End-use Energy Consumption Research Articles

Socioeconomic factors influencing residential occupancy trends during and post COVID pandemic

The residential building sector accounts for 22% of end-use energy consumption in the United States. Despite the strong influence of occupants’ behavior on energy consumption patterns in residential buildings, the impact of households’ socioeconomic background on occupancy is not well understood in the wake of the COVID-19 pandemic and its aftermath. This study aims to analyze the changes in occupancy patterns of residential buildings in the United States during and after the pandemic (2020–2022) using 14 socioeconomic variables. The American Time Use Survey (ATUS) data are used to define occupancy patterns; then correlation and regression analyses are applied to determine the most significant variables impacting the hours when members are at home. Results suggest employment status and household income level are the most significant variables predicting hours at home. Those under 25 years of age, low-income households, unemployed, and those identifying as Hispanic have most quickly returned to prepandemic (2018–2019) occupancy patterns. The results indicate that post pandemic (2022), occupancy patterns continue to change for those under 55, employed, and middle- and high-income groups, and thus must be monitored moving forward as they continue to evolve. These results are critical to help support ongoing electrification of homes and decarbonization of the electric grid.

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

The inscrutable baseline and the problem of attribution

This paper examines the problem of attribution in the evaluation of energy efficiency program impact. The methodological problem concerns the observability of consumer behavior under the baseline condition of no program intervention. The statistical solution to the problem, which entails randomized exposure of targeted individuals to program influence, is not a viable alternative in most applications. Randomized opt-in and randomized encouragement designs do not conform to this requirement because all targeted individuals are encouraged to participate in the program, resulting in negative exposure bias. Quasi-experimental methods which utilize non-targeted individuals or targeted nonparticipants as baseline surrogates are further subject to selection bias of unknown magnitude and direction. Valid attribution in the general case of unrestricted eligibility depends on prior knowledge of the determinants of measure adoption and program participation. In default of such knowledge, evaluators must rely upon structural assumptions that have no foundation in empirical science. On the other hand, established measurement and verification methods which exploit scientific knowledge of the determinants of end-use energy consumption should be utilized to obtain unbiased estimates of individual measure and gross program energy savings.

An Econometric Analysis of the Energy-Saving Performance of the Italian Plastic Manufacturing Sector

In a scenario characterised by mitigation concerns and calls for greater resilience in the energy sector, energy audits (EAs) emerge as an essential mean for enhancing end-use energy consumption awareness and efficiency. Such a tool allows us to assess the different energy carriers consumed in a productive sector, offering insight into existing energy efficiency improvement opportunities. This opens avenues for research to devise an econometrics-based methodology that encapsulate production sites and their environmental essentials. This paper contributes to the literature by exploiting the EAs received by the Italian National agency for New technologies, Energy, and Sustainable Economic Development (ENEA) in 2019 from the Italian plastics manufacturing sector, matched with Italian firm-based data extracted from the Analisi Informatizzata delle Aziende Italiane (Italian company information and business intelligence) (AIDA) database. In particular, we investigate how the implementation of energy efficiency measures (EEMs) is influenced by a set of contextual factors, as well as features relating to the companies and EEMs themselves. The empirical investigation focuses on the EAs submitted to ENEA in 2019, which was strategically chosen due to its unique data availability and adequacy for extensive analysis. The selection of 2019 is justified as it constitutes the second mandatory reporting period for energy audits, in contrast to the 2022 data, which are currently undergoing detailed refinement. In line with the literature, the adopted empirical approach involves the use of both the OLS and logistic regression models. Empirical results confirm the relevance of economic and financial factors in guiding the decisions surrounding the sector’s energy performance, alongside the analogous influence of the technical characteristics of the measures themselves and of the firms’ strategies. In particular, the OLS model with no fixed effects shows that a one-percent variation in investments is associated with an increase in savings performance equal to 0.63%. As for the OLS model, including fixed effects, the elasticity among the two variables concerned reaches 0.87%, while in the logistic regression, if the investment carried out by the production sites increases, the expected percentage change in the probability that the energy-saving performance is above its average is about 187.77%. Contextual factors that prove to be equally influential include the incentive mechanism considered and the traits of the geographical area in which the companies are located. Relevant policy implications derived from this analysis include the importance of reducing informational barriers about EEMs and increasing technical assistance, which can be crucial for identifying and implementing effective energy solutions.

