at least two dampers are always kept fully open during the occupied times, the strategy can provide a minimum static pressure drop in economizer dampers and results in minimum energy use in return and supply fans. An additional advantage is that the proposed strategy prevents reverse airflow through the discharge air damper of a VAV system that uses a volume matching control strategy. The proposed strategy along with the existing strategies such as the three-coupled dampers used in most existing system and the two-coupled dampers are evaluated on an existing system using 1-year long measured data set, along with an economizer damper model developed and validated in this paper. The simulation results show that the annual energy savings in supply and return fans of an existing system, compared to the traditional strategy of three-coupled dampers, are 12% and 5%, respectively. [Copyright &y& Elsevier]

Abstract: This paper describes a novel method for detecting abnormal energy consumption in buildings based on daily readings of energy consumption and peak energy consumption. The method uses outlier detection to determine if the energy consumption for a particular day is significantly different than previous energy consumption. For buildings with abnormal energy consumption, the amount of variation from normal is determined using robust estimates of the mean and standard deviation. This new data analysis method will reduce operating costs by detecting problems that previously would have gone unnoticed. Also, operators should save time by not having to manually detect faults or diagnose false alarms. The new data analysis method has successfully detected high-energy consumption in many buildings. This paper presents field test results for buildings that had the following problems: (1) chiller failure and a poor control strategy, (2) poor design of ventilating and air-conditioning equipment, and (3) improper operation of equipment following a change in the electrical panel. [Copyright &y& Elsevier]

This paper describes a pattern recognition algorithm for determining days of the week with similar energy consumption profiles. The algorithm determines energy use features, such as average daily consumption or peak daily consumption, from time series of energy use. Features are transformed to remove the effects of seasonal variation that may be present in time series data. Then, the transformed features are grouped by day of the week into seven clusters. Univariate and multivariate outlier analysis methods are used to remove unusual data from the seven clusters. Finally, a modified agglomerative hierarchical clustering algorithm determines days of the week with similar energy consumption profiles. Knowledge of days of the week with similar energy consumption profiles can be used in the following ways: (1) supervisory control strategies that use forecasting algorithms, and (2) methods for detecting abnormal energy consumption in buildings. This paper contains field tests results from three buildings. [Copyright &y& Elsevier]

The purpose of this paper is to elucidate the correct specification of the analytical CTTC model for predicting the urban canopy layer (UCL) air–temperature. Improvement in the model specification was proposed by Elnahas–Williamson by incorporating in the prediction process diurnal information from a site’s representative meteorological station. The paper demonstrates that the proposed improvement causes a serious error in the diurnal predictions, the effect of which may reflect on the indoor energy savings policy. [Copyright &y& Elsevier]

While many studies have estimated the energy saving potential of office equipment power management, there is no recent study about the energy saving potential of shortening the power management delay time. In this paper, we estimated the energy saving potential of the complete saturation of power management, and also the additional energy saving potential of shortening the power management delay time for PCs, displays, copiers, and laser printers in Japanese offices. To obtain key data, we conducted the surveys and field measurements of usage in business hours and the turn-off rate at night. We found that office equipment power management can save as much as 3.5 TWh per year, which is nearly equal to 2% of commercial electricity consumption in Japan. We also found that the energy use of office equipment in Japanese offices with no use of power management is even lower than that in US offices with maximum use of power management. This is primarily because the portable computer is much more popular in Japan, and also because the manual-off rate at night is more than twice that in the US. [Copyright &y& Elsevier]

Understanding the demand flexibility of city-scale buildings under different demand response (DR) strategies is crucial to balance the power supply and demand in the grid. This paper presented a case study to evaluate the electricity consumption reduction potential for buildings in Changsha under different DR strategies. First, sixty-six prototype energy models for twenty-two building types of three vintages were selected to represent 59,332 buildings in Changsha. The total electricity consumption of the building stock was obtained by aggregating the prototype electricity use intensity with their representative building area. Three DR strategies were formulated, including temperature reset, lighting control, and plug load control. Second, a toolkit, AutoBPS-DR, was developed to automatically integrate individual DR strategy as well as their combination into the baseline energy models. Five scenarios were analyzed, including the baseline case, three cases with a single DR strategy, and a case with three DR strategies combined. The results showed that compared to the baseline scenario, the electricity consumption reductions of the case buildings for a 3-h DR event under temperature reset, lighting control, plug load control, and combined control scenarios were 603.97 MWh (7.94%), 441.08 MWh (5.80%), 663.26 MWh (8.72%) and 1673.32 MWh (22.00%), respectively. In the plug load control scenarios, high-rise apartments made the largest contribution with a reduction of 134.15 MWh. Medium office buildings had the most reduction under the other three scenarios, with a reduction of 122.16 MWh for temperature reset, 107.90 MWh for lighting control, and 357.16 MWh for combination control. This study demonstrated a significant DR potential for buildings in Changsha to overcome the electricity shortage period. [ABSTRACT FROM AUTHOR]

