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• Five different energy efficiency alternatives for the building were studied. • A ground-source heat pump and solar PV usually provide the highest profitability. • A household battery has become profitable at the 2022 electricity price. • Dimensioning of a solar PV system massively depends on the electricity price level. • At the 2022 electricity price level, the array-to-inverter ratio can be up to 4.9. Buildings and the construction sector make up about a third of the final energy consumption and energy-related carbon dioxide emissions, offering a significant potential for emission reductions. This paper investigates the conversion of a Nordic oil-heated townhouse into a carbon-neutral one. Five different energy efficiency (EE) alternatives including, for example, different heat pump types and their dimensioning are studied. The electricity demand of the EE alternatives is aimed to be covered as cost-effectively as possible with an on-grid system based on solar photovoltaics (PV) and a battery energy storage system (BESS). The cost-optimal dimensioning of the components is based on the 30-year life cycle cost (LCC), and the work is carried out using an hourly level simulation model developed in MATLAB. To maximize the profitability of the BESS, an intelligent control algorithm based on linear programming is implemented based on a day-ahead electricity spot price for the years 2020–2022 and "perfectly" forecasted power demand and generation data. The data used in the simulation consist of measured electricity consumption, simulated heat generation based on annual oil consumption, and simulated solar PV power generation. It was observed that an about 60% partial-power-dimensioned ground source heat pump together with a solar PV system provides almost always the lowest LCC. However, the BESS was shown to be profitable at the current investment costs for the first time at the 2022 electricity prices, enabling a capacity of 210–230 kWh. On average, the cost-optimized on-grid electricity system was found to reduce the 30-year life cycle costs by 6%, 20%, and 85% with the electricity prices of 2020, 2021, and 2022, respectively. When the price of electricity is exceptionally high, it may be justified to implement more extensive energy efficiency improvements, such as additional insulation of the building envelope. Nevertheless, the impact on the life cycle costs is marginal. In addition, the cost-optimized solar PV capacity has increased more than sevenfold between 2020 and 2022. [ABSTRACT FROM AUTHOR]

Due to the rapid development of Advanced Metering Infrastructure (AMI), data-driven approaches are becoming more effective and accurate for Measurement and Verification (M&V) baseline energy modeling of existing buildings using historical energy consumption data. This paper develops and applies a comprehensive analysis framework suitable for developing baseline data-driven models to estimate energy savings from building retrofits. The framework employs common data-driven modeling approaches for building energy prediction, necessary data processing tasks, and industry-used evaluation methods. The modeling approaches considered by the developed framework include linear regression, ensemble models, support vector regression, neural networks, and kernel regression. The developed framework is applied to two case studies with different sources of energy consumption data generated via a simulated office building model and actual measurements obtained from a publicly available dataset. The framework applications to data from 170 buildings with 1-year data result in energy use predictions with medians of Goodness-of-Fit (GOF) scores that are less than 25%. • A data-driven framework is proposed to automate baseline modeling for measurement and verification analysis. • The framework considers several data-driven modeling approaches and timestamp frequencies. • The framework is able to predict within 5.54%, the energy savings incurred by retrofitting an office building. [ABSTRACT FROM AUTHOR]

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models. [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]

The low energy efficiency of the European building stock is both a social and environmental issue. However, plans and studies on energy retrofitting interventions do not usually evaluate the impact of these measures on households' energy affordability. This paper analyses and compares the effect of alternative retrofitting strategies on thermal energy services' affordability in Spanish vulnerable households. This analysis was carried out by applying the Required Thermal Energy Expenditure model proposed in previous studies to several 2030 residential building stock scenarios, setting 2020 as a baseline. Therefore, this work proposes a methodological approach to assess the impact of the considered energy efficiency interventions on energy poverty in winter (WEP) and summer (SEP) and carries out both a 'social cost' and a cost-benefit analysis. The dwelling's thermal-enclosure retrofitting stands out as a very WEP-reduction and social-cost effective measure. Instead, replacing old thermal systems with more efficient ones would have a higher impact on SEP. Combining both kinds of retrofitting measures could reduce heating and cooling required expenditures by, respectively, 49% and 59%, and decrease the WEP and SEP share, correspondingly, by 35% and 63%. These results might inform policymakers to enhance the targeting and design of energy efficiency programs. [ABSTRACT FROM AUTHOR]

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50 % of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730 h to 1300 h when operating at 67 % of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44 % and 21.05 %. [ABSTRACT FROM AUTHOR]

• Bottom-up archetype model for space heating demand of Swiss office building stock. • Archetypes characteristics derived from energy certificates database. • Estimation of current thermal performance and energy retrofit's potential. • Calculation of national heating demand and distribution among archetypes. • Influence of design characteristics and buildings properties on heating demand. Energy efficiency in buildings is a key objective of the European energy strategy. To address the limited knowledge of the thermal performance of the non-domestic building stocks, this paper presents a bottom-up, physics-based, archetypal model derived from a large building stock database and physical characteristics from 2170 building energy performance certificates in Switzerland. The model was validated by measured energy consumption data and national statistics. Results highlight the potential for energy retrofit: most of the stock is still heated with fossil fuels (67% of the total heated area) and 40% of the total envelope surface has a poor thermal performance associated with the period before 1980. We estimate national final energy demand for heating of the office building stock (11.3 PJ/year) and present detailed breakdown of energy by thermal loss element. Significant differences were found between archetypes related to urban settings (specific demand 50% higher in rural than in urban contexts) and age of the buildings (56% of the total demand is due to buildings constructed before 1970). The physical model highlights the impact of architectural design characteristics such as building compactness and glazed surface ratios on thermal performance. The model represents an important step for studies on optimal thermal retrofit pathways. [ABSTRACT FROM AUTHOR]