Simulation of fishery energy consumption and carbon emissions in Zhoushan City based on the long-range energy alternatives planning system model

To understand the current situation and development trend of energy consumption and carbon emissions in the fishery industry, a Long-Range Energy Alternatives Planning System (LEAP)-fishery model for Zhoushan City was constructed, and the trends of energy consumption and carbon emissions in the Zhoushan City fishery industry were analyzed under the business-as-usual scenario, the low-carbon scenario, and the strengthened low-carbon scenario. The results show that under the business-as-usual scenario, the future energy consumption of Zhoushan City’s fisheries in general shows an increasing trend, and the composition of energy consumption in all scenarios is primarily dominated by fossil energy, with diesel and coal predominating. In terms of the structure of end-use energy consumption, fishing vessels and aquaculture consume the most energy, with energy consumption exceeding 30% of total energy consumption in all scenarios. In terms of the emission reduction contribution rate, which the promotion of clean energy generation, the improvement of energy efficiency, and the reduction of aquaculture production play key roles in the low-carbon development of the fishery industry. From an economic perspective, carbon reduction measures such as reduction of aquaculture production and increased efficiency of energy delivery would have significant economic benefits, but these measures have limitations. Finally, suggestions are made to optimize the energy structure, improve the implementation of energy intensity and total energy control, and strengthen public awareness of energy conservation to reduce carbon emissions from the fishery industry, thus making the research in this paper relevant to real-world situations.

Subdividing end-use energy consumption based on household characteristics and climate conditions: insights from urban China

Rapidly increasing household energy consumption poses significant challenges to global warming mitigation and the transition to low-carbon economies, particularly in China. This paper addresses this issue by introducing a comprehensive segmentation model which effectively subdivides household energy usage into five end-uses: cooking/hot water, heating, cooling, lighting, and power. The segmentation model uncovers compelling insights into urban end-use energy consumption patterns across China and variations among provinces. We observe a consistent increase in urban household end-use energy consumption and per capita energy consumption levels over the past decade. Heating and cooking/hot water emerge as the dominant contributors to household energy consumption, accounting for 26% and 40% of the total, respectively. Furthermore, it is found that higher levels of urbanization and improved living conditions are positively correlated with increased power energy consumption. The declining number of household members, primarily due to the prevalence of nuclear families, has resulted in higher energy end-use, particularly in both developed and underdeveloped economic areas. This paper serves as a valuable foundation for understanding and quantifying household end-use energy consumption. The findings contribute to a more comprehensive understanding of energy consumption patterns, facilitating a cleaner and more sustainable transformation of energy consumption structures.

Comparative Analysis of Data-Driven Algorithms for Building Energy Planning via Federated Learning

Building energy planning is a challenging task in the current mounting climate change scenario because the sector accounts for a reasonable percentage of global end-use energy consumption, with a one-fifth share of global carbon emissions. Energy planners rely on physical model-based prediction tools to conserve energy and make decisions towards decreasing energy consumption. For precise forecasting, such a model requires the collection of an enormous number of input variables, which is time-consuming because not all the parameters are easily available. Utilities are reluctant to share retrievable consumer information because of growing concerns regarding data leakage and competitive energy markets. Federated learning (FL) provides an effective solution by providing privacy preserving distributed training to relieve the computational burden and security concerns associated with centralized vanilla learning. Therefore, we aimed to comparatively analyze the effectiveness of several data-driven prediction algorithms for learning patterns from data-efficient buildings to predict the hourly consumption of the building sector in centralized and FL setups. The results provided comparable insights for predicting building energy consumption in a distributed setup and for generalizing to diverse clients. Moreover, such research can benefit energy designers by allowing them to use appropriate algorithms via transfer learning on data of similar features and to learn personalized models in meta-learning approaches.

End-use energy projections: Future regional disparity and energy poverty at the household level in rural and urban areas of India

Regional disparity and energy poverty are major challenges for India on a macro-scale. In this study we explored regional disparity and energy poverty in India's future by formulating a series of unique mathematical functions while analyzing historical time-series of per-capita energy consumption (PEC). The functions formulated have important implications on India's approaches towards rapid urbanization, access to clean energy, and rural electrification. We also analyzed end-use energy consumption in low, mid and high-developing states and examined the impacts of the uncertainty of socio-economic changes. As per our estimates, energy consumption in urban areas, is likely to grow by 3.6 times from 2010 to 2070, whereas that in rural areas is likely to decrease by 0.6 times. Energy poverty among Indian states narrows by 2050, while energy consumption in the mid and high-developing states rises rapidly as urbanization and per-capita income increase. Even with this consumption growth, India's average household PEC in 2070 may remain lower than the world average recorded in 2010. The provision of advanced energy resources and technologies is therefore likely to be an important policy challenge for India, especially in the rural areas of low-developing states, as the country pursues its target of reducing energy poverty and regional disparity.

