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Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

Abstract In 2011, the Danish government published an energy strategy for Denmark where one of the main targets are that Denmark should be a fossil-free society by 2050. Calculations show, that in order to reach this goal it is necessary to reduce the energy consumption of the existing building stock by 50% on average. Since a 50% reduction is obviously not possible for all buildings, those that can should aim for a so-called "deep energy renovation", i.e. reducing the energy consumption to a level corresponding to that of new buildings or even more. This paper describes two case studies where multi-story apartment buildings have undergone deep energy renovation. "Traneparken" where the expected energy use after renovation aimed at a level corresponding to that of a new building according to the Danish Building Regulations from 2015. "Sems Have" where the aim was to go even further and meet the requirements expected for new buildings in 2020, i.e. corresponding to the Danish "nearly zero energy"-definition according to the EPBD. The paper reports on calculations and measurements of energy savings, the economy of the projects and looks at the added benefits or co-benefits that residents, housing association and society in general have achieved in addition to significant energy savings. [ABSTRACT FROM AUTHOR]

Highlights • A total of 925 papers were searched and 106 articles were screened for deep review. • Green buildings generally performed better than the conventional counterparts. • A significant gap between the designed and operational energy use was found. • The key findings of objective IEQ and occupant satisfaction were summarized. • The performances of green buildings in China and the U.S. were compared. ABSTRACT This paper aims to review the published researches on post-occupancy performance of green buildings in terms of energy use, indoor environment quality (IEQ) and occupant satisfaction. A total of 925 related papers were searched and 106 articles were screened for deep review. The study also compared the actual operating performance of green buildings in China and the United States, with information collected from previous research. The findings of literature review can be summarized as follows: 1) Though with some exceptional cases, the energy performance of green buildings was on average better than the conventional counterparts with similar climate conditions; 2) A significant gap between the designed and operational energy consumption was found, and lots of green buildings saved less energy than expected; 3) No clear relationship between the actual energy use and the green building certification level could be observed; 4) The actual IEQ conditions of green buildings were not comparable in different countries due to the variance in design criterions and operation modes; 5) Green buildings generally had higher occupants' satisfaction level than conventional buildings, although the subjective data from the U.S. did not support this inference. Furthermore, this study proposed two areas worthy further investigations in the field of post-occupancy evaluation on green buildings, including new data collection technologies and performance optimization at the comprehensive level. [ABSTRACT FROM AUTHOR]

Highlights • This paper uses building structural attributes such as shapes of buildings, relative compactness(RC), glazing area(GA), roof area(RA), surface area(SA), wall area(WA), orientation(OR), overall height(OH) and glazing area distribution(GAD) for prediction of heating and cooling load. • The paper explores the data set and features to get insights with feature correlation, mutual information their association strengths etc and their relation with heating and cooling load. • The paper proposes novel group of methods based on emerging machine learning method extreme learning machine(ELM) which has shallows architecture. • The paper proposes novel method based on ELM variant online sequential extreme learning machine(OSELM) for heating and cooling load prediction in online and adaptive environment. This model also helps when data is trained in chunk by chunk fashion. • The paper demonstrates several models based on combination of learning mode, activation function and features sets and compare their results with existing work extensively. Abstract In the present day environment, smart buildings require optimization of energy consumption through monitoring, consumption prediction and making policy decisions accordingly. Attributes related to building design and structure play a vital role in heating load(HL) and cooling load(CL) of the building which directly affects the energy performance of the buildings. For prediction of HL and CL, emerging machine learning approaches can help in improving accuracy and efficiency in real time. This paper provides improvements in energy load assessment of the buildings. It is the first is the in-depth study and analysis of design and structural attributes and their correlation with HL and CL, the novel methods based on ELM and its variants online sequential ELM(OSELM) to predict HL and CL. This study also proposes OSELM based online/real-time prediction when data is coming in stream The total 24 models have been developed including 12 models based on ELM and 12 models based on OSELM with different feature sets and activation functions. Models have been compared on the basis of accuracy, computational performance and efficiency with few existing models. The experimental results show that the proposed models learn better and outperform other popular machine learning approaches such as the artificial neural network(ANNs), support vector machine(SVM), radial basis function network(RBFN), random forest(RF) and existing work in the energy and building domain. [ABSTRACT FROM AUTHOR]