Several studies have indicated that Economic Model Predictive Control (EMPC) of residential space heating systems can exploit the building thermal mass to provide demand response for the energy supply system by boosting the indoor air temperature prior to peak load periods. The practical uptake of EMPC is thus dependent on whether residents accept the fluctuating indoor air temperatures induced by this form of EMPC. Evidence on this aspect is scarce but a few studies indicate in hindsight that it might be important to explain the economic and environmental benefits of an EMPC if residents are to abandon their current preferences and practices in favour of an EMPC concept. The objective of the study reported in this paper was therefore to investigate whether residents of three single-family homes actually would accept the fluctuating indoor air temperature induced by EMPC when being aware of environmental and/or economic benefits. The results suggest that residents are willing to accept the fluctuating indoor air temperature being aware of economic and environmental benefits – as long as the fluctuations do not make them feel too cold. [ABSTRACT FROM AUTHOR]

• ESS degradation cost based on battery lifetime is considered for power management of nanogrid with PV system. • Multi-objective optimization representing power management determines individual power consumption of schedulable electric appliances and ESS charging/discharging. • P2P power trading is performed between nanogrids based on cooperative game model. • ESS degradation cost is decreased by 32.75% by the proposed power management. This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average. [ABSTRACT FROM AUTHOR]

• Different renovation intervention levels are evaluated. • The technoeconomic feasibility by itself does not explain the actual renovation rates. • Existing social, material and legal barriers are key for a comprehensive analysis. • Measures should be taken to boost district/building level interventions. Building is one of the main sectors in which significant energy savings can be achieved with a consequent reduction in emissions. This paper assesses the large-scale renovation of buildings, exploring the quantitative and qualitative factors that determine their replicability potential from three different perspectives, namely dwelling, building and district intervention level. Different passive and active measures are assessed, covering energy saving measures, the improvement of energy supply systems and the integration of renewable energy sources. Different scenarios are defined for each intervention level, which are evaluated using the methodology developed in the IEA-EBC Annex 75 project. The methodology is applied to a residential district located in Bilbao (Northern Spain). The analysis is based on the results obtained from the simulation of 41 combinations of different renovation options, obtained by simulation in the Design Builder software. The assessment is carried out from the evaluation of different key performance factors, including annualised cost and annual primary energy consumption, as well as the CAPEX and OPEX requirements. The results show that energy renovation of buildings offers a great opportunity for energy reduction at affordable investment costs, obtaining the cost optimal values when the intervention focus on the energy system and reaching net NRPE values close to zero when the intervention is carried out in a comprehensive way, considering energy systems and thermal improvement of the envelope. Additionally, the different energy reduction scenarios show that, while interventions at district level offer the greatest potential for minimising annual costs and primary energy consumption levels, intervening at building level offers only slightly worse results. However, there are other issues which can better explain the current low rate of renovation works in the urban environment. Thus, if the additional existing social, material and legal barriers and constraints are included in the analysis, it becomes clear why renovation from a district perspective is not actually leading the race. Given this situation, some measures and policies are proposed to realise the true potential of large-scale building renovation. [ABSTRACT FROM AUTHOR]

Significant research effort has gone into developing bottom-up, physics based, urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to reduce the simulation inputs required in UBEM, groups of buildings are typically classified into representative “archetypes”. This approach necessarily simplifies the real diversity of occupant behaviors and construction variations, reducing in turn the variety of predicted building energy demands. To further understand the limitations of archetype-based modeling, this paper evaluates four characterization methods of increasing complexity, by comparing simulation results to measured Energy Use Intensity (EUI) distributions of 336 residential buildings in a district in Kuwait City. The first two methods follow a typical deterministic approach, organizing buildings into one and four archetypes respectively, while the third method introduces probabilistic uncertainty modeling for occupant related parameters. The fourth method introduces a new “Bayesian” calibration technique for archetype parameters, and applies it to occupant parameters in method three. In a first step, frequency calibrated distributions for occupancy, lighting power density, plug load density and cooling set points are generated through parametric modeling from a test set consisting of about half of the 336 buildings. The resulting joint distribution of parameters is then sampled using Latin Hyper Cube (LHC) for the probabilistic simulation of the remaining urban model. The comparison of simulated EUI distributions with measured data suggests that the proposed probabilistic Bayesian calibration leads to significantly closer predictions, with a maximum error in the 10th and 90th percentiles of 15% against 55%, 60% and 29% for the other three methods. [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]