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

• EV presents a technical challenge to the grid and an opportunity to introduce sustainable and novel solutions. • A sustainable energy strategy is proposed to exploit the synergy between EV batteries and heat pumps. • Electrification of transport and heat in buildings can reduce typical householder carbon footprint by up to 87%. The demand for electricity has been increasing worldwide and it is predicted that this trend will be particularly reinforced in developing countries by the gradual electrification of the transport sector and heat generation in buildings. The energy supply infrastructure required to meet the additional electricity demand should be carefully managed in light of the climate change carbon emission targets and commitments. A large proportion of the new electricity loads will be met from renewable sources. Therefore, means of power storage become vital to smooth out the intermittent nature of these energy supplies. The introduction of Electric Vehicles (EVs) could provide a viable and dynamic power storage solution through the concept of Vehicle-to-everything (V2X). This involves the storage of renewable energy (RE) in EV batteries during the charging cycle and restitution to the grid (V2G) or homes (V2H) when needed. In this context, this paper presents a methodology involving several strategies to stabilise the grid system and examines the impact of various types of EVs and heat pumps (HPs) for supplying heat in buildings. The results of this research approach show that the synergy of using V2H could reduce the carbon footprint of a typical domestic building in the United Kingdom (UK) by up to 87% and potentially recover up to 21.9 kWh/day of surplus renewable energy. [ABSTRACT FROM AUTHOR]

Glued Laminated Bamboo (GluBam) has been innovated under the support of modern industrial technology, and this renewable material can drive the development of building structural system in a fashion of low carbon footprint. From the perspective of carbon emission, energy consumption and cost-based economy, this paper presents a comprehensive life cycle assessment (LCA) of steel-GluBam hybrid truss and virtually designed steel truss for a comparison. A steel-GluBam hybrid truss constructed at the Nanjing Tech University is used to carry on this study. The system boundary (i.e., production phase, transportation phase, construction phase, operation phase and disposal phase) of life cycle are fist determined. The mechanical properties of the truss structure are considered as the functional equivalence. The inventory of construction materials and the elements in each phase in the life cycle is compiled from the SimaPro and the available literature/specification. Based on the inventory analysis, the Monte Carlo method is employed to conduct the uncertainty analysis of carbon emission of the truss. The assessment results show that the life cycle carbon emission, energy consumption and cost of the steel-GluBam hybrid truss are equal to 4572.5 kgCO 2eq , 71.15 GJ and 15626.6 CNY, respectively. The production phase is the largest contributor to the carbon emission, energy consumption and cost, and the following one is the operation phase. Whereas the transportation phase, the construction phase and the disposal phase together contribute to only the small parts of emissions and cost. GluBam has the function of carbon sequestration leading to the reduction of carbon emission and energy consumption in the production phase, and other phases have no advantage on the emissions and cost of GluBam. The 95% confidence interval of life cycle carbon emission based on the Monte Carlo simulation is estimated as [4290,6370] kgCO 2eq for the steel-GluBam hybrid truss. With respect to the whole life cycle, using GluBam to replace the steel pipe in the upper chord pole and the diagonal web pole of the truss has a little larger cost but its environmental impact and energy consumption reduce a lot. The railway transportation of GluBam reduces 36.5 kgCO 2eq carbon emission, 0.68 GJ energy consumption and 83.7 CNY cost in the life cycle with reference to the road transportation. The steel-GluBam hybrid truss optimized based on the functional unit occurs in the case of reducing the cross-section of steel pipe from 42 mm × 4 mm to 32 mm × 2.5 mm. [ABSTRACT FROM AUTHOR]

• The new solution for the façade integrated photovoltaic system, En-ActivETICS was proposed and described in detail, including technical recommendations. • The calibrated numerical model was used to determine hygrothermal performance and identify the most crucial issues of En-ActivETICS. • The positive effect of PCM and diffusion channels was revealed based on a numerical study using calibrated thermoelectrical and hygrothermal models. • The En-ActivETICS was investigated considering local climatic and economic conditions among 38 European countries. • The roadmap 2030 for the En-ActivETICS advancement was developed and presented. The main goal of the European building market transformation regards the reduction of CO 2 emissions and energy demand in the built environment towards net zero carbon buildings. This drives research and building practice to search for economically justified solutions that contribute to achieving these objectives. The work presented in this paper is concerned with the technological and economic context. The original solution of photovoltaic integration with the external thermal insulation composite system named the En-ActivETICS was developed, tested and analysed. The energy potential of the En-ActivETICS across Europe, considering south oriented façade is from 88.3 to 195.8 kWh/(m2⋅a), with the efficiency of solar energy conversion of around 13–14%. The problem of system overheating was solved by applying PCM mortar beside the PV panel, which allowed to keep the PV temperature below 70 °C. The risk of moisture load (from driven rain or water vapour condensation) was avoided by diffusion channels pressed in the mortar. Furthermore, the economic analysis of system implementation in Europe is also promising. Considering the availability of solar energy and electricity prices around Europe the payback time of the En-ActivETICS varies between 12 and 44 years for analysed countries. According to the presented roadmap for the En-ActivETICS advancement a payback period of <10 years is possible with a maximum cost of the system <275 EUR/m2 and electricity price >0.25 EUR/kWh. [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]

• Households change their heating patterns when they switch to a heat pump. • Typically, a setback temperature 2–5 °C lower than the desired thermostat setting. • We calculate the increase in heat demand with varied parameters. • The increase in heat demand is determined by heat loss and thermal mass. • This would be a useful metric for 'heat pump readiness'. Heating with air source heat pumps is an important step in the transition to Net Zero Carbon for many households. When predicting the resulting energy consumption it is frequently assumed that heating demand is unchanged from before the switch from gas heating. However, heating patterns usually change, with a net increase in heating demand to achieve the same level of thermal comfort. This paper discusses the change in pattern and uses modelling to determine the resulting increase in energy demand. It shows that the increase can be predicted with information about dwelling heat loss and thermal mass as well as weather and the pre and post switch heating pattern. The increase can be 20% or more for homes with high heat loss. It proposes that the metric for heating demand increase would be a useful measure of heat pump readiness, and that the parameters required to assess this should be provided on energy performance certificates. [ABSTRACT FROM AUTHOR]

