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To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019-2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule. [ABSTRACT FROM AUTHOR]

This paper conducted a systematic literature review (SLR) of peer-review documents focused on residential energy consumption. The main finding of this SLR derived from its computational implementation, filling a gap in the available literature. The paper had two main objectives: (i) the SLR itself; and (ii) to systematically identify the main policy measures oriented at reducing energy consumption recommended by the available literature and its related greenhouses gas emissions. A significant lack of a connection between scientific fields was identified. The fields of energy, engineering and environmental science are those that have addressed these topics the most. The sub-field with the largest presence is energy, with 29.5%, followed by engineering (23.9%) and the environment (21.3%). It is recommended that this gap be bridged because many of the main political measures in force to reduce residential energy consumption come from economics and need to be explored from a social science perspective to choose those measures that are more cost-effective. [ABSTRACT FROM AUTHOR]

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building. [ABSTRACT FROM AUTHOR]

Bottom-up energy system models have been used extensively to analyse future energy scenarios, addressing a wide variety of policy questions. This paper focuses on energy efficiency, a key energy, climate, and economic policy area where several examples of energy system model applications can be found in the literature. This paper analyses how different studies implement energy efficiency scenarios in energy system models and explains how the approach taken can affect the results significantly, potentially affecting policy decisions. This analysis contributes to understanding how this type of modelling framework considers energy efficiency policy issues and the extent of insight provided, or not, on different dimensions. With the aim of identifying 'best practice' in using energy system models to inform effective analysis of energy efficiency policy, the UK TIMES energy system model is used to implement five different energy efficiency scenarios for residential heating following different modelling approaches and replicating scenarios available in the literature. The study concludes that energy efficiency scenarios, implementing the same target, produce significantly different results in terms of technology mix, energy use, emissions and costs. Additionally, the outcomes show that there is no overall best energy efficiency scenario, as each impacts on different policy targets, which could come into conflict with each other. [ABSTRACT FROM AUTHOR]

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single- layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user's energy consumption data is collected and pre-processed, and the user's energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents' demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved. The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation. [ABSTRACT FROM AUTHOR]

This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO. [ABSTRACT FROM AUTHOR]

This work employs the carbon emission factor method to offer real-world instances for carbon footprint accounting, allowing for a thorough analysis of the carbon footprint and important influencing elements throughout the materialization stage of prefabricated housing. To identify the 18 important influencing factors that need to be examined from the five stages of building material production, conveyance of building materials, component manufacturing, component transportation, and building, this paper applies the DEMATEL-ISM-MICMAC (Decision-Making Trial and Evaluation Laboratory–Interpretive Structure Modeling–Cross-Influence Matrix Multiplication) model based on data quantification. Following the findings, the case project's physical phase generated a carbon footprint of approximately 4.68 × 106 kg CO2. The building materials' production and processing phase contributed the highest carbon footprint of the entire physical phase, totaling 4,005,935.99 kg CO2, or 88.24% of the total carbon footprint. To determine the centrality and causality of the influencing factors, four major influencing factors—energy consumption of raw materials (S4), construction planning and organization (S15), transportation energy type (S6), and waste disposal (S2)—were identified using the DEMATEL approach. The influencing factor system hierarchy was divided into six levels using the ISM technique. Level L6, which comprises one influencing factor for organizing and planning, is construction planning and organization (S15). Utilizing the MICMAC technique, it was possible to identify the energy consumption of raw materials (S4) as the primary cause of the materialization phase of built dwellings' carbon footprint. The building material production phases have the largest influence on carbon footprints, according to both case accounting and modeling research. The study's findings can offer some conceptual guidance for the creation of low-carbon emission reduction schemes. [ABSTRACT FROM AUTHOR]