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]

The paper presents an integrated assessment model of an energy supply system for an energy-efficient building (ESSINTEGRA). This assessment comprises a comprehensive analysis of the building energy demand and energy supply systems as well as determining a rational solution on the basis of the selected assessment criteria: energy efficiency, environmental impact, economic, comfort and technical functionality. ESSINTEGRA is applied for a case study of a single family house. Six different alternatives of the house envelope parameters and 15 combinations of the technological solutions of the building energy supply systems (BESS) are explored as design options. Applying the model has allowed to determine the rational solution for the energy-efficient single family houses in Northern European climate (zone 5). When the combination of the wood boiler with a solar collector system is used, the primary energy demand of 10.1 kWh/m 2 , CO 2 gas emissions of 0.24 kgCO 2 /m 2 and the renewable energy fraction of 97.4 % can be obtained. When the decision making method is applied, the reliability of the rational solution amounts to 99.5%. The sensitivity analysis has shown that the variation of the criteria significances has no influence on the reliability of the rational solution. [ABSTRACT FROM AUTHOR]

Application of Thermoeconomics in the diagnosis of building energy supply systems allows the localization and quantification of an abnormal operation, which can cause significant increase in energy consumption to obtain the same product. To do that, a comparison between the real operation of the energy system and the reference operation must be done. In this paper a DHW and heating plant based on a condensing boiler has been analysed. Diagnosis is applied through a mono-failure case using a methodology based on the fuel impact formula and the malfunction and dysfunction analysis. An innovative methodology will be presented in order to filter the induced effects that take place in a dynamic situation due to the control system. Although the real aim of diagnosis is to solve the inverse problem, in this work we have faced a direct problem, in which a parameter of one component of the plant has been modified and its effect over the other components and the whole plant is evaluated. The major outcome is that by the application of this methodology the effects produced by the faulty component can easily be quantified, both in performance and economic terms. Those effects are due to the component malfunction as well as the induced malfunctions and dysfunctions generated in the other components of the facility. [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]

While power crises persist in Africa with many heads of governments placing priority on power generation, the need for energy conservation through efficient lighting can be a short-term solution. This paper compiles the power supply and consumption pattern from lighting of six cities and towns in North Central Nigeria. A total of 1637 residential households were surveyed. Each compound had 2 to 4 houses. Most of the households surveyed had a combination of modern and local buildings such as huts. The work revealed that the predominant form of electricity consumption is in lighting. Electricity supply is found to last for an average of 5 days a week and 9.8 h a day. The major electric lighting source is the 60 W and 100 W incandescent bulbs, but a significant population uses both incandescent and energy efficient lamps (e.g. compact fluorescent lamps, CFLs) for their lighting needs. In the absence of public power supply, kerosene and low power generators are used as alternative sources for lighting. The number of kerosene lamps in a residential compound was found to be 3 or more. About 70 per cent of power generated in Nigeria can be saved if efficient lighting sources such as CFLs and solid state lighting are urgently adapted. Also, an estimated 796.4 billion naira (US$4.98 billion) will be saved annually from fuel to power electric generators if they are replaced by solar- based lighting. [ABSTRACT FROM AUTHOR]

Load forecasting plays an important role in energy management in smart buildings. It is expected that precise prediction of loads can bring significant economic benefits to smart buildings by enabling accurate demand response strategies for peak load reduction, reducing electricity use and integrating distributed energy resources. The complexity increases for load prediction in case of a district of buildings having much functional diversity and including several heterogenous buildings-blocks. The main goal of this paper is to present a comprehensive and detailed study for very-short term and short-term load forecasting in a district building using artificial neural network (ANN). The main objectives of this paper are: (a) Evaluate the performance of the ANN considering two back-propagation learning algorithms, namely Bayesian regularization (BR) and Levenberg-Marquardt (LM); (b) Analyse the relative performance of the model for hour-ahead and day-ahead load forecasting for different types of buildings; (c) Investigate how the network design parameters such as number of hidden layers, hidden neurons, number of inputs and training data affect the model's ability to accurately forecast loads. In order to demonstrate the efficiency of the proposed approach, it is examined on real-world data of a Campus in downtown Montreal that includes many types of buildings. [ABSTRACT FROM AUTHOR]