[Display omitted] • A split-degree days index is proposed for building energy use/climate classification. • Split-degree days index is shown to better estimate solar radiation and humidity data. • The estimation of building energy end-uses is improved using the split-degree days. • Split-degree days also showed improved results for high thermal mass and 24-hr models. • Split-degree days index maintains a high accuracy for varying base temperatures. Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation. [ABSTRACT FROM AUTHOR]

The use of radiant technology in attics aims to reduce the radiation component of heat transfer between the attic floor and roof decks, gables, and eaves. Recently, it has been shown that EnergyPlus underestimates the savings using radiant technologies in attic spaces. The aim of this study is to understand why EnergyPlus underestimates the performance of radiant technologies and provide a solution strategy that works within the current capabilities of EnergyPlus. The analysis uses three attic energy models as a baseline for comparison for EnergyPlus. Potential reasons for the discrepancies between the attic specific energy models and EnergyPlus are isolated and individually tested. A solution strategy is proposed using the Energy Management System (EMS) capabilities within EnergyPlus. This solution strategy produces similar results to the other attic specific energy models. This paper shows that the current capabilities of EnergyPlus are sufficient to simulate radiant technologies in attics. The methodology showcased in this paper serves as a guide for engineers and researchers who would like to predict the performance radiant technology in attics using the whole building energy software, EnergyPlus. [ABSTRACT FROM AUTHOR]

In a traditional building design process (TDP), design variables are fixed sequentially, as opposed to integrated design process (IDP) which tends to avoid sequential design phases to create more sustainable buildings. First, a reference building is introduced and an energy model based on TRNSYS is presented to determine the energy consumption and comfort in the building. The model is validated based on energy bills, certified simulations and literature. Then, the paper performs an extended sensitivity analysis (SA) of 30 design variables with respect to different performance criteria related to energy consumption and comfort, based on a TRNSYS model. Three SA techniques were used, namely standard regression coefficients (SRC), partial rank correlation coefficients (PRCC) and Sobol indices. Results show that all three techniques yielded a similar ranking of the importance of the variables for most model outputs. Interactions between variables were identified with second-order Sobol indices. In the second part of this paper, a traditional design framework was adopted in which sets of variables were fixed sequentially. A SA was performed at each phase of the process, assuming fixed values for parameters chosen in previous design phases. Results show that fixing variables during the phases of a traditional design process tends to reduce the probabilities of finding low-energy consumption designs. Moreover, the influence of some variables was found to change during the design phases. [ABSTRACT FROM AUTHOR]

Reducing energy consumption in buildings and increasing renewable production are key goals of European policies to achieve a sustainable and competitive low-carbon economy by 2020 and beyond. Non-residential buildings constitute a heterogeneity sector characterized by high energy consumption and various building types, sizes and energy characteristics over Europe. This paper presents the overall results of the data collected by the GreenBuilding Programme (GBP), launched in 2006 to promote and improve energy efficiency in new and existing European non-residential buildings. The GPB involved building owners willing to adopt energy efficiency measures to decrease energy consumption of their buildings by of at least 25%. Based on voluntary participation, hundreds of partners joined the project, which collected data from more than a thousand buildings of different age, size, use and type (such as offices, hotels, and industry). This paper provides an overview of the Programme and its main results up to its completion in 2014. The paper focuses on building characteristics, energy performance, efficiency measures and energy savings, which are globally estimated to be around 985 GWh/year. A more detailed focus is then given to office buildings, which represent the most frequent building category in the Programme. Case studies are presented to show best practices in various countries with consolidated energy efficiency policy strategies. The paper categorises the main technological measures related to envelope, appliances and systems. It shows how a wide range of technologies are becoming an integral part of buildings and how technology plays a major role in exploiting the massive potential benefits of reducing building energy consumptions. The analysis of the results generates a reliable snapshot of European non-residential building stock. [ABSTRACT FROM AUTHOR]

There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [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]

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities. [ABSTRACT FROM AUTHOR]