Developing countries in hot climate regions such as Saudi Arabia have witnessed rapid population growth, which has led to greater resource consumption as a result of the increased demand for new buildings. This research proposes a multi-objective evaluation of the potential green engineering solutions to conserve energy using a building within the ROSHN housing project, which is one of the mega projects in Saudi Arabia, as a case study for this paper with the aid of simulation software, taking into consideration the context of the sustainability concept. The results showed that traditional passive architectural design, whether courtyards or Mashrabiya, had the nearly greatest influence, with percentages ranging from −4% to −5.15% for varied parameters and designs compared to the base case energy usage. Furthermore, energy efficiency solutions for the building envelope's external insulation and finish system (EIFS) enabled a drop in the U-value that lowered energy usage to −5.40%. However, the wall insulation thickness beyond 300 mm in this system has no substantial influence on energy savings. This research's most clear finding is that a P2P system for PV panels on a district scale can supply enough energy to meet its needs after implementing the optimal strategy of the other proposed solutions. [ABSTRACT FROM AUTHOR]

This paper uses data from the 2015 Residential Energy Consumption Survey to explore the extent to which renters' electricity use in the United States exceeds that of otherwise similar non-renters. Renting households are found to use approximately 9% more electricity than non-renters when controlling for location, socioeconomic, and many appliance-quantity controls. There are multiple factors that explain this extra electricity use, including inferior energy efficiency of appliances, behavioral factors, differences in bill payment responsibilities, and additional reliance by renters on electric space and water heaters. The paper finds that none of these factors are dominant. The phenomenon of renters' (conditionally) higher electricity use is thus best understood as one that emerges from multiple sources. [ABSTRACT FROM AUTHOR]

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth formidable challenges, including non-Gaussian data distributions, unknown cluster counts, and varying feature importance within high-dimensional spaces. This article introduces an innovative learning framework integrating the expectation-maximization algorithm with the minimum message length criterion. This unified approach enables concurrent feature and model selection, finely tuned for the proposed bounded asymmetric generalized Gaussian mixture model with feature saliency. Our experiments aim to replicate an efficient smart meter data analysis scenario by incorporating three distinct feature extraction methods. We rigorously validate the clustering efficacy of our proposed algorithm against several state-of-the-art approaches, employing diverse performance metrics across synthetic and real smart meter datasets. The clusters that we identify effectively highlight variations in residential energy consumption, furnishing utility companies with actionable insights for targeted demand reduction efforts. Moreover, we demonstrate our method's robustness and real-world applicability by harnessing Concordia's High-Performance Computing infrastructure. This facilitates efficient energy pattern characterization, particularly within smart meter environments involving edge cloud computing. Finally, we emphasize that our proposed mixture model outperforms three other models in this paper's comparative study. We achieve superior performance compared to the non-bounded variant of the proposed mixture model by an average percentage improvement of 7.828%. [ABSTRACT FROM AUTHOR]

The interest in sustainability and energy efficiency is constantly increasing, and the noticeable effects of climate change and rising energy prices are fueling this development. The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions. This article presents a detailed literature review relative to modeling approaches and techniques in residential energy use, including case studies to assess and predict the energy consumption patterns of the sector. The purpose of this article is not only to review the research to date in this field, but to also identify the possible challenges and opportunities. Mobility, electrical devices, cooling and heating systems, and energy storage and energy production technologies will be the subject of the presented research. Furthermore, the energy upgrades of buildings, their energy classification, as well as the energy labels of the electric appliances will be discussed. Previous research provided valuable insights into the application of modeling techniques to address the complexities of residential energy consumption. This paper offers a thorough resource for researchers, stakeholders, and other parties interested in promoting sustainable energy practices. The information gathered can contribute to the development of effective strategies for reducing energy use, facilitating energy-efficient renovations, and helping to promote a greener and more sustainable future in the residential domain. [ABSTRACT FROM AUTHOR]

Due to the rapid progress of urbanization in China, the percentage of residential energy consumption out of total energy consumption has increased. This paper uses statistical data from 30 Chinese provinces (autonomous regions and municipalities) from 2000 to 2020 to analyze the impact of urbanization on residential energy consumption and construct an econometric model to test the mechanism. The empirical tests show that the consumption of direct energy (energy that exists in nature in its original form and has not been transformed) is positively U-shaped about the urbanization rate. Furthermore, the impact of economic development on direct and indirect energy consumption is significantly positive. In contrast, the effects of population agglomeration on immediate energy consumption are adverse, and the indirect energy consumption is positive. [ABSTRACT FROM AUTHOR]

