Energy Efficiency最新文献

{"title":"Optimization of residential rooftop PV configurations for maximum energy efficiency and economic viability","authors":"Ahmed Attia","doi":"10.1007/s12053-026-10435-4","DOIUrl":"10.1007/s12053-026-10435-4","url":null,"abstract":"<div><p>This paper proposes an integrated optimization model for residential rooftop photovoltaic (PV) systems, aiming to balance economic viability and operational performance. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model that captures seasonal variations and incorporates reliability indicators such as the Loss of Power Supply Probability (LPSP) and energy density. To efficiently solve the model, a hybrid approach is used: the Relax-and-Fix (R&amp;F) heuristic generates an initial feasible layout, which is subsequently refined using the Branch-and-Bound (B&amp;B) algorithm. In addition, variability in energy density is addressed by constructing an empirical probability distribution, which guides the determination of optimal inter-row spacing across different seasonal conditions. To enhance performance, the model determines the optimal number of PV panels, tilt angle, and inter-row spacing. A case study of a residential building in eastern Saudi Arabia assesses the model's practicality. Sensitivity analysis examines the impact of economic and design factors, such as inflation dynamics and tilt angles, on system viability. Findings reveal that at low interest rates (0%–5%), larger PV systems achieve a lower levelized cost of energy (LCOE) and greater grid independence, whereas interest rates above 7% lead to system downsizing, making solar investments unviable. An optimal tilt angle range of 20°–40° balances energy generation, cost, and land-use efficiency across seasonal variability, while extreme angles degrade economic and operational performance.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovation and urban energy efficiency: a case study of China’ s innovative city pilot policy","authors":"Feifei Xu,&nbsp;Wei Shao,&nbsp;Yixuan Tang","doi":"10.1007/s12053-026-10436-3","DOIUrl":"10.1007/s12053-026-10436-3","url":null,"abstract":"<div><p>Sustainable economic development under energy constraints remains a critical challenge for China. As a new mode of urban development, innovation strategies have become a solution in Chinese practices. Motivated by this context, this study treats the phased rollout of the Innovative City Pilot Policy (ICPP) that started in 2008 as an exogenous policy shock and adopts a staggered difference-in-differences (DID) framework to evaluate its impact on urban energy efficiency. Using a three-stage data envelopment analysis (DEA) model to measure urban energy efficiency, our findings indicate that ICPP has a significant positive effect on energy efficiency in pilot cities. Mechanism analysis from the perspective of factor agglomeration shows that ICPP improves urban energy efficiency by promoting the agglomeration of talent, technology and capital, as well as the synergistic agglomeration of them. Heterogeneity analysis reveals that the ICPP has a greater effect on energy efficiency for non-resource cities, non-industrial cities, cities with a higher administrative hierarchy, and eastern and western cities. These findings have significant policy relevance for countries with backgrounds similar to China’s in alleviating energy constraints and achieving sustainable economic growth.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of thermal properties of lightweight concrete from mechanical parameters using GMDH","authors":"Davut Sevim,&nbsp;Yılmaz Kaya,&nbsp;Hasan Oktay","doi":"10.1007/s12053-026-10434-5","DOIUrl":"10.1007/s12053-026-10434-5","url":null,"abstract":"<div><p>Growing concerns about the energy consumption of heating and cooling systems in buildings have led to an increased demand for construction materials with enhanced insulation performance. Accurate determination of the thermal properties of lightweight concrete (LCW) is vital for evaluating its energy performance; however, conventional high-precision tests are both time-intensive and expensive. In this study, the Group Method of Data Handling (GMDH) model is proposed as a tool for directly predicting the thermal properties of LCW from its mechanical properties. For the experimental study, LCWs were produced using waste rubber, pumice, and expanded perlite aggregates as substitutes. Three prediction scenarios were established to accurately estimate thermal conductivity (λ), specific heat (c_p), and thermal diffusivity (α) from key mechanical properties, including compressive strength (σ_c), tensile strength (σ_t), porosity (ϕ), bulk density (ρ), and ultrasonic pulse velocity (upv). Using the GMDH model, mathematical relationships were established between mechanical inputs and thermal outputs, with the most influential parameters identified through layer-by-layer transformations. A topology consisting of two hidden layers, each with three neurons, was adopted, and the model was benchmarked against five machine learning algorithms. The results showed that GMDH provided high predictive accuracy, particularly for thermal conductivity, with an R² of 0.9957 and low error metrics. These findings indicate that GMDH can serve as an effective and cost-efficient approach for predicting the thermal behavior of LCW, while supporting the sustainable use of alternative aggregates in energy-efficient construction.