• A hybrid appliance classification model is proposed for the feature extraction in the electrical power savings of buildings. • The strategy of time series is presented in the proposed appliance classification. • A secondary ensemble structure is proposed to handle the class imbalance multiclass classification problems. • The random forest algorithm is selected as the binary classifiers in the secondary ensemble structure. • The effectiveness of the proposed model is verified on the extended PLAID dataset. The non-intrusive load monitoring, which gets the energy usage information of each appliance by analyzing the aggregative signal in the centralized point is beneficial to electrical power savings of buildings. As an important step in the non-intrusive load monitoring, the appliance classification has gained great attentions. The feature extraction, the design of classification structure and the selection of classifiers are three important parts in the appliance classification. In this paper, a hybrid appliance classification model is proposed. In the hybrid model, the time series features are utilized to depict the characters of appliances, which is different from the conventional perspective of feature extraction. A secondary ensemble structure is designed to handle the class imbalance multiclass classification problems. The class imbalance is often the case in appliance classification but no effective solutions has been put forward yet. Besides, the random forest is selected as the binary classifiers in the secondary ensemble structure. The effectiveness of the proposed model is verified on the extended PLAID dataset. The experimental results indicate that the time series features provide better overall performance compared to the widely used features, the random forest performance better than the widely used classifiers and the overall performance of the proposed model is better than that of the state of the art model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

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

Highlights • The paper studies the impact of urban parks on reducing urban temperature. • Findings reveal that urban park construction reduces building energy consumption. • There is a limit distance to the reduction of energy consumption due to urban parks. • Change of urban characteristics due to park construction affects energy consumption. Abstract A change in the thermal environment of an urban area affects health, living conditions, and energy consumption. In urban planning, urban parks are one of the methods for improving the thermal environment and saving cooling energy. Urban park construction can mitigate temperature, but it also causes urban development by increasing local attractiveness. To achieve efficient energy saving through parks in urban planning, the purpose of this study is to investigate the relationship between building energy consumption and urban characteristics both before and after the construction of an urban park. This study targeted Seoul's Gyeongui line forest, which was recently converted into a linear park on the former railway as an urban regeneration project. We analyzed the relationship between energy consumption and urban characteristics using a regression model, focusing on the changes before and after the construction. In this study, urban characteristics include environment, building physical characteristics, and economic variables. The results show that the construction of the urban park reduced not only temperature but also building energy consumption. The energy reduction effect of the park was limited to a marginal distance. Meanwhile, the urban park construction caused land prices to rise and prompted new development, and this changed the urban characteristics of the area and affected energy consumption. Despite changes in urban characteristics, urban park planning is an effective methods of reducing the energy consumption involved in cooling urban areas. We recommend comprehensive consideration of the urban factors when making park policy to reduce urban temperature and energy consumption. [ABSTRACT FROM AUTHOR]

In Germany, policy-induced energy efficiency improvements typically aim at reducing primary energy consumption. Private households, on the contrary, pursue the maximization of wellbeing or, in microeconomics jargon, the maximization of utility of the occupants. There is a marked difference between upfront-calculated energy performance ratings (EPRs) and actual heat energy consumption (HEC). From an energy and environmental policy point of view, deviations of energy consumption from ex ante calculated EPRs is problematic, as it jeopardizes their usefulness for (prospective) homeowners, policy-makers and researchers relying on EPRs as a guidance. The EPR-HEC gaps reported are, apart from deviations from the mean aggregate values (i.e. heterogeneity), often attributed to anticipated or unanticipated behavioral effects. From an energy economist's point of view, energy rebound induced by lowered costs per unit of energy service due to increased energy efficiency is one explanation. The related literature almost entirely treats building-specific EPRs as universal standards, trying to explain the empirically observed discrepancies. In this paper, we investigate to what extent the current EPR scheme in place in Germany today can address behavioral issues. To this end, based on two different multi-family housing data sets for Germany and regression analysis, we empirically investigate the deviations between EPRs used in the regulatory norms and the HEC levels observed in reality. We find evidence that it is not necessarily the behavioral dimension, but rather the static and mostly technically guided calculation of the EPRs itself that accounts for the major part of the deviations. We conclude that there is indeed a need for further improvements in the field of EPR regulation and methodology. [ABSTRACT FROM AUTHOR]