Imperfect information on future energy costs can lower the demand for more energy efficient technologies, with clear implications for energy supply, emissions and climate change. These hypotheses are explored using a randomised discrete choice experiment for property rental decisions. Results show that when energy consumption is expressed in physical units (as per the current EU labels) the willingness- to-pay for energy efficiency improvements is small and marginally significant. While bimonthly energy cost information displayed in monetary terms has no effect on the valuation of energy efficiency, annual monetary energy cost information led to a significant increase. This is the first paper to compare short and long-term point of sale energy cost information for household property decisions. There are clear implications for labelling policy for properties--framing energy consumption according to long-term monetary cost increases the demand for energy efficiency. [ABSTRACT FROM AUTHOR]

In the current context of joint efforts towards the decarbonisation of buildings, integrating occupants' comfort and health with latest technological advancements for energy efficiency is at the center of the latest development of research, policies and professional practice. Radiant systems are encountering great success since the low-thickness systems can also be used in renovation projects for both heating and cooling, while guaranteeing optimal comfort. However, dehumidification is often required for optimal radiant cooling operation with no condensation risks, and the great potential of mechanical ventilation systems to optimally address the needs for dehumidification, air renewal, health and energy efficiency appears to be far from its full exploitation in the post-COVID-19 era. The present paper aims at providing a quantification of the energy and financial impacts of the implementation of a controlled mechanical ventilation system (CMV) coupled to a radiant system in a typical residential case study building in Italy. The results show that the sole CMV may decrease primary energy demand and energy costs by more than 30% and contribute to an increase in the smart readiness of the building by 8%, but further incentive policies must be developed to cover the still high investment and maintenance cost. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Under rapid urbanization-induced global warming and resource depletion, growing interest in zero-energy building (ZEB) and zero-emission building (ZEB) technologies have emerged globally to improve energy performance in homes and shape sustainable cities. Although several countries have released ZEB-enhanced strategies and set national standards and policies to promote ZEBs, construction projects are still limited to demonstration projects. This paper reviews global ZEB activities and state-of-the-art technologies for energy-efficient residential building technologies [based on an evaluation of 40 residential buildings]. Over 40 residential buildings on different continents were reviewed, and their technical details and performance were evaluated. Our results show that 62.5% of the buildings achieved the +ZEB standard, 25% of the buildings were net-zero energy buildings, and only 12.5% of the buildings were near-zero energy buildings. Solar PV is the most widely used renewable energy source in the studied cases, while in warmer climates, advanced cooling technologies and heat pumps are the preferred technologies. A building envelope and thermal ventilation with heat recovery are essential in cold climates. Our systematic analysis reveals that the thermal performance of the building envelope and solar energy are the most effective mechanisms for achieving energy efficiency and shaping sustainable cities. [ABSTRACT FROM AUTHOR]

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing energy consumption. A clear causal relationship between water pipe temperature changes, air temperature, and building energy consumption is found, compensating for the neglect of temperature in the SHAP analysis. The findings of this research can help residential building owners/managers make more informed decisions around the selection of efficient heating management systems to save on energy bills. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate bi-directional spillovers into residential and industrial sectors induced by energy efficiency improvement (EEI) in both the short- and long-term, and the impact of nesting structure as well as the size of elasticities of substitution of production and utility functions on the magnitude and the transitional dynamic of rebound effect. Developing a dynamic general equilibrium model, we demonstrate that residential EEIs spillover into the industrial sector through the labor supply channel and industrial EEIs spillover into the residential sector through the conventional income channel. Numerical simulations calibrated on the U.S. suggest that not taking into account these spillover effects could lead to misestimating the rebound effect notably of residential sector EEIs. We also demonstrate how the size and the duration of the rebound effect depend on the elasticities of substitution’s values. Numerical simulations suggest that alternative sets of value for the elasticities of substitution may give different sizable patterns of rebound effects in both the short- and long-term. In policy terms, our results support the idea that energy efficiency policies should be implemented simultaneously with rebound effect offsetting policies by considering short- and long-term economy feedbacks. As a consequence, they require considering debates about what type of policy pathways are more effective in mitigating the rebound effect. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model of national innovation production that formalizes the role of trade partnerships as a channel of knowledge spillovers across countries. The model is used to investigate the energy efficiency technological domain in the European Union (EU) using a panel database covering 19 EU countries for the time span 1990–2015. The model is estimated by using a novel empirical strategy which allows to assess the knowledge spillover effects benefiting a country depending on its relative position in the trade network, and correct for common endogeneity concerns. We show that being central in the trade network is a significant determinant of a country's innovative performance, and that learning-by-exporting mechanisms are responsible for increased innovation performances. We further reveal that neglecting network effects may significantly reduce our understanding of domestic innovation patterns. Finally, we find that the benefits obtained from knowledge diffusion varies with the domestic absorptive capacity and policy mix composition. Our main implication is that policy design informed by network-based case studies could help maximizing the exploitation of positive knowledge spillovers. [ABSTRACT FROM AUTHOR]