• Long term field evaluation of forecast control of heating. • Easy integration with existing installations in buildings. • Widely applicable measure for energy savings around 10%. • Payback time below one heating season. The priority for reaching carbon neutral buildings by 2050 is to first increase energy efficiency in existing buildings by use of easily and widely applicable measures that are verified based on long-term field research. One such solution is a forecast control of heating systems, which can be quickly and easily integrated to existing weather based controls. However, there are still no long-term field evaluation and detailed analyses of the main features of such a system, which would provide a clear picture about the system's energetical and economical features and thus increase their wide application in existing buildings. This paper provides the data from a long term field evaluation of an innovative forecast control of heating systems (forHEAT), which can be easily installed (in less than 2 h) and integrated with existing installations in millions of existing buildings in Europe and throughout the world. The energy consumption for heating was evaluated in seven residential buildings and three public office buildings before and after the installation and use of the forecast control. The resulting energy savings averaged 13.4% in the residential and 10.7% in the public buildings located in continental European climate, with very cold winters. The payback time averaged a 0.6 heating season and in most case studies does not exceed a single heating season. [ABSTRACT FROM AUTHOR]

Providing the occupants with tailored feedback on their energy consumptions can be an effective method to help the users to adopt energy-efficient behaviours. However, in specific contexts such as the social housing, this may be challenging, due to the peculiarities and the economic constraints of the end-users, which make them vulnerable and needing a proper design of the information. Nevertheless, there is a lack of detailed studies in the literature analysing the effects of personalized energy feedback on the space heating consumption and the indoor air temperatures in the social housing sector. This paper aims to evaluate the effects on space heating consumption and indoor air temperature of a personalized feedback campaign delivered in three social housing buildings consisting of 27 apartments in Mediterranean climate. To this end, a personalized feedback campaign was designed and developed, providing for the use of disaggregated energy consumption indicators to show the location of excessive thermal energy consumptions. The daily thermal energy consumptions, as well as the indoor air temperatures of the single dwellings, were monitored daily for over four heating seasons and their variations were analysed before and after the implementation of the personalized feedback. A saving in normalized natural gas consumption of the buildings was found on average equal to −14,8% after the feedback campaign, with no significant variations in the indoor air temperatures. However, some critical issues have been highlighted in the response of individual end-users to the feedback provided. A greater propensity of the occupants to regulate the indoor air temperature through the thermostatic radiator valves in the unoccupied rooms of the house was also observed after the occurrence of the feedback campaign. [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]

The purpose of this paper is to study the energy usage pattern of office building all year round using time-series energy consumption data. This investigated pattern could uncover the energy utilization issue availing building energy efficiency implementation. Past researches principally focused on the total energy condition instead of time-series in terms of time cycle. This paper implements the innovative artificial intelligent algorithms to perform the data mining target via cluster analysis and association rule discovery between different types of energy. Official energy building models provide the studied database. The result shows that k-shape and apriori algorithm could successfully obtain the energy using pattern hidden dataset. There is significant dispensable energy wastage after working time with office building since the long leave course happening after 18:00. The main subentry energy determining total energy is different in disparate stages. Moreover, cooling energy primarily manipulates the total energy in most of time indicating more than 80% degree in terms of confidence level. Conclusion illustrates that this workflow could successfully detect power load profile features and find the unreasonable issues with energy using. Combining above cluster and association analysis outcome contributes to the energy adjustment in each period more precision. [ABSTRACT FROM AUTHOR]

In HVAC systems, the application of fault detection and diagnosis (FDD) for chillers has brought many benefits to building utilities, i.e., indoor thermal comfort, energy saving, and energy consumption management. Although recent studies have achieved great progress on chiller fault diagnostic problem, some serious issues have been raised in these studies and need to be resolved comprehensively. In that context, two problems are listed and studied in this paper namely outliers detection and the insufficiency of labeled data. To deal with outliers mixed in chiller data, this paper proposes a supervised multiclass deep autoencoding Gaussian mixture model (S-DAGMM) algorithm which is an ensemble model of individual unsupervised DAGMMs. This mechanism helps S-DAGMM to detect ambiguous outliers in both training and testing data. In addition, this paper proposes to use DAGMM to pretrain a deep neural network (DNN). Since DAGMM can learn the data distribution from the unlabeled data, it can prevent DNN from getting stuck on local optima. Comprehensive experimental results in this study have proved the outstanding performance of the proposed approach, compared with state-of-the-art rival methods. [ABSTRACT FROM AUTHOR]

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

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]

[Display omitted] • This study presents a state-of-the-art on thermal bridges focusing on slab on grade TB, including calculation methods, norms and software summary. • A numerical study of possible technical solutions to decrease slab on grade TB's effect is presented. • Different external insulation solutions to renovate existing building were compared. • The study highlights the effect of soil thermal properties on slab on grade TB calculation. • Improving insulation thickness and depth does not necessarily reduce the thermal bridges effect. The building industry is responsible for considerable energy consumption and the production of large amounts of greenhouse gas emissions worldwide. Hence, reducing the building heat losses that occur through the building envelope, especially through the floor foundations, is important. This paper provides an up-to-date review on thermal bridges (TBs), mainly with respect to slab-on-grade TBs. First, different thermal bridges types are defined; then the standards used to evaluate TBs are presented. Common approaches to evaluate TBs are discussed, such as the U-value method, the equivalent method approach, and the three-dimensional dynamic method. This latter focuses on heat loss through the foundation and soil in existing buildings. Soil's effect on thermal bridges is described and our case studies showed that thermal bridges heat losses are inversely proportional to soil thermal conductivity. This analysis is involved in the present paper. Finally, exterior insulation solutions at ground level are presented among those the trapezoidal double insulation type can reduce TBs up to 65% compared to no insulation case. As conclusions, directions for further works are suggested to fill current literature gaps. [ABSTRACT FROM AUTHOR]