Impacts of UHI on Heating and Cooling Loads in Residential Buildings in Cities of Different Sizes in Beijing–Tianjin–Hebei Region in China

The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating and cooling energy consumption levels of buildings. However, there is a lack of research on the impact of the UHI on the heating and cooling energy consumption of buildings in cities of different sizes in the Beijing–Tianjin–Hebei urban agglomeration, which is the most urbanized region in northern China. We selected rural reference stations using the remote sensing method, and applied an hourly data set from automatic weather stations, to examine the impact of the UHI on the typical residential building heating and cooling loads in three cities of varied sizes in the Beijing–Tianjin–Hebei urban agglomeration through building energy simulation. The main conclusions were as follows. As the UHI intensity (UHII) increased, the heating load difference between urban and rural areas decreased, while the cooling load difference between urban and rural areas increased in the cities. The average daily heating loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 8.14, 10.71, and 2.79% lower than those in their rural areas, respectively, while the average daily cooling loads in the urban areas were 6.88, 6.70, and 0.27% higher than those in their rural areas, respectively. Moreover, the absolute hourly load differences between urban and rural areas were significantly larger during the heating periods than during the cooling periods, with the former characterized by being strong at night and weak during the day. During the peak energy load period, the contribution of the UHI to the peak load of residential buildings varied between the cities. During the stable high-load period, from 18:00 to 07:00 the next day in the heating periods (from 18:00 to 05:00 the next day in the cooling periods), the hourly loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 3.15 (2.48), 3.88 (1.51), and 1.07% (1.09%) lower (higher) than those in their rural areas, respectively. Our analysis highlights the necessity to differentiate the energy supplies for the heating and cooling of urban buildings in different sized cities in the region.

What is the driving mechanism for the carbon emissions in the building sector? An integrated DEMATEL-ISM model

Clarifying the critical driving factors and the driving mechanism for building carbon emissions is fundamental for carbon peak and carbon neutrality prediction. This study comprehensively identifies 23 representative factors using the literature research and questionnaire survey method. Then, by coupling the decision-making trial and evaluation laboratory (DEMATEL) method and the interpretive structural model (ISM), an integrated DEMATEL-ISM model is developed. The proposed model is further employed to quantify the causal relationships among the factors and establish a multi-level recursive structure model to determine the action mechanism of those drivers. The results show that the 11 cause factors, 12 effect factors and the three most important factors including the economic development level, building end-use energy consumption and end-use energy intensity are identified based on the calculated cause and center degree. Based on the obtained hierarchy, the 23 factors are divided into six levels, among which the population factors, the economic development, the urbanization level and the share of the tertiary sector are the root factors leading to the building carbon emissions. In comparison, policy factors are the most critical inhibiting factors. This study can not only promote the theory and model development for the driving mechanism of carbon emissions, but also help decision-makers develop scientific carbon reduction strategies.

Comparison of energy-efficiency benchmarking methodologies for residential buildings

Benchmarking with defined metrics is an excellent tool to track progress and obtain the desired goals. Benchmarking end-use energy in the residential sector has been a topic of great interest among researchers in recent times. This research analyses the buildings’ energy performance based on a devised benchmarking procedure for residential buildings in the Indian city of Jaipur. Creation of benchmarking system involves collection, analysis and verification of the results obtained from a similar set of data into consideration. This study explores the relationship between the Energy Performance Index (EPI) and with other influencing factors like; Area, end-use appliances (Fridge, AC, Cooler, etc.) to ensure the contribution of each variable towards the buildings’ end-use energy consumption. In this study, information has been collected and analysed from over 2700 houses of Jaipur City. The independent and dependent variables have been identified, and a five-star benchmarking framework has been developed. There are very few studies available investigating the effectiveness of various benchmarking techniques, the domain still lacks a comparative performance study of black-box and gray-box benchmarking techniques. This study has selected two black box approaches and one gray box approach to design a benchmarking model and has compared the energy performance of sample buildings. According to the Pearson and Spearman correlation coefficients, Multiple Linear Regression (MLR) and Bayesian ranking scores are the most consistent for energy benchmarking of the residential building sector. This study proposes a novel implementation of the composite indicator (C.I.) as a platform for designing energy benchmarking tables. Finally, the study concludes with recommendations for future work to employ numerous or hybrid combinations of benchmarking methodologies that are expected to provide a more accurate depiction of the energy performance of buildings.

Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region

Energy benchmarking is an accurate tool to measure, monitor, and reduce end-use energy consumption in the building sector using comparison scenarios. Various studies have applied Bottom-Up energy consumption assessment to compare the energy performance of a group of buildings with a benchmark. The Bottom-Up method mostly relies on simulating an ideal building to develop a benchmark. However, this study has developed a Top-Down energy benchmarking methodology based on the actual energy consumption within a cluster of governmental office buildings. The method presents multi-level benchmarks to provide a detailed policy for improving the energy efficiency of buildings in the short or long-term. In an exploratory study, 26 office buildings in a cold climate region were investigated to identify the multi-level benchmarks. Four general benchmark levels were developed, including the ‘Best Practice, ‘Good Practice’, ‘Benchmark’, and ‘Poor Practice’. In addition, 15 energy efficiency classes were also introduced and applied as a base for developing the ‘Energy Performance Certificate’ method. The results indicate that the benchmark values for electricity and natural gas are 40 and 252 kWh/m2/yr, respectively, while the total energy benchmark is 292 kWh/m2/yr. According to the benchmarking results, 69% of the case studies were inefficient, 23% were labeled ‘C’, and no cases were labeled ‘A’ or ‘B’. The multi-level benchmarking system can provide quick and clear guidance for building designers, operators, and government regulation/enforcement agencies; thus, it can apply at local, regional, and international levels. These benchmark levels establish reference points for measuring and rewarding good performance, on the other hand, it recognizes poor-performance buildings and prioritizes them for energy efficiency improvement. The method can be replicated in various climates and urban scales.

A Review on the Policy, Technology and Evaluation Method of Low-Carbon Buildings and Communities

In order to address global warming, most countries in the world have established carbon neutral targets and are continuously taking action to achieve carbon neutrality. The building sector accounts for 36% of end-use energy consumption and 37% of energy-related CO2 emissions globally, so carbon mitigation in building sector is considered to be the most critical step in completing the “last mile” of global carbon neutrality. Low-carbon buildings and communities are the foundation for achieving low-carbon cities and the key transition to reach the goal of carbon neutrality. Therefore, this review aimed to: (a) provide a comprehensive review of countries’ policies on low-carbon buildings and communities and a theoretical basis for improving the corresponding laws and regulations; (b) investigate low-carbon technologies related to building and community construction and operation, as well as identify the current technology gaps; (c) provide a comprehensive overview of low-carbon buildings and communities assessment systems to analyze and evaluate the economic, technical, environmental and social benefits of current building and community energy systems; and (d) identify enablers and barriers in low-carbon buildings and communities to provide direction for future research. The results of this paper can provide comprehensive insights in to further achieving low-carbon buildings and communities.

Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques

The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR.

Research on Technology Foresight of Terminal Energy Consumption Based on Delphi Survey

In view of China's "carbon peak, carbon neutral" strategy, especially the important point of reaching the carbon peak by 2035 and achieving socialist modernisation, it is important to select a number of priority end-use energy consumption technology topics to promote the company's technological innovation. Based on the results of the two rounds of the Delphi survey, eight end-use energy consumption technologies with a "dual carbon" focus should be prioritised, namely: virtual power plant technology, electric vehicle technology, impact assessment and costing of carbon emissions and reduction, and carbon dioxide capture and storage technology. The results of the Delphi survey show that R&D investment, policy mechanisms and market viability are the main constraints to the completion of these technologies. In response to these constraints, relevant recommendations are made in terms of funding input, institutional development and market development.

Sustainable Building Optimization Model for Early-Stage Design

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.