In the US, building sector consumes approximately 41% of all U.S. primary energy and 70% of all generated electricity. Office building uses the largest percentage of primary and derived energy in the commercial buildings sector. Therefore, energy saving is the primary target of building renovation and equipment retrofit. To estimate energy savings after a renovation or equipment retrofit, measurement and verification (M&V) must be conducted based on energy use models or benchmark tools to track and assess savings. Although some methods and modeling tools are applied more often, there is no well-accepted model formulation methodology and some methods have high uncertainty. This paper discusses a method to formulate the energy use model as an inverse regression model via an advanced and robust high-dimensional data analysis method. According to the inverse regression model, independent variables determined to be most influential to the energy consumption should be metered prior to building retrofit or audit. To select the most influential variables, this research considers a comprehensive set of potential key variables to avoid underspecified model issue. The models established by this method are observed to have great power in predicting the building energy consumption. More importantly, through the variable selection procedure, the method identifies key variables that should be monitored to continuously improve the building energy performance. A simulation study and a case study are presented to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

• The solar louver was proposed to increase direct heat gaining for building. • Effects of blades' angle and characteristics on heat gain of solar louver was quantified. • Indoor temperature was higher and temperature field was more uniform with coupled solar louver. • By comparison with non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%. Promoting solar energy for heating is of great significance for achieving the national goals of "double carbon" goals. Increasing the heat gain of window space could improve the solar energy utilization in congested urban environments. The paper proposed a new heating type for active solar house by solar louver. Thermal effects validation on solar louver were studied by field-test and simulation analysis. A solar louver model was established on the platform of MATLAB and TRNSYS. The simulated results showing that heat gain of solar louver reached the largest when blades' angle was about 10°. Solar louver with high reflectivity and low thermal emissivity could capture more heat gain. The in-situ experiments of active solar house coupling solar louver were carried out in Ordos. From 24th to 29th Jan. 2022, all-day comparative experiments of thermal effects in actual winter-weather were conducted. The experimental results indicating that solar louver in maximum light transmission mode was preferred in active solar house. Compared with the condition of solar louver in overlapped light transmittance mode, maximum light transmission mode could obtain 17.7% higher heat gain. Indoor temperature was more higher and temperature field was more uniform. Moreover, by comparison with the non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%, which could reduce energy consumption of building. The main work here provided a theoretical basis for performance optimization of active solar house. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

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

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input–output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. [ABSTRACT FROM AUTHOR]

Accurate building energy prediction plays an important role in improving the energy efficiency of buildings. This paper proposes a homogeneous ensemble approach, i.e., use of Random Forest (RF), for hourly building energy prediction. The approach was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida. The RF models trained with different parameter settings were compared to investigate the impact of parameter setting on the prediction performance of the model. The results indicated that RF was not very sensitive to the number of variables ( mtry ) and using empirical mtry is preferable because it saves time and is more accurate. RF was compared with regression tree (RT) and Support Vector Regression (SVR) to validate the superiority of RF in building energy prediction. The prediction performances of RF measured by performance index (PI) were 14–25% and 5–5.5% better than RT and SVR, respectively, indicating that RF was the best prediction model in the comparison. Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters. [ABSTRACT FROM AUTHOR]

In this paper, a model of homeowners’ energy efficiency retrofit choices is presented based on the database of a project implemented by the national energy efficiency retrofit scheme in Croatia during 2015 and 2016. The analysis was performed on the cleaned dataset of 4610 implemented projects in privately owned family houses. We have analyzed available data to answer how much money consumers are willing to pay for energy refurbishment and how this corresponds to household characteristics. We have developed linear regression models for estimating costs and energy savings of different measures and have further applied multinomial and nested discrete choice models on investment choices made by homeowners. The resulting models provide estimations of willingness to pay for energy efficiency refurbishment in private family houses in relation to income class the owners belong to. The results show that homeowners in all income classes have a similar level of willingness-to-pay for similar energy efficiency measures, with main differences being their capability to invest. With the current level of subsidies, only three of four observed measures show a significant level of free-riders. This suggests there could be a better allocation of public funds, where less money could be spent for subsidies to generate a similar amount of savings. Furthermore, the proposed model replicates existing experience and provides insight into inconsistencies which appear when the effectiveness of implemented measures is analyzed. [ABSTRACT FROM AUTHOR]