Despite the climate commitments made by countries in the Paris Climate Agreement adopted in 2015 and reinforced during COP 21 and with notably less success during COP 22, world carbon emissions increased in both 2021 and 2022. It is increasingly unlikely that the world will achieve the targeted 50% carbon reduction by 2030, the reduction approximately needed for reducing global temperature rise since the beginning of the Industrial Revolution to less than 1.5 deg. C. At the same time, there remain nearly 2 billion people in the world who have no or highly unreliable access to power. In developed countries, access to both clean energy and energy efficiency investment in residences within low to moderate income communities has also lagged. This paper provides a review of the "Productive Use of Energy (PUE)", which is a means to add value to solar energy mini- and micro-grids to ensure investment worthiness and add more value to the communities being served. In this context, it posits an opportunity to leverage Bitcoin mining as a common PUE strategy applicable to new solar installations. Several actual pilot cases are described to demonstrate this potential throughout the world and at multiple scales. These include: (i) existing micro-grids with significant stranded energy to generate income that could be used to reduce the cost per kWh for the community; (ii) new solar micro-grids optimized to meet community load and mining operations; (iii) dedicated solar-powered Bitcoin mining mini-grids developed solely to create a funding stream for self-investment by communities for their benefit; and (iv) a low-income residential solar-powered Bitcoin miner to reduce the energy cost burden for residents. Several of these scenarios show significant potential to aid investment worthiness. [ABSTRACT FROM AUTHOR]

The aim of the study was to identify the state of residential buildings in terms of energy consumption and modernisation in Poland against the background of the EU, and to determine factors and activities increasing households′ interest in net-zero energy buildings. In the European Union, we are observing a tendency to increase the energy efficiency of buildings, including residential, and decarbonise building stock by 2050. The objective of the undertaken activities is to reduce energy use and the negative impacts of the use of buildings on the environment. Attaining this objective requires modernising many buildings. This dissertation includes an investigation into the condition of buildings in Poland from the aspect of energy use and the kind of carriers of this energy, and also into another issue: what factors and activities may increase society′s interest in net-zero energy buildings, and in the use of energy from renewable sources in residential buildings. The studies were conducted with the use of the data provided by the Central Statistical Office and EUROSTAT, and also of the reports of the European Commission, the data of the Central Emission Register of Buildings (CERB) and the Odysse–Mure database. An important source of information was also surveys conducted with the application of CAWI (Computer-Assisted Web Interview), and also of PAPI (Paper and Pencil Interview), among 387 households in Poland. For the purpose of analysing the results of the studies, the methods of descriptive statistics, the chi-square test of independence, the ANOVA test of Kruskal–Wallis and the Mann–Whitney U-test were used. The analysis gives rise to the conclusion that, in residential buildings, household′s annual primary energy demand is dependent on the year in which a building was commissioned. Newer buildings can boast smaller heat energy use. Simultaneously, ever more households are undertaking activities that will result in the thermal modernisation of residential buildings. The studies have shown that the development of net-zero energy buildings requires undertaking activities in the scope of introducing allowances and subsidies, and also increasing social awareness in the scope of this kind of building. Interest in buildings using solely renewable energy sources is contributed to by raising energy prices, and also the falling prices of required installations. [ABSTRACT FROM AUTHOR]