Modelling least-cost technology pathways to decarbonise the New South Wales energy system by 2050

Deep decarbonisation pathways can enable the state of New South Wales (NSW) in Australia to reach a net-zero emissions reduction goal and contribute to global mitigation efforts to limit temperature rise to 1.5 °C by mid-century. This paper explores minimum cost solutions for achieving the corresponding greenhouse gas (GHG) emissions reduction target for NSW, using an Australian implementation of the TIMES (The Integrated MARKAL-EFOM System) energy system modelling framework. This paper investigated possible decarbonisation pathways and available technology options to reach the target. It includes both a higher emissions reference case scenario and a scenario implementing the NSW state government's target of net-zero emissions by 2050 under the NSW Climate Change Policy Framework, consistent with the international Paris Agreement on climate change, with available and viable well-developed technologies. The findings show that the NSW energy system can continue its shift from fossil fuels to renewables like solar, wind, and hydro and can entirely phase out coal- and gas-fired electricity generation by 2050. The deployment of zero-emissions technologies along with policy supports are crucial to achieving deep decarbonisation of the NSW economy by 2050. In addition, electrification and energy efficiency improvements play a significant role in the end-use sector's energy consumption reduction in the coming decades. This paper shows that the electricity sector is the dominant contributor to emission reductions up to the year 2030, while transport, buildings, and industry sectors are set to decarbonise later in the projection period (2030–2050) along this least-cost trajectory. However, the NSW government's aspirational target of net-zero emissions by 2050 can be achieved by 2039 by offsetting negative emissions.

Building an Energy Efficiency System for Residential Households in the Context of Quantitative Feedback

The way in which households consumed electricity largely influences the total amount of energy used. This paper uses questionnaires to obtain information on the daily electricity consumption behavior of 1,140 households. A comprehensive analysis was used to determine the residential electricity consumption pattern. The study showed that, the traditional residential household electricity consumption patten has some effect on the energy saving strategies, however, it did not provide any scientific or rational information loop system overall. The quantitative feedback platform can be used to improve the level of communication among the residents regarding the electric consumption pattern, and to continuously strengthen residents’ awareness of energy saving. In addition, by combining traditional energy saving strategies with the scientific, and systematic residential household electricity energy saving system as a new idea for reducing end-use energy consumption will be useful and may also help in achieving the goal of energy saving subsequently emission reduction.

Phase change materials in thermally activated building systems: A comprehensive review

Heating and cooling requirements in modern buildings are considerably high, where the building sector contributes 40% to the current end-use energy consumption, and its related CO2 emissions have increased by 1% in the past decade only. Different novel solutions have been proposed in the literature to reduce energy consumption and peak loads while maintaining the same thermal comfort levels within the building envelope. The use of phase change materials (PCMs) as thermal energy storage media is a promising concept for storing energy as latent heat and then releasing it when the temperature is lower than the melting point. This has the potential of shifting and dampening peak loads and stabilizing space air temperatures. PCMs can be integrated into the well-established low-temperature radiant heating technologies and/or the emerging high-temperature radiant cooling ones for additional benefits of enhanced thermal comfort, system size reduction, and annual energy savings. Multiple review articles on radiant systems or the application of PCMs in modern buildings are available in the literature, but there are no review articles focused on the interaction between the two technologies. This review article provides a brief background and a gentle introduction to radiant heating and cooling systems (RHCSs), as well as the use of PCMs in modern building applications. Then, the article comprehensively reviews the relatively limited state-of-the-art research on the use of PCMs in RHCSs to better understand the research progress and identify research gaps and questions to be tackled in future research. The literature shows a generally favorable impact of PCMs on radiant systems, with heating and cooling energy savings up to 54% and 50%, respectively, extended comfortable occupied hours by more than two folds, system cost reductions up to 5%, and payback periods of as low as 3.32 years. Some discrepancies exist among reported studies, especially regarding the performance of PCMs in dual mode (heating and cooling) systems, which is mainly due to the lack of clear integration schemes. It is concluded that the merits of using PCMs in RHCSs are outstanding, but the existing studies are not nearly sufficient to address such complex and dynamic systems. Hence, different future directions have been highlighted to accelerate the progress of this research field, including the need for optimizing the relative position of PCM layers, studying the economic aspects of using PCMs, detailed experimental case studies of full-scale buildings, user-friendly numerical models, and advanced control strategies to handle the slow thermal response of the PCM in RHCSs. Highlights Studies on the use of phase change materials in radiant systems are reviewed. Different integration schemes are analyzed for radiant heating and cooling. PCMs reduce heating and cooling energy consumption by up to 50%. PCMs extend the comfortable occupied hours by more than two folds. A clear lack of advanced control strategies is noticed in the literature.

Factors Affecting Residential End-Use Energy: Multiple Regression Analysis Based on Buildings, Households, Lifestyles, and Equipment

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.