Large scale smart meter deployments have resulted in popularization of sensor-based electricity forecasting which relies on historical sensor data to infer future energy consumption. Although those approaches have been very successful, they require significant quantities of historical data, often over extended periods of time, to train machine learning models and achieve accurate predictions. New buildings and buildings with newly installed meters have small historical datasets that are insufficient to create accurate predictions. Transfer learning methods have been proposed as a way to use cross-domain datasets to improve predictions. However, these methods do not consider the effects of seasonality within domains. Consequently, this paper proposes Hephaestus, a novel transfer learning method for cross-building energy forecasting based on time series multi-feature regression with seasonal and trend adjustment. This method enables energy prediction with merged data from similar buildings with different distributions and different seasonal profiles. Thus, it improves energy prediction accuracy for a new building with limited data by using datasets from other similar buildings. Hephaestus works in the pre- and post- processing phases and therefore can be used with any standard machine learning algorithm. The case study presented here demonstrates that the proposed approach can improve energy prediction for a school by 11.2% by using additional data from other schools. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid Logarithmic Mean Divisia Index (LMDI) model that decomposes the change of total energy consumption (TEC) in the building sector to three different dimensions: 1) key influencing effects that include area effect, structure effect, population intensity, value intensity, and energy intensity; 2) three phases that include material production, construction, and operation; and 3) geographically distributed regions. Then, the model was validated in China's building sector based on the data collected from 30 provinces during 2007–2015. The key findings show that: (1) the increased final energy consumption in the building industry accounted for 53.3% of that in China. (2) Building material production contributed the most (69.2%) to the energy changes, followed by building operation (29.6%) and construction (1.2%). (3) The change of TEC was attributed the most to the area effect (90.4%) and the energy intensity effect (42.4%). (4) Zhejiang, Jiangsu, Hubei, Hebei, Fujian and Shandong contributed the most (47%) to the increase of China's building energy use. These findings provide an insightful understanding of the building energy changes and also a quantifiable justification for making building-specific energy policies, which are discussed at the end of the study. [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]

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]

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]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [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]

• Building codes require about 20 powered safety devices in new U.S. homes. • Health and safety devices consume about 1% of U.S. residential electricity. • Safety devices in the U.S. are typically exempted from efficiency regulations. • Per-unit consumption is small, but each home will have many units. • Home medical devices are a growing use of energy. Miscellaneous electrical loads (MELs) are responsible for a significant fraction of energy consumption in buildings. This paper addresses one category of MELs: Safety, Security, and Health Devices (SSHDs). Common SSHDs include: electrical life safety equipment, smoke alarms, radon mitigation fans, and home oxygen concentrators. The installation or use of these devices is dictated by building codes, health providers, insurance companies, and other entities—none of which would ordinarily consider energy efficiency a priority. For this project, the most important residential devices were explored in terms of their governing regulations, functions, technologies, and energy use. The power consumption of 41 life safety SSHDs were measured. Individual power consumption of life safety devices is very small but many are typically required in every home. Devices performing the same functions have wide ranges in power use. Opportunities for reducing SSHD energy use were investigated, the results are summarized, and recommendations are made for further actions that could be taken to reduce SSHD energy use. SSHDs identified in this paper are responsible for at least one percent of current U.S. residential electricity consumption, but that number will climb steadily as existing buildings are upgraded and new types of SSHDs appear. [ABSTRACT FROM AUTHOR]

Policy makers have realized that without significant increases in the energy efficiency and notable reductions in the energy use of the household sector, it will be impossible to lower carbon dioxide emissions and mitigate the risks of climate change at the global level. The energy demand in residential buildings is influenced by several parameters, such as national economic growth, climate and weather, demographic structure, and technological development. In this paper, we analyze the determinants of electricity and natural gas consumptions in Italy during the period from 2004 to 2013. We provide estimates of price and income elasticities of the demand for both the energy sources in the residential sector. The role played by both energy price and household income is worth attention because it provides evidence for the occurrence of the direct rebound effect. Therefore, the economic drivers of the energy demand are important to improve the policies meant to reduce greenhouse gas emissions and contrast the global warming. [ABSTRACT FROM AUTHOR]