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. [ABSTRACT FROM AUTHOR]

Due to the chronic shortage of energy-related analytical data and disintegration of building energy regulations, numerous existing residential buildings in Petra (Jordan) and many cities worldwide suffer from poor building energy design. This paper aims at investigating the potential of applying energy-saving standards in order to improve the whole-building energy consumption of low-rise residential buildings in mild and dry climate zones. Representative buildings were selected based on a field survey. Proposed strategies focused on applicable solutions such as envelope components, and energy-related systems were set. The models were created using Autodesk Revit, and then the results were generated by the EnergyPlus engine. The findings showed that the application of building energy standards greatly impacts the overall energy end-use, where up to 30% reduction can be achieved by applying the Jordanian code, and up to 45% by applying the American standard. This work provides guidance for the residential building industry and policymakers in Jordan and many other countries with similar building characteristics and climate zones. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa's energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters. [ABSTRACT FROM AUTHOR]

Featured Application: The potential application of the proposed model is a computationally inexpensive semi- or fully automated system for the optimization of operation in residential buildings in terms of energy consumption. Some analyses state that buildings contribute to overall energy consumption by 20–40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REPowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house's real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization. [ABSTRACT FROM AUTHOR]

Wind energy (WE), which is one of the renewable energy (RE) sources for generating electricity, has been making a significant contribution to obtaining clean and green energy in recent years. Fitting an appropriate statistical distribution to the wind speed (WS) data is crucial in analyzing and estimating WE potential. Once the best suitable statistical distribution for WS data is determined, WE potential and potential yield could be estimated with high accuracy. The main objective of this paper is to propose a novel approach for calculating wind energy potential. For this purpose, the Efficient Global Optimization (EGO) technique was proposed for fitting a statistical distribution to WS data and the performance of the technique was compared with genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE). Performance metrics showed that EGO is providing better estimations compared with GA, SA, and DE. Based on Weibull parameters obtained by using EGO, potential WE and potential annual revenue were estimated for Gdańsk, which is the capital of Pomerania Voivodeship in Poland, in the case of having city-type wind turbines in the city center. Estimations for Gdańsk showed that city-type wind turbines might be helpful for producing electricity from WE in the city without being limited by constraints such as having a long distance between wind turbines and buildings. If such wind turbines were erected on the roofs of residential buildings, malls, or office buildings, there is a possibility that part of the electric energy needed for such buildings could be generated using WE. However, this topic should be further investigated from technical and financial perspectives. [ABSTRACT FROM AUTHOR]

Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking. [ABSTRACT FROM AUTHOR]

Over the last 20 years, energy consumption in the residential sector in China has grown rapidly, and the growth is faster than that of any other energy form. To assess the limitations of the spatial characteristics of household energy consumption in urban areas, this paper selected Guangzhou as the research area. Specifically, the old town, core area, central area and peri-urban areas, which best reflect the evolutionary characteristics and spatial differentiation of households, were assessed. Based on the surveyed database of community-scale household energy consumption (N = 1097), the spatial heterogeneity of household energy consumption and carbon emissions at the community scale were assessed through exploratory spatial data analysis and the standard deviation ellipse method. The results report that (1) the main sources of energy consumption in Guangzhou households were water heating equipment, kitchen equipment and refrigeration equipment, which were related to the climatic conditions and cultural traditions of the city. (2) There was significant spatial heterogeneity in the carbon emissions from household domestic energy use in Guangzhou. (3) The economic level, the effects of the Lingnan culture and the characteristics of residents are the main drivers influencing the spatial characteristics of household energy consumption and carbon emissions in Guangzhou. We propose that policy development should actively promote energy-efficient equipment, such as water heating and cooling equipment, in urban households and take full account of the basic household energy needs of residents in old urban and suburban areas while promoting the development of low-carbon buildings. [ABSTRACT FROM AUTHOR]