The ability to accurately model and predict timing and duration of activities for different individuals is essential for successful and widespread Demand Side Response (DSR) policies, especially in the residential sector. Understanding what people do during the day and what factors influence their activity participation decisions is important for planning an effective DSR strategy to harness the end-user flexibility. The recent Covid-19 pandemic has shown how much activities can be shifted to a virtual mode in the presence of mobility restrictions. Further, participation in activities via digital devices (virtual activity participation) has spread across society. Such virtual activities, including teleworking, online shopping, and virtual social interactions, are observed to explicitly impact travel behaviour and activity scheduling. And yet, activity-based models of mobility and energy demand do not accommodate the trade-offs between activity types, location and virtual activity participation. This paper presents a model of activity participation that captures the relationship between the three dimensions of: activity type (such as work, study, shopping), activity location (in-home, out-of-home), and activity modality (in-person, virtual). A Multiple-Discrete Continuous Extreme Value (MDCEV) model structure is applied, and the empirical analysis is undertaken using the 2015 United Kingdom Time Use Survey (UKTUS). The model results provide insights for better understanding of the trade-offs made by individuals as they participate in and allocate time across a set of activity type-location-modality alternatives, and the heterogeneity in these trade-offs. Further, holdout sample validation and policy scenario analysis exercises are presented to demonstrate the reliability and suitability of the model for policy implications. The empirical results presented in our paper suggest that this framework embedded in an activity and agent-based simulator of energy demand will enable us to test a variety of scenarios related to end-user flexibility and activity behaviour change, including consideration of virtual activity participation trends. [ABSTRACT FROM AUTHOR]

This paper evaluates through experimental testing a prototype of switchable insulated shading (SIS) systems suitable for windows to optimize solar heat gains in order to maintain indoor thermal comfort. Specifically, the paper describes a laboratory testing procedure and measurement results for SHGC values specific to various SIS designs and operation settings using a box calorimeter set-up. The SIS prototype can modulate continuously its optical and thermal properties including U-value and SHGC using rotating insulation layers controlled through an actuator to a specific slate angle setting. The results of the experimental testing confirm that SHGC value for the SIS prototype is correlated to the slate angle of the insulated layers. This correlation remains applicable for any SIS insulation thickness. The measured data are used to assess the potential energy efficiency benefits from SIS when applied to windows for US homes. In particular, a prototypical housing unit located in three US cities is considered to evaluate the energy efficiency potential for switchable opaque insulated shading systems. The results indicate that SIS systems can achieve up to 8.3%, 11.3%, and 11.4% in annual HVAC energy consumption compared to no-shading cases for dwellings located in Golden, CO (representing a cold climate), Phoenix, AZ (representing a hot climate), and San Francisco, CA (representing a mild climate), respectively. [ABSTRACT FROM AUTHOR]

• Urban scale energy simulation using open CityGML datasets and Modelica. • Open datasets lack essential building attributes. • Crucial information such as years of construction, refurbishment states and building functions are not openly available. • Archetype based approach for urban energy simulations require additional information. • Simulation of five hundred open 3D building models. In this paper, simulations of 500 open-source CityGML building models for heating demand calculation are presented. Using an archetype-based approach and statistical enrichment, Modelica simulations are carried out using the TEASER tool and Dymola. The authors compare the simulation results against measurement data and investigate the impact of years of construction, building usages and refurbishment states in the context of urban building energy simulations. The individual buildings are assigned with information over the construction years and building functions using the measurement data. The years of construction and building usages are primarily obtained in the context of two other research projects and are used for the archetype assignments of the individual building models. Hence, these are well-informed assumptions by the authors, resp. the industry partner experts. This paper includes an in-depth investigation of individual building archetypes with respect to the years of construction. Firstly, the buildings are simulated using externally acquired construction years and pre-assigned CityGML building functions. The simulation results are compared against the measured primary heating demands. Due to high discrepancies between simulations and measured data, the authors secondly, simulate the building models with different refurbishment states. In this paper, the authors conclude that open datasets are indeed important, however, for energy performance simulations, attributes such as construction years, building usages and refurbishment states are necessary and should be included in the open datasets to present an added value. [ABSTRACT FROM AUTHOR]

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

• An improved system is proposed based on multi-fans with frequency conversion. • The improved system meets pressure control requirement in non-working modes. • The energy saving and cleanliness can be achieved by switching operation modes. Pressure control for cleanrooms in the pharmaceutical industry is a necessary means to avoid the cross-contamination. The pressure control of traditional air conditioning can only handle the working stage. In the non-working stage, the system cannot reduce the air change rate per hour (ACH) in order to alleviate the problem of high energy consumption. This paper proposes an improved air conditioning system, which focuses on the ability to control pressure gradient of the system in working and non-working mode. By theoretical derivation of the combined fan frequency conversion, when the impedance and volume of the infiltration air satisfy S i G Z , i = S j G Z , j = S k G Z , k = c o n s t a n t , the pressure gradient in multi-zone cleanrooms can remain stable. A mathematical model of the system has been established, which includes five modules: air pipe network, fan characteristic curve, fan operating point solution, variable volume damper characteristic curve and multi-zone network pressure gradient solution. The correctness of the model is verified by field measurements. Taking a clean workshop as case study, the room pressure can be stabilized in the range of ± 3 Pa by switching the working and non-working mode, which can save 39.8% of the energy consumption in non-working mode, while ensuring cleanliness. This paper demonstrates the process of the air pipe network's influence on cleanrooms pressure and proves the effectiveness of improved purification air conditioning system in pressure gradient control and energy saving potential. [ABSTRACT FROM AUTHOR]