Microgrids play an essential role in the integration of multiple distributed energy resources in buildings. They can meet critical loads in buildings while reducing peak loads and congestion and providing other types of ancillary services to the main electrical grid. Sizing the components of microgrids and scheduling their optimal operation while jointly integrating uncertain renewable energy generation and loads can significantly affect its ability to meet these objectives. In fact, microgrids face great challenges due to renewable energies and load uncertainties. In this paper, a two-stage stochastic programming model for optimal sizing and operation of various distributed energy resources for peak load shaving in district buildings is proposed. The first stage is related to the planning of photovoltaic panels and batteries, while the second stage aims to find the optimal operation of the system in grid-connected mode. The uncertainties are related to photovoltaic power generation and loads. The Generative Adversarial Network (GAN) is implemented to generate the scenarios of uncertain parameters, and the k-medoids classical method for scenario reduction is applied to decrease the number of scenarios. The proposed two-stage stochastic optimization is tested for a real case study of a university campus in Canada. [ABSTRACT FROM AUTHOR]

The advantages in applying the exergy concept and exergy efficiencies to evaluate the performance of energy systems in the building environment, contributing to the reduction in the use of high quality energy sources is currently known. Although Thermoeconomics has been widely applied in energy systems at building level Symbolic Thermoeconomics has been rarely used. This paper presents a review of Symbolic Thermoeconomics as a technique, based on the Exergy Cost Theory, to obtain general equations which relate the overall efficiency of an energy system and other thermoeconomic variables, such as fuel, product, exergy cost or exergoeoconomic cost, with the efficiency of each component, the total production and with certain coefficients which reflect the structure of the system. A case of a simple DHW facility is presented and Symbolic Thermoeconomics is applied in the demand-driven model to obtain general formulas which are valid for any state of the system, explaining the cost formation process. A TRNSYS simulation of the plant is performed and the results obtained are applied in order to obtain numerical values of the thermoeconomic variables and particularly the production costs which are decomposed into its different contributions, due to the external resources, equipment investment and waste generation. [ABSTRACT FROM AUTHOR]

The most common parameter to evaluate how close a building is from the Net Zero Energy Building (Net ZEB) is the non-renewable primary energy balance. But two other performance indicators can be complementary in this evaluation: the renewable energy ratio (RER) and the on-site energy fraction (OEF). To overcome the identified weaknesses of these indicators, a methodology to calculate the overall renewable energy fraction (OREF) is presented in this paper, which consists of an extension of OEF including the off-site renewable energy. The inclusion of the on-site generated energy that is exported, applying different weighting factors is discussed. The results show that the more consistent approach is not crediting the exported energy in OREF. This conclusion is sustained by the fact that (i) the option of payback credit, without considering embodied energy, could theoretically lead to OREF above 100% and (ii) OREF becomes redundant with the non-renewable primary energy balance for an avoided burden approach. Results also revealed that OREF is as an indicator of the independency from fossil fuels energy carriers, direct use or other energy carriers based on fossil fuels, and increases for buildings with higher self-consumption of on-site generated energy, relatively to the exported fraction. [ABSTRACT FROM AUTHOR]

An accurate forecast of natural gas (NG) consumption is of vital importance for economical and reliable operation of the distributive NG networks. In this paper, a structure-calibrated support vector regression (SC-SVR) approach is proposed to forecast the daily NG consumption, which is correlated with the past time series using the SVR model. To better accommodate the dynamic nature of the NG consumption, the structural parameters of the SVR model are online calibrated in response to the receding horizon of the NG consumption series. The calibration of the structural parameters for the next-day forecast is performed by extended Kalman filter. The proposed SC-SVR approach is evaluated using real data collected from a NG company in the period from January to December 2012. The results indicate that the mean absolute percentage error and the root mean squared error are 2.36% and 3913.88 m 3 /d, respectively. To show the applicability and superiority of the SC-SVR approach, two peer methods, i.e., least squares SVR model and dynamic back propagation neural network are also employed for comparison. The results show that, thanks to nonlinear mapping capability of the SVR and dynamic nature of the online calibration for the model structure, the proposed SC-SVR method is capable of improving the forecast accuracy for the daily NG consumption. [ABSTRACT FROM AUTHOR]

