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This paper explores the implementation of Internet of Things (IoT) technology to create a remote-control system for electrical equipment in households. The system offers a unified interface for managing various home appliances, including lights, vertical curtains, and air conditioning units. A private network infrastructure supports a personal cloud service, enabling remote access to appliance controls through the internet. The primary objective of this study is to enhance energy efficiency within households by facilitating convenient and remote management of electrical devices. Through the IoT framework, users can monitor and adjust appliance settings based on environmental conditions, accessed through a web server and user-friendly web applications or smartphone interfaces. Key findings of this paper include the real-time monitoring of electrical parameters through web applications, providing insights into energy usage patterns and opportunities for optimization. By leveraging IoT technology, homeowners can reduce energy consumption and contribute to environmental sustainability while enjoying improved convenience in appliance management. This study underscores the importance of integrating IoT solutions into household infrastructure to promote energy conservation and enhance overall efficiency. The implications of this paper extend to both individual homeowners and broader efforts to mitigate energy consumption in residential settings, ultimately contributing to a more sustainable future. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the "energy performance difference between housing units" (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units. [ABSTRACT FROM AUTHOR]

Residential energy policies for individual dwellings predominantly provide minimum design standards for thermal performance and energy efficiency on a square metre basis. This paper quantifies predicted dwelling energy use on a national scale to enable evaluation of the effectiveness of this policy model. Utilizing Australia as a case study, analysis of 580,956 thermal energy design certificates for new detached dwellings verifies that, although minimum energy efficiency design standards were achieved, predicted energy use for thermal comfort increased by 10.4% per dwelling and 5.6% nationally in 2022 compared to 2018. The study examines the impact of both the number and floor area of new dwellings on dwelling energy use and highlights the disparate jurisdictional outcomes resulting from inconsistent implementation of national policy across multi-state governance. To deliver reductions in household energy use it is imperative that residential energy policies target household energy use, rather than only energy efficiency, of new dwellings, of which floor area is a key determinant. Furthermore, to achieve global climate change objectives, there is a need for complementary policy mechanisms that consider the collective energy use of the residential dwelling sector. [ABSTRACT FROM AUTHOR]

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption. • A novel forecasting combined model is proposed for solar energy consumption. • The proposed combined model outperforms the five benchmark models. • The interpretation of combination weight in the combined model is elaborated. • Four experiments, hypothesis test and multi-step prediction are performed. [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 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]

Improving the energy efficiency of buildings is one of the main challenges facing Europe in the context of climate neutrality policy. In this article, the authors decided to investigate the impact of thermal insulation of building envelopes and the type of ventilation on the energy efficiency of multi-family buildings located in different Polish cities. In accordance with EU directives, economic analyses of the costs incurred during the lifecycle and calculations of C O 2   emissions were carried out for the analyzed facilities. It was determined what measures need to be taken under Polish climatic conditions in order to meet the requirements for improving the energy efficiency of multi-family buildings. The multi-criteria analyses presented in this article provide a voice in the discussion of the issues related to the achievement of the energy performance of buildings with almost zero primary energy demand (nZEB). Based on the conclusions of the research conducted, it was possible to develop general recommendations for investors and designers of multi-family residential buildings characterized by energy efficiency and reduced environmental impact. [ABSTRACT FROM AUTHOR]

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development. • Digital credit resulted in a significant decline of the consumption of traditional energy products. • Impacts of digital credit on electricity consumption and the new-style energy products consumption were not significant. • Digital credit reduced traditional energy products consumption via boosting agricultural mechanization. • Digital credit reduced traditional energy products consumption by adopting precision fertilizer applying technique. [ABSTRACT FROM AUTHOR]

Energy retrofitting of residential buildings is considered a challenge to be tackled not only at building level, but also at urban scale, to give a stronger impulse for a concrete transition towards climate neutrality. This paper presents an easy-to-use analysis method based on the elaboration and scaling-up of data collected through the energy performance certificates (EPCs) to identify different urban energy zones distinguished by their energy performance index. This method is therefore conceived for better informing urban planning decisions, thus boosting more effective energy-sensitive urban planning strategies and eventually to foresee dedicated financial instruments to act in the more energy demanding areas of the city, establishing priorities for integrated strategies. This analytical method has been applied and tested in one municipality in the Emilia-Romagna region, Italy, where the recent urban planning law is pushing municipalities towards the development of urban planning strategies oriented to drastically improve the energy efficiency of the existing building stock. Results show that only a small number of areas manage to reach a good performance with fairly low levels of energy performance index, with the most critical situation found in the most central areas of the capital and in the hamlets. • Easy-to-use method for assessing energy performance of residential building at urban scale is proposed • Method based on data easily available for local authorities, like energy performance certificates (EPCs) and urban morphology • The method supports urban planners in municipalities in taking more targeted decisions for improving energy efficiency • The main output of the method is the definition of an energy performance index (EPI) for different urban contexts • EPI allows to wisely balance economic incentives, identifying areas where multi-purpose interventions are recommended [ABSTRACT FROM AUTHOR]

Based on survey data from Chinese households, this study uses a fixed effects model to examine the impact of car ownership on household energy consumption. The results show that car purchases lead to a significant 46% increase in household energy consumption costs. Purchasing cars leads to a 108% increase in household transportation expenditure (including gasoline expenditure), showing a significant direct effect, while heating costs increase by 17.1%, 12.0%, and 23.3%, respectively, representing an indirect effect. Furthermore, in regions with rapid EV growth, EVs have alleviated the promotion of cars in energy consumption, which is conducive to achieving the goal of carbon neutrality. This study helps provide measures to reduce future household energy consumption and promote the realization of carbon neutrality. [ABSTRACT FROM AUTHOR]

A hybrid workstyle is becoming more common post-COVID-19, and longer occupancy hours at home are increasing household electricity consumption. Small homes are regarded as a potential for improving energy efficiency in the residential sector, and a home consists of mixed-function spaces with dynamic occupancy behaviors. These underpin the opportunity to optimize presence sensing lighting in small homes for energy efficiency and user-behavioral needs. A comprehensive overview of presence sensing approaches, comparing four types of non-wearable sensors connected to home lighting is made. A bibliometric mapping of the reviewed literature visually reinforces a significant research gap that presence sensing studies were not connected to home lighting but inclined toward the commercial and institutional context. Next, a non-exhaustive example of commercially available presence sensing products applicable to residential lighting for small homes is analyzed, and their general characteristics and technologies are synthesized. The literature and product overview identified five significant product knowledge gaps. Incorporating the gathered information leads to the proposal of a conceptual flexible radar-based sensor (prototype design), addressing a wish list with three important criteria to optimize future presence sensing lighting in a mixed-function small home. Future radar sensing studies are expected to develop an anticipatory lighting system that processes real-time multi-user vital signals for smarter localized and personalized lighting options for (small) living environments. [ABSTRACT FROM AUTHOR]