• Modelling energy savings of renovations in the Dutch non-profit housing stock. • Assessment of 1.6 million dwellings, 21 building features and 3 empirical models. • Empirical models outperform the theoretical building energy model. • Shortcomings exist when predicting effects of specific energy-saving interventions. In Europe, the energy performance of dwellings is measured using theoretical building energy models based on the Energy Performance of Buildings Directive (EPBD), which estimates the energy consumption of dwellings. However, literature shows large performance gaps between the theoretically predicted energy consumption and the actual energy consumption of dwellings. The goal of this paper is to investigate the extent to which empirical models provide more accurate estimations of actual energy consumption when compared to a theoretical building energy model, in order to estimate average actual energy savings of renovations. We used the Dutch non-profit housing stock to demonstrate the results. We examined three empirical models to predict the actual energy consumption of dwellings: a linear regression model, a non-linear regression model, and a machine learning model (GBM). This paper shows that these three models alleviate the performance gap by giving a good prediction of actual energy consumption on sectoral cross-sections. However, these models still have shortcomings when predicting the effects of specific renovation interventions, for example newly introduced heat pumps. The non-linear and machine learning model (GBM) outperform the theoretical model in terms of estimating energy savings through renovation interventions. [ABSTRACT FROM AUTHOR]

[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]

Plug loads are one of the most important energy consumption items in non-residential buildings and their weight is continuously increasing in new generation buildings with highly insulated envelopes and high efficiency HVAC systems. An accurate and realistic modeling of plug loads is therefore of paramount importance for building energy modeling and energy efficient operation, as well as for other emerging applications such as renewables integration and demand response. We present in the present paper a method for generating aggregated electric load profiles for plug loads in office buildings based on more than 6400 field-measured load profiles of individual appliances used in office buildings and a field survey covering more than 1000 office building occupants which provides the distribution of the considered appliances in office buildings. We show that the aggregated load curves scale up following different laws depending on the considered appliances. We also provide aggregated load curve stabilization thresholds for all appliances: 43 for desktop computers, 32 for computer screens, 127 for laptops and 204 for multifunction devices if we mention the main office appliances. Finally, by combining the individual appliances' aggregated load curves and the survey results, we propose a model for generating building-level aggregated plug load curves for three main building types defined through the survey data analysis. We show that the aggregated load curves scale up differently when the number of occupants increases depending on the building type and the day type. Obtained results provide unique insights on plug load aggregated load profile shapes and behaviors and can be very useful for researchers and engineers interested in non-residential building energy modeling. [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]

The American Society for Heating, Refrigeration, and Air Conditioning Engineers (ASHARE) prescribes fixed ventilation rates for the operation of heating, ventilation, and air conditioning (HVAC) systems, which is sub-optimal in terms of controlling indoor air pollutants and energy consumption. This paper relies on a physics-based model of a residential house to predict the concentrations of indoor pollutants such as ozone, formaldehyde (HCHO), and particulate matter (PM) as function of time, outdoor conditions, and indoor emissions. The model captures real-world scenarios such as pollutants entering the house from ambient air through an air handling unit (AHU) and pollutants emitted in the house. A dynamic optimization problem is then formulated and solved to calculate the optimal ventilation rate at each time instant, that minimizes the total energy consumption, while maintaining the indoor pollutant concentrations below or as close as possible to their respective acceptable thresholds. For the considered example day, operation under optimized conditions reduces the peak pollutant concentration by 31%, time of exposure to undesirable concentrations by 48%, and energy consumption of the AHU by 17.7%. [ABSTRACT FROM AUTHOR]

• A model-based optimization strategy is developed for energy saving in LDCD system. • An improved self-adaptive firefly algorithm is proposed to the presented optimization problem. • 12.49% energy savings can be realized by utilizing the proposed optimization strategy. • The energy reduction of chiller system contributes significantly to energy efficiency of the whole LDCD system. In this paper, a model-based optimization strategy for a liquid desiccant cooling and dehumidification (LDCD) system is proposed to improve system energy efficiency. The energy models of the LDCD system are established to predict system energy consumption under different operating conditions. To minimize the system energy consumption while maintaining the system thermal performance, the system energy consumption and thermal performance indicators are normalized by introducing a weight factor in cost function, then an optimization problem considering the interactions between components and system constraints is formulated. An improved self-adaptive firefly algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Tests on an experimental apparatus are carried out to verify the energy saving potential of optimal control strategy under different weight factors and operating conditions. The results indicate that the energy consumption of LDCD system in the proposed optimization strategy is reduced by 12.49% over the conventional strategy. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for high cooling and dehumidification load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing dehumidification systems. [ABSTRACT FROM AUTHOR]

• We proposed a novel modal decomposition based ensemble learning method for GSHP energy consumption prediction. • Variational mode decomposition and empirical mode decomposition were used to decompose the energy consumption data. • The method was compared with two common used machine learning models with optimization process. • The method showed outstanding effect on improving the performance of GSHP energy consumption prediction. This study presents a case study of office buildings using modal decomposition based ensemble learning method to forecast energy consumption of ground source heat pump systems (GSHP). Conventional machine learning methods have uncertainty in practical application as there are lots of stochastic terms in the structure of the algorithm. Therefore, ensemble learning models are proposed to ameliorate the problem. In this paper, the prediction potential of modal decomposition based ensemble learning models are investigated while providing a comprehensive comparison on different single models and ensemble learning models in the building field. Results show that the proposed VMD based ensemble learning models have remarkable advantages in GSHP energy consumption prediction comparing with other machine learning methods, and the prediction performances of VMD based ensemble learning models measured by RMSE and MAE are 30–50% better than common machine learning methods. This work is enlightening and indicates that VMD based ensemble learning models fit well with energy consumption prediction, which could bring more efficient and concise solutions for GSHP energy consumption predictions. [ABSTRACT FROM AUTHOR]