Climate change mitigation, the economy's decarbonisation, and energy poverty reduction are major challenges globally and for the European Union. However, competing agendas might create trade-off situations that hinder the achievement of these goals. Energy efficiency promotion in the residential sector, through the replacement of space heating and cooling equipment, can be an important solution to simultaneously contribute to reducing energy poverty and carbon emissions whilst improving households' comfort and wellbeing. This paper analyses the regional impact of replacing space heating and cooling equipment on energy poverty levels in the population using the Energy Poverty Vulnerability Index. Moreover, the impact on carbon emissions is also investigated. Results show that increasing equipment efficiency to regulation levels is only effective in reducing winter energy poverty, with a decrease in municipal vulnerability levels of about 18 per cent. Implementing a "deep change" in the heating and cooling equipment stock is significantly effective for reducing winter and summer energy poverty, respectively, 47.8 per cent and 26.3 per cent in average municipal levels, while significantly decreasing potential carbon dioxide emissions by 3554 kilotons. This transformation should be coupled with the improvement of buildings' energy performance and presents various significant challenges regarding financial investment and social justice that should be addressed by authorities at different scales. This study demonstrates the relevance of exploring the impact of space heating and cooling equipment replacement measures on energy poverty, efficiency and carbon emissions at the regional level while providing a replicable method for investigating this subject and producing valuable insights into other geographical contexts. [ABSTRACT FROM AUTHOR]

Through effective demand side management, residential energy consumers can enjoy both lower electricity bills and satisfactory electricity-consuming experiences. Lots of residential appliance scheduling algorithms have been proposed, and consumer satisfaction has attracted more and more attention. In this paper, the consumer satisfaction index is defined, and AHP (Analytic Hierarchy Process) is applied to describe the consumer sensitivity to the usage time of each appliance. The time and appliance models are constructed, by use of which the minimum electricity bill model is set up. Based on these models, the consumer satisfaction-oriented residential appliance scheduling algorithm is developed. A case study shows that the new algorithm can help consumers cut down the electricity bill and use the appliances at their favourite time slots. Compared with the consumer's original energy usage scheme, the optimized scheme reduces the electricity bill by 33.56%. At the same time, the Peak-to-Average Ratio can be lowered by 5.26%. [ABSTRACT FROM AUTHOR]

As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability. [ABSTRACT FROM AUTHOR]

The topic focuses on current solutions of interspaces in the renovation and modernization of residential buildings, the design of which is conditioned by the principles of sustainable development and construction. It concerns the integration of closed interspaces in renovated buildings from the 1950s to the 1980s. One example is the frequent glazing of loggias/balconies in original panel construction. The form of glazing of loggias and balconies is not regulated and is chosen by the apartment owners. For these reasons, the integration of interspaces in renovated buildings should be supported by research that will bring new insights about their effect on assessments of the energy performance of residential buildings and their effect on the architectural expression of facades. The paper compares alternatives for the integration of interspaces with an emphasis on the efferc of their orientation. The research should serve to help regulate the glazing of loggias in residential panel buildings. [ABSTRACT FROM AUTHOR]

There are possible synergies between the decision to invest in energy efficiency measures and to adopt renewable energy, in the sense that the former reduces energy demand so that the latter can further cut future greenhouse gas (GHG) emissions, and which has great potential in the residential sector. Much work has been done in the residential sector on demand for clean energy and on investment in energy efficiency, but to our knowledge there is no specific study that investigates the relationship between the two. This paper fills a gap in the literature, and first shows theoretically that there are relationships of substitution or complementarity between the two decisions depending on the threshold of the cross effect related to the environmental motivation of the consumer. Second, the paper empirically shows that the two decisions are positively interrelated due to unobserved characteristics that determine both decisions. Third, the paper provides differential impact of energy poverty, split incentive problem, dwelling characteristics, commitment and trust on the two decisions. Finally, the paper investigates household characteristics that significantly affect the joint adoption of energy efficient and renewable energy technologies. This contribution can serve to define incentive policies to advance the energy transition. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