Constant thermal conductivity values of insulating materials are typically used in building design and assessment. However, the thermal conductivity depends on many factors, such as on the temperature and moisture content. Linear temperature-dependent laws have been occasionally proposed for inorganic fibrous materials such as fiberglass or rock wool that exhibit a decreased thermal conductivity (better performance) at low temperatures. However, the petrochemical-foamed insulating materials such as the polyisocyanurate, have less regular temperature dependent behaviors with a poorer performance at both extremely cold and warm temperatures. This means that the use of constant thermal conductivity values results in actual building envelope performance different from the design predictions, with increasing building energy consumptions, greater risks of condensation issues, and decreased occupants’ comfort. This paper aims to quantify the impact of the temperature dependency of the thermal conductivity in exterior walls and flat roofs. Experimental results over a large temperature range (from −20 °C to +60 °C) for different insulating materials were used in hygrothermal simulations in both continental and humid temperate climates. Common insulating materials were taken into accounts, such as fiberglass, rock wool, polyisocyanurate, and extruded polystyrene. The increase in the energy fluxes from common building envelopes once the effective thermal conductivity was considered resulted below 10% for walls and as high as 70% for roofs, especially in the cold weather. Finally, a hybrid insulation system constituted by two layers of different materials, i.e. a polyisocyanurate and a rock wool is investigated. [ABSTRACT FROM AUTHOR]

To reduce energy consumption in buildings, researchers have in the recent decade explored the potential of changing occupants’ energy consumption behaviors using eco-feedback technologies. Energy consumption behavior is a type of consumer behavior, which has been proven culture-specific in prior research. This paper aims to assess the impact of culture on the effectiveness of eco-feedback technologies in reshaping building occupants’ energy consumption behaviors, and to examine the mechanism of such impact. A total of 39 students from 10 different countries who shared four university dormitories were recruited in an experiment. A web-based eco-feedback system was developed in this study, and implemented in these dormitories. The eco-feedback system was responsible for sending weekly email reminders to students participating in the study and, upon their login, providing them with their detailed energy consumption data as well as those of their peers on a daily basis. Subsequent changes in the students’ energy consumption behaviors were analyzed, and correlated with their cultural background, which was assessed using a survey instrument designed based on Hofstede's cultural dimensions model. The experiment results showed that the mean and variance of changes in energy consumption, in response to the provision of eco-feedback information, differed significantly between participants from different countries. The results also showed that all cultural dimensions were statistically correlated to the effectiveness of the eco-feedback system, which explained how different aspects of culture could influence the energy consumption behaviors of building occupants. The results suggested that eco-feedback technologies should be tailored to specific cultural context to improve their effectiveness in building energy conservation. [ABSTRACT FROM AUTHOR]

Abstract: Hotels are a diverse building type with high normalized energy end-use because of its focus to provide best facilities for guests. There is scope, and in many countries legislative requirements, to reduce energy consumption without compromising facilities; case-studies from tourism intensive countries could be useful for demonstrating such possibilities. This paper presents a method of deriving energy benchmarks to enable classification of hotels in Greece based on operational energy use in terms of electricity and oil. The method of classification defines clusters of hotels using the k-means algorithm controlled with the silhouette plot after applying normalization factors for the operational energy data to correct for size, operation (seasonal or annual) and climatic conditions. Ninety hotels are analysed in the sample presented in this paper; and well separated clusters are defined for the whole sample and for the sample split in hotels with annual and seasonal operation. Energy consumption varies considerably between and within clusters indicating that a range of energy consumption targets might be more suitable than single value benchmarks for this type of building. [Copyright &y& Elsevier]

In the tourism industry, hotels and homestay facilities account for considerable amounts of energy consumption and CO 2 emissions. Unlike other types of buildings, occupancy has significant fluctuations over time, while mostly being ignored or using a simple assumption based on energy use intensity or setting the occupancy levels. Due to the lack of occupancy rate information, great uncertainties exist in hotel operations, which leads to erroneous estimates of energy consumption in existing hotel buildings. This paper examines hotel operation practices in China with real occupancy rate data which is critical for reducing the uncertainty of estimating energy consumption patterns. Specifically, this study proposes a two-stage comprehensive and quantitative performance assessment approach for the homestay hotel building considering the system's energy and economic performance simultaneously. Regarding energy performance, there are two key facets to consider: renewable energy penetration and grid impact performance. Additionally, the economic performance (annual income and extra annual investment) of various energy portfolios is evaluated using break-even analysis. The impacts of large fluctuation in occupancy rate on electrical loads are considered and assessed in typical homestay hotels. The proposed assessment approach can comprehensively quantify the impacts of the photovoltaics (PV) areas and battery capacities on the system performance in terms of energy and economics. According to the results of the occupancy-based assessment, the ideal PV area and battery capacity are 400 m2 and 16 kWh, respectively, as opposed to the optimal energy portfolios (PV area 600 m2 and battery capacity 24 kWh) determined from conventional assessment results. [ABSTRACT FROM AUTHOR]