Highlights • Lack of a clear path in the fight against energy poverty across the European union. • Energy poverty related to the residential sector allows new solutions to be developed. • Current energy poverty indicators provide an incomplete analysis of vulnerability. Abstract The term energy poverty (EP), mostly used to capture problems related to an inadequate access to energy suppliers in developing countries, and that of fuel poverty (FP), commonly employed when households suffer from insufficient monetary resources to pay for their basic energy needs, are used interchangeably by many researchers. Energy poverty and/or fuel poverty are considered to be an indication of domestic energy deprivation or energy vulnerability and remain a global problem that has been accentuated in Europe due to the economic crisis. This work has made a comprehensive review of the current concepts and indicators of FP, and of current initiatives to tackle this issue, across Europe, thereby raising the discussion of how energy vulnerability factors fit FP situations: available infrastructure, energy efficiency, social and economic poverty, and wellbeing and health. In this context, the first weak spot of the FP indicators and initiatives analyzed is that most factors that influence the everyday activities of a household are not considered (comfort, health, and wellbeing), resulting in an incomplete analysis if these are used in an isolated way. This paper provides a multiple-indicator approach to understand an FP situation beyond its traditional definition, thereby establishing a starting point for the reflection of vulnerability in policy decisions and methodological implications. [ABSTRACT FROM AUTHOR]

• Energy efficiency of low GWP refrigerants for linear compressor was compared. • R717 requires highest power input and produces largest cooling capacity. • For 75 W cooling, the stroke ratio for R717 is lowest indicating highest EER. • R600a has highest EER at full stroke but cooling capacity is lowest. • R1234yf is ideal drop-in alternative to R134a as EER and cooling capacity are similar. The ever rapid rise in the number of refrigerators causes a dramatic increase in energy demand and emissions, which represents a small but growing contribution to overall global warming. To mitigate the greenhouse gas emission and energy efficiency, high efficiency linear compressor with capacity modulation and low global warming potential (GWP) refrigerants can be considered for use in refrigerators. This paper presents a comparative study on energy efficiency of R717 (ammonia), R600a (iso-butane), R1234yf and R134a in domestic refrigerators using an oil-free linear compressor. The results show that for given operation conditions, R717 has the highest resonant frequency, power input, cooling capacity while R600a has the lowest. R717 has the potential to be used the proposed linear compressor where there is no contact between the corrosive refrigerant with copper windings. This can significantly improve the energy efficiency ratio (EER) with low cost and minimal environmental impact compared with R134a. R600a requires a larger capacity compressor to achieve the same cooling capacity as R717, R1234yf, and R134a. R1234yf can be considered as an ideal drop-in alternative to R134a for an existing refrigerator because of the similar system performance to R134a. [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]

Abstract In the last two decades, a remarkable socio-economic transition has been witnessed in China. Existing studies have only shown that energy consumption of residents is influenced by economic growth and urbanization. It is unclear, however, how socio-economic transition influences the inequality of energy consumption among urban and rural residents. Focusing on the empirical investigation of two different regions (the eastern developed provinces with little energy resources and the western underdeveloped provinces with rich energy resources), this paper examined the major socioeconomic determinants of energy consumption inequality between urban and rural residents. The results of panel data analyses showed that there was a long-term relationship between socio-economic transition and changes in the inequality of energy consumption among residents. Meanwhile, the results revealed that the influence of socio-economic transition varied from regions to regions. Generally, the influence of urbanization on the inequality of both quantity and diversity was found to be the most profound and significant in all regions. The industrialization transition also exerted far-reaching effects, although its influence was determined by the growth of industry sectors. Moreover, the influence of economic transition was only significant in underdeveloped regions and such an influence fitted to a Kuznet's curve. For the transition of technology and education, its influence was significant in the developed provinces but was only effective in affecting the diversity of inequality. In summary, the findings of this research present policymakers a better understanding of the relationship between the socio-economic transition and inequality in energy consumption. [ABSTRACT FROM AUTHOR]

Abstract The household residual component of total power consumption can be considered as a portion of load demand describing the non-temperature-related factors. This component can be decomposed to irregular and predictable energy demands. The predictable component of the residual load include consumptions which are likely to have a periodic behavior. The modeling of this periodic part of the residual load can help to enhance the overall forecasting accuracy. This paper intends to model the periodic part of the residual component by capturing the behavioral patterns of overestimated and underestimated residuals as the divisions of the main residual component. Accordingly, in order to achieve its ambition, this work proposes an Adaptive Circular Conditional Expectation (ACCE) method on the basis of circular analysis to define the sub-residuals operation schedules. Consequently, an adaptive Linear Model (LM) procedure is employed to predict the residual component demand using the results of the ACCE process at each time window. Subsequently, the predicted residual is utilized to adaptively improve the performance of total electricity demand forecasting. The accuracy of the forecasting results is evaluated using Normalized Mean Absolute Error (NMAE). As a result, the proposed approach of the periodic residual demand modeling in a daily horizon leads to a promising accuracy increase of 23%. Furthermore, the proposed residual modeling method, in combination with the temperature-related component forecasting, can increase the total power consumption prediction performance by 7%. The efficacy of the proposed approach is examined via numerical analysis of real data. [ABSTRACT FROM AUTHOR]

Abstract Energy efficiency in buildings is a key issue in the current energy transition. In order to reduce building energy consumption, users' behaviour and the perception of indoor environmental comfort must be taken into account; these aspects are inextricably linked to energy demand, consumption and related costs. In this paper, we present the methodological framework, technological solutions and outcomes of the ComfortSense project. ComfortSense aimed at decoupling energy demand from indoor comfort. We focused on Heating, Ventilating and Air Conditioning (HVAC) systems in buildings, on users' behaviour and on comfort perception by treating buildings as socio-technical systems. Our approach - which was multidisciplinary and included the contribution of sociologists, physicists and computer scientists - was based on Internet of Things technologies, on a Living Lab design and testing process and on a Crowdsensing approach. Physical parameters (objective variables), such as temperature, CO 2 concentration and relative humidity, were measured by a Wireless Sensor Network and by wearable devices, while the users' perception of comfort (subjective variables) were recorded as real-time feedback through a Mobile App in three pilot buildings of the University of Turin, engaging about a thousand buildings' users (professors, researchers, students and employees). Objective and subjective variables were correlated through an ad-hoc Direct Virtual Sensor. Thanks to the Direct Virtual Sensor forecasting we demonstrated that, adopting an adaptive indoor comfort management, users' comfort can be remarkably improved while reducing the energy consumption of HVAC systems. [ABSTRACT FROM AUTHOR]