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12053-026-10434-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can tax credit schemes support energy efficiency measures in residential buildings? A case study for Greece","authors":"Vlasios Oikonomou,&nbsp;Christos Tourkolias,&nbsp;Amaryllis Perotti,&nbsp;Julia Luca Szabo,&nbsp;Stavros Spyridakos,&nbsp;Shima Ebrahimigharehbaghi","doi":"10.1007/s12053-026-10429-2","DOIUrl":"10.1007/s12053-026-10429-2","url":null,"abstract":"<div><p>Alongside subsidies and grants, a standard policy instrument for supporting building energy efficiency upgrades is tax credits. In this study, we assess the case of Greece, which uses both subsidies and tax credits for energy efficiency improvement interventions in residential buildings. We conducted both a socio-economic and a cost–benefit analysis including environmental, multiple benefits, macroeconomic, and private perspectives. Drawing upon data from the Hellenic Statistical Authority including the Households Budget Survey, Eurostat and the Greek National Energy and Climate Plan (NECP), we built a model that evaluates how different tax credits scenarios interact with households’ characteristics. This way, we divided households using a clustering method, based on income, size, and energy use patterns. We explored how clusters and tax credits interact within a baseline, a low macroeconomic return and a high macroeconomic return scenario. The paper concludes that the optimal tax credit rate for middle and high-income households ranges from 40 to 52%, depending on economic and budgetary conditions. We recommend, however, that vulnerable households (below the €1,100 monthly income threshold) receive direct support such as grants, zero-interest loans, or refundable tax credits. This study informs suitable and fair policies adapted to the Greek context and aligned with the building renovation goals set by the Greek NECP.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting energy use and calculating carbon emissions of higher education buildings with COVID-19 lockdown effect","authors":"Blessing, Ibukun Mafimisebi,&nbsp;Bahar Sennaroglu","doi":"10.1007/s12053-026-10433-6","DOIUrl":"10.1007/s12053-026-10433-6","url":null,"abstract":"<div><p>This study uses regression with Autoregressive Integrated Moving Average (ARIMA) errors method incorporating the COVID-19 lockdown effect to analyse the electricity consumption of a UK college’s facilities. The objective is to accurately predict the electricity consumption of three college campuses (labelled B, C, and T) for use in decision-making activities and planning. As benchmarking methods, Holt-Winters’ multiplicative and additive methods, ARIMA, Neural Network Autoregression (NNAR), and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) methods are used to predict electricity consumption. The result identified regression with ARIMA errors as the best-performing model, as it considers the effects of the national lockdowns for Coronavirus based on error measures employed. The Mean Absolute Percentage Error (MAPE) values for forecast accuracy are 4.85% for Campus B, 5.31% for Campus C, and 10.76% for Campus T. The proposed method provides a single forecasting equation for all seasons, enables the inclusion of explanatory variables, including an intervention event, and shows good forecasting performance, even though the sample size is not large. The energy used and carbon emission intensities for each campus dropped significantly during the COVID-19 era compared to the pre-pandemic period. This result showed decreases in energy used intensity (EUI) and carbon footprint (CFP) across the three campuses. However, the significant basal energy use highlights a key opportunity for energy savings during low-occupancy periods. The NetZero carbon implication is that this result could be applied to predict carbon footprint of higher education buildings under similar circumstances.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing energy efficiency through building information modelling: a review of current insights and future prospects","authors":"Shubham Kumar Verma,&nbsp;Sahil Thappa,&nbsp;Yatheshth Anand,&nbsp;Sanjeev Anand","doi":"10.1007/s12053-026-10431-8","DOIUrl":"10.1007/s12053-026-10431-8","url":null,"abstract":"<div><p>In the current era of rapid urbanization, building development has increased significantly. This growth has placed increasing pressure on the architecture, engineering, and construction (AEC) industry to deliver energy-efficient buildings while maintaining quality. Building Information Modelling (BIM) has emerged as a transformative technique which is considered as having a potential to integrate energy performance considerations into architectural design decision-making. However, despite this potential, existing