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [ABSTRACT FROM AUTHOR]

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis. [ABSTRACT FROM AUTHOR]

The reduction of energy consumption and CO 2 emissions in buildings has become an essential field of study in the recent years. Simplified design tools, used in the first design stages, can be of great help in adopting concrete decisions that will, at the end, allow these to be reduced. This paper presents a new predictive model for office buildings in Chile. Starting from the 1,386,000 study cases pursuant the ISO 13790:2008 standard, 18 multivariable regression models have been generated, 9 for energy consumption and 9 for CO 2 emissions., They have been adapted to each climatic zone in Chile. In these case studies, 8 fundamental variables have been considered to cover the design parameters. This research considers number of stories (NS), floor area (FA), form ratio (FR), window-to-wall ratio (WWR), coefficient of performance (COP), Energy efficiency ratio (EER), heating emission factors (HEF) and cooling emission factors (CEF). The models generate an R 2 between 91.81% and 98.05% for energy consumption and between 96.83% and 99.56% for CO 2 emissions, with the results of this research being incorporated into future regulations and into the first stages of design for office buildings in Chile. As an expected outcome, the model will contribute to reduce, or at least contain, energy consumption and CO 2 emissions associated with office buildings in the future. [ABSTRACT FROM AUTHOR]

The objective of this article is to improve diagnosing and data availability for all stakeholders, including building operators, planners, inhabitants and utility suppliers. This paper presents the results concerning the demand for final and primary usable energy. For each of the buildings a preliminary energy audit was prepared and this was the basis for comparison between the estimated energy consumption in the buildings before and after thermal upgrading. The research was carried out mainly in buildings belonging to the Department of Public Utilities and Housing in Zielona Góra. The group of selected buildings consisted mainly of traditional technology buildings having different functions, i.e. residential, residential-commercial, commercial, and other buildings (schools and boarding houses). Forty buildings were selected for analysis. They were built in different years and had different functions, i.e.: 23 residential buildings, 6 residential commercial buildings, 7 commercial buildings, and 4 buildings had other functions (schools). They are the main energy consumers, which has a considerable impact on the environment. [ABSTRACT FROM AUTHOR]

Current indoor thermal environment is usually automatically controlled based on a temperature set-point given by room occupants or building managers. However, investigation shows that many temperature set-points are far from the comfortable temperature range recommended by design handbooks. Such unreasonable temperature set-points will result in not only uncomfortable indoor thermal environment, but also waste of cooling or heating energy. The authors have proposed a methodology of satisfaction based control to take place of the traditional set-point based control. This paper describes the experimental comparison results between satisfaction based indoor thermal environment control and set-point based control. Two test-beds were set up and a series of experiments was conducted to compare control performances, user acceptances, user work performances and system energy consumptions of the set-point based and satisfaction based control. The comparison results show that the satisfaction based control can achieve more stable thermal environment. Both of the two control methods can get relatively high scores regarding user acceptance and work performance. There are no statistically significant differences for user acceptance and work performance between two control methods. However, satisfaction based control consumed 15.3% and 11.9% less energy at two test-beds respectively than set-point based control due to more reasonable temperature settings given by the satisfaction based control. [ABSTRACT FROM AUTHOR]

The effective and efficient utilization of electrical energy is the outcome of smart energy management system. Moreover, appropriate augmentation of renewable energy alternatives through distributed generation can considerably unburden the power utility system. This paper identifies the scope for the rule based approach to develop an energy efficient utilization model for bulk power consumers. The augmentation of distributed power generation using solar PV has also been proposed which considerably reduces the maximum demand (M.D) of power during peak hours. The distributed generation from solar PV for this case-study uses minimum storage battery as the generation schedule and the load schedule often coincides. The portfolio of energy distribution is formulated based on priority of energy demand. The proposed model can be suitably modified for similar type of load centers including industrial loads. [ABSTRACT FROM AUTHOR]