This paper provides a novel and efficient approach for the simulation-based multi-criteria optimization of the building energy performance. In the current investigation, a multi-objective non-dominated sorting genetic algorithm (NSGA-II) is coupled with EnergyPlus building energy simulation program to obtain optimal solutions leads to increase the building energy performance. The main aim of this work is to explore the effect of some architectural parameters, including the building orientation, the window size and the overhang specifications on the building energy consumption in four major climatic regions of Iran, e.g. cold, mild, warm-dry, and warm-humid. In the optimization section, mono-criterion and bi-criteria optimization analyses of the annual cooling and lighting building energy demands are studied with the purpose of understanding the interactions between these objective functions. The results point out that the bi-criteria optimization of the test case model brings down the annual cooling energy consumption 55.8–76.4% in various climates. However, the annual lighting electricity demand increases 1–4.8%. As a result, the final optimum configuration leads to 23.8–42.2% decrease in the annual total building energy consumption. The results depict that the climate and the appropriate selection of the architectural parameters are very important and critical in reducing the building energy consumption. The proposed optimization methodology is a powerful and useful tool to achieve this goal and to facilitate decision-making in early phases of a building design in order to enhance its energy efficiency. [ABSTRACT FROM AUTHOR]

The large public building energy consumption (BEC) is the focus of Building Energy Saving, therefore, it is necessary to study the characteristics of BEC to find out the important factors affecting BEC. For this purpose, 27 office buildings in the west of Inner Mongolia Autonomous Region were studied, based on statistical analysis of the researched basic information and energy consumption bill of these buildings. This paper focused on the determination of the significant factors affecting the total and subentry energy consumption intensity (ECI) of office building, as well as the establishment of standardized linear regression models between these selected factors and total and subentry ECI. Firstly, eleven continuous variables, three independent categorical variables, and the climate factor were selected and analyzed the impact on total and subentry ECI by statistical software SPSS20.0, in order to find out the significant influencing factors. Then based on the results of curve fitting, standardized models of total and subentry ECI and their respective significant impact factors were established using multiple linear regression analysis. The regression results showed that the electricity use percentage of the total equivalent electricity consumption was an important factor affecting the total ECI of office building in the west of Inner Mongolia Autonomous Region. Finally, univariate analysis of variance (ANOVA) was conducted between the three independent categorical variables and the total and subentry ECI, and the results showed that these factors had no significant effect on the ECI of office building. Process of the regressive model establishment and the results of analysis of variance could both guide us to propose more targeted energy saving measures. [ABSTRACT FROM AUTHOR]

Occupants’ interactions with the building envelope and building systems can have a large impact on the indoor environment and energy consumption in a building. As a consequence, any realistic forecast of building performance must include realistic models of the occupants’ interactions with the building controls (windows, thermostats, solar shading etc.). During the last decade, studies about stochastic models of occupants’ behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested. In this paper, stochastic models of occupants’ behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor temperature, relative humidity and CO 2 concentration was measured in the living room and bedroom at five minute intervals in five apartments with similar layout in a building located in Copenhagen, Denmark. Outdoor temperature, relative humidity, wind speed and solar radiation were obtained from a weather station close by. The stochastic models of window opening and heating set-point adjustments were implemented in the BEPS tool IDA ICE. Two apartments from the monitoring campaign were simulated using the implemented models and the measured weather data. The results were compared to measurements from the monitoring campaign to get an estimate of the forecast’s realism. The simulations resulted in realistic predictions in a sense that the measured values were within or close to the range of the simulated values. The variation in the simulated and measured variables between apartments and over time was similar. However, comparisons of the average stochastic predictions with the measured temperatures, relative humidity and CO 2 concentrations revealed that the models did not predict the actual indoor environmental conditions well. [ABSTRACT FROM AUTHOR]