Featured Application: Tailoring of energy performance diffusion regarding public and private policies in countries where energy efficiency is not clearly seen as a source of benefits in the residential context. Within the Energy Performance of Buildings Directive information symmetry regarding financial and environmental benefits, coming from real estate performance, is a key element in the promotion of efficient buildings. However, the link between energy efficiency and its perceived co-benefits is far from perfect in EU countries with a short trajectory in the promotion of such residential attributes, endangering the efficacy of information-symmetry policies. Using a large survey applied to sellers, buyers, lessors and tenants this paper explores, in Spain, the perceived co-benefits in terms of energy savings and thermal comfort of efficient homes and whether such perception correlates to current living conditions, housing tenure and sociodemographic profile of respondents. Results suggest that in general such co-benefits are present in the mind of the main agents of the housing market. However, their perception is far from being homogeneous, it is clearer in the case of households whose current home is poorly insulated or inadequately serviced in terms of thermal active systems. Sociodemographic conditions also play a role in co-benefits perceptions: Elder, wealthier and landlords, irrespective of whether they act as sellers, buyers or lessors, do perceive to a larger extent the co-benefits of efficient homes. Such findings signal clear paths for the improvement of energy and housing policy. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results. [ABSTRACT FROM AUTHOR]

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach. [ABSTRACT FROM AUTHOR]

Background: Achieving zero-energy targets in residential buildings is challenging due to improper energy design and the selection of energy-related systems. Moreover, the absence of benchmarks for zero-energy residential buildings, along with the scarcity of studies tailored to diverse climates and building characteristics, highlights the urgent need for further research. This study aimed to address these gaps by designing zero-energy buildings to suit diverse climate zones in Jordan, acting as benchmarks to enhance energy efficiency and promote renewable energy use in the residential sector. Methods: Energy simulation tools were employed to design and verify zero-energy systems. The energy use intensity (EUI) results from the IDA ICE tool were compared with the reported targets and OpenStudio tool outcomes, ensuring that deviations among the proposed designs within the same climate zone consistently remained within acceptable limits, averaging 2, 1, and 1 kWh/m2 year in 1B (very hot dry), 2B (hot dry), and 3B (warm dry), respectively. Additionally, an economic evaluation was conducted by comparing the cost estimates of a Jordanian code-compliant house and the most acceptable proposed zero-energy design. Results: The proposed designs exhibited average EUI values of 64.4, 64, and 60 kWh/m2 in diverse climate zones. Outperforming typical Jordanian houses by 56%, 55%, and 60% in 1B, 2B, and 3B, respectively, these designs surpassed national and international benchmarks by at least 35%. Notably, the proposed zero-energy designs achieved substantial cost savings of 1938 USD, equivalent to 11 USD per square meter, throughout the construction phase. Conclusions: Considering Jordan's ambitious energy strategy for 2030 and the significant energy consumption in the residential sector, the proposed zero-energy building designs play a crucial role in advancing the national transition towards zero-energy buildings. This study provides valuable insights by presenting precise designs, benchmarks, and a comprehensive guide tailored to Jordan's distinctive building and climate characteristics with potential applications beyond its immediate context. [ABSTRACT FROM AUTHOR]

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces' URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China's policymakers. [ABSTRACT FROM AUTHOR]

Energy is one of the crucial aspects now-a-days to be considered from the household chores to the educational, transportation, industrial and many other sectors. Apart from cooking, space heating and cooling also have greater impact as buildings consume about 40% of world's energy use and major part of energy is used for space heating and cooling [1]. Gender is another aspect that should be taken in to consideration in the energy sector. Discrimination between men and women, either it may be in the knowledge regarding energy sector, profession, application and practices etc. is seen in our society. Therefore, this paper seeks to investigate the impacts of space heating and cooling in the energy consumption pattern of Residential buildings. This paper explores how the gender issues in the energy sector can be addressed and how can it lead towards the sustainable development of the society and then nation. At the same time, paper highlights the changes and improvements in the energy consumption pattern with the enrollment of women in the energy sector. The conclusions are derived from the several literature studies and explorative data analysis with the concern of gender relation with the energy efficiency in the space heating and cooling of residential buildings. [ABSTRACT FROM AUTHOR]