research synthesis remain fragmented and limited in their ability to present a coherent architectural perspective. This review critically examines the role of BIM in improving building energy efficiency from an architectural perspective. The study focuses on how energy-related tools and methods are embedded in design workflows across different project stages. The paper reviews BIM-enabled approaches, including Building Energy Modelling (BEM), artificial intelligence and machine learning, and data-driven digital twin frameworks, synthesising research published over the past two decades. From this synthesis, several key research gaps are identified, including fragmented application across the building lifecycle, limited feedback between operational performance and early design stages, and a persistent emphasis on tool-centric rather than workflow-integrated implementations. The findings show that BIM-enabled energy approaches can significantly improve energy performance when they support architect-led, performance-informed design processes. However, their effectiveness remains constrained by challenges related to workflow adaptation, interoperability, data integration, and professional skill development. By linking BIM-enabled energy approaches to architectural design workflows and decision-making processes, this review highlights strategic directions in which BIM can function as a central integration platform for energy-efficient design. The study underscores the need for lifecycle-oriented and architecturally embedded BIM workflows to support more reliable energy outcomes and contribute to the delivery of sustainable, high-quality built environments.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic growth and environmental trade-offs: energy transition strategies for BRICS nations","authors":"Samir Ul Hassan","doi":"10.1007/s12053-026-10430-9","DOIUrl":"10.1007/s12053-026-10430-9","url":null,"abstract":"<div><p>This study examines the energy–growth–emissions nexus in the BRICS economies, which account for nearly 36% of global energy output and have experienced a 101% increase in CO₂ emissions since 2000. Using balanced panel data for the period 2000–2022, the study investigates the structural constraints and policy drivers shaping the renewable energy transition. The findings reveal that industrial scale generates substantial transition inertia in large economies such as China and India, where persistent coal dependence contributes to 12–18% higher annual emissions. Renewable energy is shown to have a limited short-term impact on economic growth, contributing less than 5% to GDP, due to a 3–5-year lag between investment and measurable economic returns. In contrast, fossil fuels continue to support industrial output and employment—accounting for about 8% of India’s workforce—highlighting the socio-economic trade-offs involved in rapid decarbonisation. Financial dynamics play a critical role: green foreign direct investment reduces emissions intensity by approximately 9%, while non-renewable investment increases it by 14%. Policy interventions, including China’s carbon neutrality commitment and India’s Perform, Achieve, and Trade (PAT) scheme, partially validate and refine the Environmental Kuznets Curve for BRICS nations by lowering emissions elasticity with respect to growth. Overall, the results suggest that effective decarbonisation in BRICS nations requires long-term renewable strategies, targeted green investment, and tailored policy frameworks to decouple economic growth from emissions while maintaining socio-economic stability.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing circularity in EU product policymaking: A case study of a domestic oven using the EcoReport tool","authors":"María González-Torres,&nbsp;Chiara Magrini,&nbsp;Judit Rodriguez-Manotas,&nbsp;Vincenzo Senatore,&nbsp;David Bernad Beltran,&nbsp;Miguel Gama Caldas","doi":"10.1007/s12053-026-10423-8","DOIUrl":"10.1007/s12053-026-10423-8","url":null,"abstract":"<div><p>Although supported by the scientific community and increasingly widespread in industry, the transition from a linear to a circular economy will be hampered if it is not accelerated through adequate policy measures. Therefore, integrating circularity into existing policies becomes imperative. Within the European Union (EU), the Ecodesign framework has been identified as one of the key legislative tools to foster products’ circularity, as it makes it legally possible to transform circularity principles into requirements, e.g. resource use, reparability, durability and recyclability. For the definition of requirements and their level of stringency, an assessment of economic and environmental impacts needs to be conducted. So far, this assessment has been supported by the EcoReport tool, to perform a simplified Life Cycle Assessment. The aim of this paper is to present how the EU is integrating circularity in the assessments that support product policymaking and to describe how the EcoReport tool is being adapted. Using an oven as a case study, the differences compared to the previous version of the tool are identified and discussed. The implications in terms of data requirements and results are presented. A more detailed and time-consuming process for data collection allows for more robust results of the impacts all over the life cycle. Moreover, guidance on how to further integrate circularity strategies, specifically on closing and slowing resource loops, is proposed in order to fill the gaps in the current assessment. However, a balance needs to be maintained, as adding complexity to the assessments could delay the policymaking, hindering the timely development of measures. The paper aims not only to draw lessons from the EU's transition experience but also to advocate their applicability and adaptability to shape policy frameworks across other regions and countries.