Various lighting and blind control methods have been presented to improve user comfort and reduce energy consumption simultaneously. However, there are opportunities to improve control performances by introducing more recent information and machine learning technologies which allow more comprehensive consideration of the balance between user comfort and system energy consumption. To be more specific, in terms of user comfort, unified set-point may not be desirable since different people may have different comfort preferences. In terms of energy consumption, the excessive cooling load of HVAC system should be considered in summer when utilizing solar incidence to reduce the lighting electricity consumption. The setting of the blind slat angle still has great room to improve instead of the cut-off angle. Moreover, users’ demands are not fully met, so sometimes they still want to override the automated control. Thus, a closed-loop satisfaction based system is developed in this paper, specifically we introduce an improved reinforcement learning controller to obtain an optimal control strategy of blinds and lights. It could provide a personalized service via introducing subjects perceptions of surroundings gathered by a novel interface as the feedback signal. The proposed system was implemented on a practical test-bed in an energy-efficient building. Compared with the traditional control, it can provide a more acceptable and energy-efficient luminous environment. [ABSTRACT FROM AUTHOR]

Since the Great East Japan Earthquake and Fukushima nuclear accident in 2011, both energy consumption and CO 2 emissions have been increasing in the residential sector of Japan. For this reason, smart meters have received much attention as a way to provide energy-use feedback to households and thereby encourage energy conservation. In order to provide effective feedback, it is necessary to take into account the lifestyle of each household, but little work has been done to develop a methodology to determine the relationship between a household’s lifestyle and its electricity consumption. This paper proposes two methods that identify a household’s lifestyle from electricity use data. By using a frequency analysis of weekly load profiles, we verified that, except in winter (December 2013–February 2014), the average daily consumption of morning-oriented lifestyles is 5.3% less than that of night-oriented lifestyles. The results of cluster analyses of each household’s daily electric load profiles suggest that most households consume less electricity when following a regular routine. [ABSTRACT FROM AUTHOR]

Energy saving and its efficient utilization is of prime interest in today's world due to the limited energy resources and growing significance of environmental issues. Despite the intensive efforts to narrow the gap between conventional energy sources (wood, coal, gas, oil, etc.) and renewables, renewable energy resources currently supply only about 14% of total world energy demand. In this regard, energy management and optimization are considered compulsory as much as the clean energy generation. Recent works indicate that the buildings play a significant role on global energy consumption. They are responsible for about 40% of global energy demand. Among the different building types, domestic buildings have the largest share with 63% and most of energy is utilized for heating, ventilation and air conditioning ( HVAC ) systems in those buildings. Energy consumption levels of buildings can be notably reduced through waste heat recovery in HVAC systems. There are several attempts in literature addressing the possibility of decreasing energy consumption of buildings via waste heat recovery technologies. The heat recovery technologies are cost effective and user friendly applications. The use of heat recovery systems aims at mitigating the energy consumption for HVAC applications as well as the greenhouse gas emissions, and hence decreasing the adverse effects of global warming on the Earth. It is well-documented in literature that the heat recovery systems are very promising for domestic applications. In this paper, experimental results of a novel heat recovery system developed for low-carbon buildings are presented. The proposed heat recovery system consists of a plate-type heat exchanger, blower fans and ducts. The parallel-flow arrangement is used to run the system. The system is designed as under roof application. The aim of the system is to recover waste heat and to preheat fresh air using stale air. The experiments of the system are carried out in winter season in Kent, UK. The study aims to investigate the coefficient of performance ( COP ) of the system as well as the heat recovery efficiency. The results show that the heat recovery efficiency of the proposed system is around 89% while the COP is 4.5. The proposed system can be used in both winter and summer conditions without requiring additional work. Its labor cost is extremely low, so it is cost-effective and user friendly. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • The influencing factors on energy efficiency of data center are reviewed. • Energy efficiency metrics are classified according to different priorities. • Appropriate metrics should be selected according to data center function for optimization. • Metrics are analyzed by hierarchy according to the degree of sustainable development. • Challenges and gaps are highlighted and suggestions are given for future works. The rapid development of the Internet requires data center infrastructures to be extended to a large scale with a higher power capacity and a reduction in energy consumption and carbon emissions. It is of great significance for this application to reduce energy consumption, capital cost and carbon emissions while ensuring a safe and reliable data center operation. Evaluation metrics have been considered an effective method to assess the energy efficiency of data centers and have been widely used in various data centers for many years. This work reviews the latest progress of data center evaluation metrics development in recent 20 years. Energy efficiency metrics are summarized based on energy conservation, eco-design and data center security, with the advantages and disadvantages and their correlations discussed. The applicability of each metric is analyzed. The influencing factors of energy efficiency of data centers discussed include site selection, IT equipment, power supply and distribution systems, cooling systems, building structure, and temperature control. The results show that the current evaluation metrics are valuable for the assessment of energy saving of data centers. However, more comprehensive and multi-scale evaluation metrics combining energy consumption, eco-design and data center security are needed to accelerate the progress towards carbon neutrality. In this paper, the hierarchy analysis of metrics is carried out according to sustainability, and metrics in line with sustainable development should be used preferentially. [ABSTRACT FROM AUTHOR]