Abstract Housing prices in Israel have become exceptionally prohibitive, and this includes single family housing. Conventional construction is mainly cement and concrete based, and this has significant negative impacts on cost, time and environment. Can modern industrialized processes and materials alleviate such issues? This paper compares a conventional cement concrete residential building with one made of Fiber Reinforced Polymer (FRP) composite material. It assesses the relative advantages of each one in terms of overall energy implications during their life cycle. Results show that the FRP house has an advantage of higher thermal resistance, which leads to lower energy consumption during cold periods of the year. Onsite erection time savings is another significant advantage price-wise. Low thermal mass of the FRP option is a disadvantage that makes it more energy consuming during summer. The main disadvantage is the noticeably higher Embodied Energy (EE) of the FRP in the production phase (cradle-to-gate) in comparison to the parallel concrete house EE for the same phase. The main tools used were EnergyPlus for thermal simulations and Simapro for LCEA. [ABSTRACT FROM AUTHOR]

Highlights • Experimental tests of five IEC configurations are carried out. • Performance significantly depends on system configurations and water flow rate. • Highest effectiveness occurs in case of horizontal orientation of plates. • Lowest effectiveness occurs with water and secondary air supplied from the bottom. Abstract The indirect evaporative cooler (IEC) can be effectively used to increase energy efficiency and reduce energy consumption of HVAC systems. In particular, such system can be suitably adopted in data center applications, where the design indoor temperature can reach 45 °C, according to recent ASHRAE standards. It is well known that IEC performance can be significantly increased by an effective secondary air humidification process. In particular, the system should be designed in order to obtain an optimal water distribution on heat exchanger plates. In this paper, five different water nozzles and airflows arrangements of a IEC system based on a cross flow heat exchanger are experimentally investigated. The system is designed to have a limited water consumption (water to secondary air mass flow ratio between 1.4%–4%) and performance are measured in different operating conditions. Results show the highest wet bulb effectiveness (up to 82%–84% in the investigated conditions) is obtained in two configurations: (i) Horizontal orientation of heat exchanger plates; (ii) Vertical orientation of heat exchanger plates and water/secondary air supplied from the top. [ABSTRACT FROM AUTHOR]

Abstract Beyond the definition and measurement of energy poverty, which have mainly attracted research interest over the last decades, attention has recently begun to turn to a broader notion of energy vulnerability. So far, this has mostly been approached on a theoretical base, suggesting the incorporation of broader socio-economic aspects into the energy poverty framework. However, the quantitative measurement of energy vulnerability is still an unexplored field of research. In this paper, the "Vulnerability Index" is developed, which, based on stochastic analysis, quantifies the vulnerability of a population to the energy poverty ratio, compared to a reference population. Moreover, Sensitivity Analysis quantifies the particular contribution of various parameters to the problem, by determining their weighting factors. As case study, the vulnerability of Greek mountainous regions with respect to the country level was studied. The results showed that households living in mountainous regions are more vulnerable versus those of the whole country, at 89.7%. It was also found that energy policy should focus on raising incomes of mountainous population and, afterwards, on energy saving measures and reduction of heating cost in mountains. The methodology suggested can address social inequalities of vulnerable population groups, in terms of energy poverty. [ABSTRACT FROM AUTHOR]

Abstract Convenience stores are known to have higher Energy Use Intensities (EUIs) than most other commercial building types. However, convenience store energy performance analyses have not been systematically investigated. Moreover, current national convenience store benchmarks are inconsistent from one data base to another due to lack of relevant energy data. This paper assesses the energy performance of more than five hundred convenience stores in the same fleet in Eastern United States. The investigation aims to establish benchmarks for convenience stores with and without gasoline pumping stations. The main factors influencing the actual convenience store building energy use were determined. Multiple regression models were developed to find relationships between annual energy consumption per unit area and gross floor area, customer transaction count, building operating year, etc. The statistically characterized results are meant to serve as a reference data set in comparing the whole-building energy performance index for buildings of similar functionality. The statistical correlation analyses can strengthen the robust prediction of energy performance in convenience stores, enabling proper assessment of energy conservation measurements (ECMs) for convenience store operations, as well as guiding additional such energy performance studies. [ABSTRACT FROM AUTHOR]

Highlights • Calculation of life cycle costs and payback periods of building intervention packages. • Calculation of financial gap between nZEB and cost optimality. • Study covers single and multi-family house buildings for 2 periods and climate zones. • Ranking of intervention packages based on cost effectiveness and energy efficiency. Abstract Nearly zero energy buildings (NZEBs) constitute one of the main pillars of the energy policy of European Union (EU). Greece has recently established a clear definition for NZEB, however the interdependence between cost-optimality and the energy performance of NZEB has not been adequately studied. The present work includes a cost effectiveness analysis among different energy efficiency interventions for the existing residential Greek building stock. To this end, the calculation of the investment as well as the operating and maintenance cost for a wide range of energy efficiency measures were carried out. All calculations were based on the methodology defined in the Directive 2010/31/EU on the energy performance of buildings, while the economic evaluation assumptions were based on the EN 15459-1 standard. The goal is the determination of the most cost effective (cost-optimal) measures that are applicable for two different building types; Single Family House (SFH) buildings and Multi-family House (MFH) apartment blocks, taking into account the period of their construction and the climate zone they belong to. Furthermore, the scope of the paper includes also the assessment of the gap between these measures and the NZEB thresholds. The analysis showed that in various cases and depending on the exact national NZEB definition, NZEBs are located beyond cost optimality. [ABSTRACT FROM AUTHOR]