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12053-026-10423-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biophysical and thermodynamic foundations of energy economics: introducing the novel concept of econobolism","authors":"Xiangdong Xu,&nbsp;Faeze Akbariresketi,&nbsp;Vahid Taghvaee","doi":"10.1007/s12053-026-10428-3","DOIUrl":"10.1007/s12053-026-10428-3","url":null,"abstract":"<div><p>In biophysics and thermodynamics, organisms’ energy use is proportional to their mass, whereby larger organisms are more energy-efficient, according to the Kleiber and Constructal laws, respectively. This raises questions in energy economics about whether energy consumption is proportional to economic size, and whether larger economies are more energy efficient. To answer these questions, this research aims to examine the Kleiber and Constructal laws in the context of energy economics. To this end, it combines machine learning and econometric methods including k-clustering, Fixed Effects (FE), Random Effects (RE), Generalized Least Squares (GLS), and Driscoll and Kraay (<i>The Review of Economics and Statistics, 80</i>(4), 549–560, 1998) standard errors (SCC). These models analyze data from 217 countries between 1974 and 2023 to estimate metabolism rate in the context of energy economics, to introduce a novel concept, called “econobolism rate”. The results of the k-clustering method accept the Constructal law of thermodynamics by indicating that countries with large economic size have more energy efficiency. Moreover, the results of econometric methods accept Kleiber’s law by showing that the estimated econobolism rates range within 81% and 85% which are, respectively, 6% and 10% greater than the metabolic rate of Kleiber’s law, 75%. This result confirms that energy consumption is a sublinear proportion of economic size, which affirms Kleiber’s law where energy consumption of a biological organism is a proportion of its mass. These findings incorporate biophysics and thermodynamics into energy economics by estimating the econobolic rate, which open new horizons in research on the nexus of energy consumption and economic development.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive climate zoning for sustainable energy performance of window systems: A comparative study of unsupervised clustering algorithms","authors":"Mojgan Moradi","doi":"10.1007/s12053-026-10427-4","DOIUrl":"10.1007/s12053-026-10427-4","url":null,"abstract":"<div><p>Reliable climate classification is essential for evaluating building components that strongly impact heating and cooling energy demand. Among building components, windows play a significant role in thermal exchange and are commonly assessed through climate-dependent energy labeling programs. In Iran, the current classification system based on ISO 18292:2011 divides the country into ten climatic zones relying on temperature and solar radiation. However, this classification does not fully reflect Iran’s climatic diversity, potentially misrepresenting window performance and energy consumption. This study proposes an adaptive approach incorporating local climate data to define suitable window properties, improve energy efficiency, and reduce greenhouse gas emissions while supporting the United Nations Sustainable Development Goal (SDG) 11 for resilient urban development. To develop and validate the framework, ten years of historical observed meteorological data from 158 stations of the Iran Meteorological Organization were analyzed based on degree-days and solar irradiance conversion to vertical surfaces. Two unsupervised clustering algorithms, K-means and hierarchical clustering, were adopted to define climatic zones. Ward’s hierarchical method identified 11 clusters validated by the Silhouette Coefficient, while EnergyPlus simulations showed improved annual window heating and cooling performance under the proposed zoning. Kernel interpolation with barriers was utilized to generate a thorough geographical representation of climatic diversity across 468 counties. The proposed approach demonstrates how machine learning and spatial analysis can be integrated to improve climate zoning. The resulting climate map provides a more reliable basis for climate-responsive window design and future updates of national energy performance standards.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}