• Relative and absolute home retrofit targets are reviewed and discussed. • Retrofit depth is determined by policy targets or practical cost-effectiveness. • Research studies find feasible energy savings of around 60% maximum. • Neighbourhood is optimal spatial scale to large-scale deliver home energy retrofit. • Future retrofits to be phased instead of one-off, and less primacy of fabric first. Home energy retrofit has been a publicly-funded policy in many countries since the 1990s. Government motivations have spanned from combating energy poverty to climate action and sustainability. Given the ambition of public policy, this paper reviews the targets, spatial scales and metrics used to deliver and evaluate home energy retrofit. Large-scale retrofit projects do differ: by spatial area, by retrofit depth and in evaluation metrics (if used). This review surveys first, the quantities of homes and inhabitants in intermediate spatial scales used to plan retrofit programmes. Second, the optimal spatial scale and retrofit depth to deliver large-scale home retrofit. Third, the quantitative metrics to evaluate retrofits, and future trends in occupant-based metrics. Neighbourhood is found to be the optimal spatial scale for home energy retrofit. Deep retrofit is described as 60% energy savings that, in synergy with energy supply decarbonisation, promises to deliver 80% or higher emission reductions. Recent evidence shows a shifting primacy from fabric-first retrofit to decarbonisation of heat generation and occupant health and well-being. [ABSTRACT FROM AUTHOR]

In this paper, district heating is described and its operation process is considered in varying operation conditions over time. Taking into account the ordered thermal power which affects the amount of heat supply to consumers, the system operation states are introduced. Heat supply to consumers is carried out by means of a heat-carrying agent having the necessary parameters: required quantity, temperature and pressure, in a specific location, under certain operational conditions and timeframe. To model the system operation process and describe the changes between the operation states the semi-Markov process is applied. The identification of the district heating operation process is performed on the basis of real operational data obtained from the District Heating Company in the city of Rzeszow. Using the data and considering the specific character of the system, the main characteristics describing the safety of heat supply to consumers were determined. [ABSTRACT FROM AUTHOR]

Energy consumption data are required to perform analysis, modelling, evaluation, and optimisation of energy usage in buildings. While a variety of energy consumption data sets have been examined and reported in the literature, there is a lack of a comprehensive categorisation and analysis of the available data sets. In this study, an overview of energy consumption data of buildings is provided. Three common strategies for generating energy consumption data, i.e., measurement, survey, and simulation, are described. A number of important characteristics pertaining to each strategy and the resulting data sets are discussed. In addition, a directory of energy consumption data sets of buildings is developed. The data sets are collected from either published papers or energy related organisations. The main contributions of this study include establishing a resource pertaining to energy consumption data sets and providing information related to the characteristics and availability of the respective data sets; therefore facilitating and promoting research activities in energy consumption data analysis. [ABSTRACT FROM AUTHOR]

Currently, there is no clear and unified understanding about the status quo of China's energy consumption in the building sector. In addition, a considerable underestimation of energy associated with buildings has impeded the effective implementation of measures to improve building energy efficiency of China. Thus, in this paper, we seek to identify the building sector's energy consumption of China by establishing an estimation model of building energy consumption from a life cycle perspective. On the basis of macro-level statistical data and relevant literature, we analyze the activities in each phase and calculate associated energy consumptions throughout buildings’ whole life cycle in China from 2001 to 2013. The results show that China's energy consumption associated with buildings has reached 1.66 billion tons coal equivalent in 2013, with a stable growth rate of 7% annually since 2001. Buildings’ life-cycle energy has approximately accounted for 43% of China's total energy consumption for recent three years (2011–2013). What's more, energy consumption in buildings’ operation phase has been salient, accounting for over 20% of China's total energy consumption. More focus should be drawn on energy efficiency in building material production phase and energy consumed in China's rural residential buildings as both have been significantly neglected. [ABSTRACT FROM AUTHOR]