Abstract Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system. [ABSTRACT FROM AUTHOR]

Highlights • Providing a critical review of office building energy management based on occupancy monitoring and modeling, and HVAC and lighting control strategies. • Proposing a roadmap towards cognitive building management. Abstract Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings' physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research. [ABSTRACT FROM AUTHOR]

Abstract The globe thermometer has been considered a reliable instrument to quantify mean radiant temperature (MRT) since Bedford & Warner isolated its readings from air movement in their 1934 paper so that radiation could be quantified. Recent expanded use of radiant heating and cooling systems has presented new challenges for the usage of globe thermometers in the built environment by causing additional radiant asymmetries and performance expectations. Therefore, we replicate the original Bedford & Warner work to reconsider and develop a more holistic understanding of black globe performance and the determination of MRT in buildings. We recreate the MRT and air temperature separation to investigate the accuracy of globe thermometers on measuring MRTs. A radiantly heated open-plan laboratory and a radiantly cooled conference room were selected and measured with multiple globe thermometers and non-contacting infrared sensors. The globe temperature results were then corrected with air movement to produce MRTs and compared against MRTs simulated from measured surface temperatures. We demonstrate a significant impact of air speed on the MRTs obtained from globe thermometers. We also illustrate a less-investigated non-graybody emissivity variation and spatial variation of MRTs of up to 5 °C at the same height. We believe the increasing temporal and spatial resolution of digital sensors may create new challenges for using globe thermometers to measure MRTs, since fluctuating readings may camouflage potential MRT changes. Through a validation of our spatial MRT distribution with experimental results, we believe there is a need for better sensors that could spatially resolve MRTs, and recognize issues with both air speed and emissivity. [ABSTRACT FROM AUTHOR]

Highlights • The art on airflow distribution and management in data center is reviewed. • Experimental and numerical investigations of several typical cases are summed. • Energy efficiency of air distribution must be based on the reliability of data center. • HACS is a promising air management system due to its flexibility and extendability. • Improvement methods of vertical airflow uniformity needs to be developed. Abstract Rapid expansions of high-density data centers have been triggered worldwide owing to the high demand of cloud computing over the past decade. The airflow distribution and management have great association with the thermal performance of the typical underfloor air distribution (UFAD) data center. Therefore, numbers of configurations and methods have been proposed to improve the thermal performance of data centers with various operation conditions. This paper reviews the ventilation configurations of data center and presents the geometric effects of underfloor plenum on airflow. The experimental and numerical investigations on typical cases as well as the relevant parameters are also summed up. Various air management methods including cold aisle containment system(CACS), hot aisle containment system(HACS) and vertical exhausted duct system(VEDS) are analyzed on the benefits and limitations. Based on the enormous amount of research on the traditional UFAD system with hot aisle/cold aisle (HA/CA) arrangement, it can be concluded that the practices of data centers have a tendency of being more flexible and extendable, while the literature on the corresponding cases which applied HACS or other air distribution systems remains scarce. Moreover, beside the improvement of the airflow uniformity along the cold aisle, methods for enhancing the airflow uniformity along the height direction are also encouraged to be developed. [ABSTRACT FROM AUTHOR]

Highlights • We examine the impact of HEMS (feedback and smart features) on energy consumption. • Findings from interviews with 14 households on their interaction with HEMS are presented. • Effects on energy consumption varied widely among individual households. • Obstacles for energy consumption behavioral change are identified. • Policy implications and key issues for future research are suggested. Abstract Home energy management systems (HEMS), providing energy feedback and smart features through in-home displays, have the potential to support more sustainable household decisions concerning energy consumption. However, recent findings from European smart metering trials have reduced the optimism, suggesting only modest savings from energy feedback. In this paper, we investigate the potential of HEMS to foster reductions in energy use, focusing on a population segment of particular relevance; high-income and highly educated households, considered as early adopters of smart grid technologies. Covering 154 households participating in a field trial in a sustainable city district in Stockholm, Sweden during one year, this study draws on the analyses of smart meter electricity and hot tap water data and in-depth interviews to provide an increased understanding of how feedback and features are perceived, used, and acted upon, and resulting effects on awareness, behavior, and consumption. Our results show that impact on energy consumption varies widely across individual households, suggesting that households respond to energy feedback highly individually. Although HEMS may lead to increased awareness of energy consumption, as well as increased home comfort, several obstacles for energy consumption behavioral change are identified. Drawing from these findings, we suggest policy implications and key issues for future research. [ABSTRACT FROM AUTHOR]

Highlights • The urban energy budget can be reduced by retro-reflective (RR) directional cool materials. • The effect of ageing on RR tiles and paints performance is investigated. • The aged samples are characterized by spectrophotometric, directional and colorimetric analysis. • The cooling potential of new and aged RR materials is evaluated by an algorithm simulation. • The aged materials are performant: the urban cooling effect is preserved. Abstract Cool materials have been proposed for building applications given their potentialities in reducing building energy consumption, urban heat island effects, and global warming. Among cool materials, retro-reflective (RR) ones have been recently proposed for their ability to reflect backwards the incidental striking solar energy. This property is useful in densely urbanized areas, in urban canyons patterns, and in urban areas with buildings of different heights, because it avoids that the reflected energy contributes to the overheating of the neighbor buildings and structures. This study aims to predict the long-term performance of some RR tiles and paints intended for building applications. To this purpose, laboratory accelerated aging tests as described in the ASTM G154 were performed to determine the long-term material properties within a much shorter time than with outdoor weather aging. The samples show minimal changes in the reflectivity, directional reflection, and colorimetry. Finally, this paper shows that the RR character of the investigated materials and their urban cooling potential would be preserved. [ABSTRACT FROM AUTHOR]