Lack of information on energy consumption patterns of intermittent-operating escalators and unawareness of building owners of potential energy savings constitute the main obstacles for the penetration of energy-efficient escalator technology. Even though intermittent operation provides large energy savings in low passenger traffic, little research has been devoted to analyzing the composition of total energy consumption in these situations. This paper presents observations and data gathered during long-term energy measurements of an intermittent-operating escalator pair (one upwards, one downwards operating), equipped with reduced speed and stop & go modes, installed in a store in the Helsinki area. Energy consumption measurements were performed concurrently with people counting. The effect of passengers walking on the step band on daily energy consumption was calculated by relying on the people counting sensor data. Additional experiments were conducted by applying different masses to analyze the dependence of energy consumption on the carried load. Finally, the effect of intermittent operation, or speed control with stop & go functionality, on energy consumption was calculated for the escalator pair on the given site. The energy consumption is reduced up to 42% in the upwards running escalator and 52% in the downwards running escalator compared to a continuously running escalator with the same operating hours. The study concludes that the power consumption profiles of the intermittent-operating escalator pair highly depend on the characteristics of the people flow, mainly the consistency of the people flow. [ABSTRACT FROM AUTHOR]

• Synthetic thermal comfort profiles were generated from ASHRAE thermal comfort databases I and II used to evaluate occupant satisfaction and to calculate the HVAC setpoints. • Corrective Power of PCS was integrated into the Comfort Profiles. • PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18%. • ASHRAE requirement of 80% occupant satisfaction is met with little increase in energy consumption. Personal Comfort Systems (PCS) are energy-efficient devices that create a microenvironment around occupants and can be controlled individually to improve personal comfort. Most studies related to PCS either focus on improving occupants' thermal satisfaction or evaluate the energy-saving potential of PCS devices but do not consider the energy and comfort consequences simultaneously. This paper investigates the impact of PCS on improving occupants' thermal satisfaction and energy consumption across different climates in the U.S. using 16 representative cities. To account for the diversity in individual preferences, 400 comfort profiles for four climatic zones for summer and winter were generated utilizing comfort votes from ASHRAE global thermal comfort database I and II. PCS devices were used to improve occupant satisfaction by integrating the Corrective Power (CP) values into the comfort profiles. CP is defined as the temperature difference between two ambient temperatures that achieve the same thermal sensation one with PCS and another without PCS (reference condition). Then we compute the zone level setpoints that maximize the number of satisfied occupants and evaluate energy consumption of those zone level setpoints using EnergyPlus. Our study includes 3 cases; Case 1: without PCS (baseline), Case 2: only fans in summer and heaters in winter, and Case 3: fans and heaters in both summers and winters as required to improve satisfaction. In each of the three cases, we evaluated the comfort consequences using the comfort profiles, and the energy consumption implications. This accounts for the change in HVAC setpoints and the power requirements of PCS devices. The results show that incorporating PCS can increase occupants' satisfaction from 68% in Case 1 to 77.8% in Case 2 and 86.6% in Case 3. Moreover, energy consumption results in Case 2 are 0–1% higher, and 0–1.3% higher in Case 3 compared to without PCS, depending on the climate. Our results demonstrate that the adoption of PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18% and meet the ASHRAE requirement of 80% occupant satisfaction with little increase in energy consumption. The comfort profiles generated in this study are also released as a dataset to support future studies. [ABSTRACT FROM AUTHOR]

• Two model buildings are specially built for comparative study. • Dynamic monitoring of external insulation for improving thermal environment and energy saving during heating. • The time for EPS building to approach the air temperature set value has no obvious advantage. • The energy saving effect of short-time heating operation of EPS building is very weak. • A dynamic index is proposed to evaluate the improvement effect of external insulation on indoor thermal environment. In this paper, two identical model buildings were built, one of which adopted 38 mm Expanded Polystyrene (EPS) board as the external insulation. Under the same heating set-point temperature, the dynamic change process of indoor thermal environment and heating energy consumption of EPS insulation building and non-insulation Reference building was compared and monitored. Results show that under the same heating set-point temperature, the indoor temperature of the two buildings can quickly approach the set value in about 20 min. However, the temperature rise of the internal surface of the wall lags behind. After several hours, the advantage of the temperature rise of the internal surface of the EPS building gradually appeared. The energy consumption of the two buildings is similar in the initial heating period of 20 min, and the energy saving rate of EPS building increases slowly with the increase of the heating duration. The energy saving rate is about 1%–26% in the range of 0.5–8 h intermittent heating in hot summer and cold winter zone. The change curve of cumulative temperature rise ratio with heating time is proposed to evaluate the dynamic improvement effect of external insulation on indoor thermal environment. This research provides a reference basis for the development of energy conservation. [ABSTRACT FROM AUTHOR]