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The domestic sector uses the majority of the energy consumed in Palestine. The Palestinian Central Bureau of statistics (PCBS) performs periodic statistics regarding the average energy consumption of the households, but there is no up to date breakdown of the energy consumption in the residential sector. The Palestinian energy sector main challenges are the high fuel prices, lack of sustainable consumption and energy insecurity. Developing energy policies and studying the potential of shifting towards more independent and sustainable energy sources need a clear understanding of the current energy consumption. A survey was used to collect relevant quantitative and qualitative data. The total number of households in Hebron who responded to the survey was 322 households. The survey covered physical characteristics of the dwellings, socioeconomic features of the households in addition to the pattern of heating the spaces, quantity and type of fuel used for heating. On average the households heated only 9.2% of the total area of their houses during the winter days. The survey revealed that households spend 3.5%–21.6% of their monthly income on space heating during winter. The main contribution to knowledge of this paper is a regression model to estimate energy consumption based on the data collected from the survey. The regression model is developed with an R 2 adjusted 0.606 indicating 60.6% accuracy of the predicted energy consumed for heating. The regression model depends on 14 parameters which include the type of settlement, physical characteristics of the dwelling that are housing typology, level of thermal insulation of the walls, the main building materials and the housing age. It also depends on socioeconomic parameters which are the family monthly income and the occupancy period. Moreover, the type of the heating system used, the number of heated months and the nominal heated area were influential parameters in this model. The significance of this paper stems from the methodology that propose an alternative to the energy audit and simulation methods. The proposed method can be used in contexts where a combination of energy resources is used with no proper metering system and with the absence of accurate data that is needed for simulation. [ABSTRACT FROM AUTHOR]

The current European energy policies have an influence on the need to rehabilitate the housing stock in order to meet the objectives of the European Union. Most of this housing stock was built without any type of energy regulation in adverse technical and economic conditions and thus is now energetically obsolete. The major rehabilitation effort required must be approached through actions based on previous quantitative energy knowledge of the existing buildings in order to guarantee the efficiency of energy-retrofitted solutions. This assessment can be carried out through monitoring dwellings conditioned by use patterns; through simulation programs, which do not usually offer faithful representations of energy conditions; or by using test cells, which allow us to evaluate a controlled indoor environment without the influence of users. The objective of this paper is to present the design and performance of test cells as an experimental method for vertical facade analysis in order to tackle the problem of retrofitting residential buildings in a Mediterranean climate, taking into account energy and environment. With this equipment, efficiency and energy savings, as well as illumination and interior air quality, can be simultaneously and comprehensively evaluated. [ABSTRACT FROM AUTHOR]

The global energy crisis has spurred increased investments in energy efficiency and clean energy initiatives; however, the results have fallen short of expected effectiveness. Concurrently, population growth and urbanisation drive a persistent surge in energy demands, especially within the residential sector, significant to overall building energy consumption. Current research focuses on residents' responses to one-shot investments for energy efficiency or clean sources. The renovation wave, involving a massive number of existing buildings, calls for the mobilisation of huge investments that can be hard to afford in the short run. Sustainable behavioural change is complementarily rising as a key asset for maximising the overall estimated energy saving potential. Despite significant efforts to analyse household energy use and promote behavioural transformations, the literature remains gaping about future users, particularly the younger generation, as future leaders of sustainable development who exhibit a more responsible approach towards climate-related issues but also a strong dependency on digital-based solutions, which may influence energy use patterns and living habits, also impacting relations among peers and overall societal sustainability and energy efficiency. This article proposes a systematic literature review to analyse the variables affecting young people's energy behaviour at home. The aim is to investigate the engines and gaps between strategies or tools for behaviour change and the expected effects, then find potential methods to address that barrier to identify a more promising approach, encouraging the younger generation to translate towards more sustainable energy behaviours. [ABSTRACT FROM AUTHOR]

In 2023, residential and commercial sectors together consumed approximately 27.6% of total United States (U.S.) energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data were analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using Residential Energy Consumption Survey (RECS) data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives. [ABSTRACT FROM AUTHOR]