Energy Reports最新文献

{"title":"Comparative analysis of building energy simulation using manufacturer-verified performance data: Focusing on variable refrigerant flow (VRF) heat pump system","authors":"Seongju Lee ,&nbsp;Dongsu Kim ,&nbsp;Jaewon Kim ,&nbsp;Jongho Yoon ,&nbsp;Heejin Cho ,&nbsp;Jaeyoon Koh ,&nbsp;Sung Lok Do","doi":"10.1016/j.egyr.2025.108955","DOIUrl":"10.1016/j.egyr.2025.108955","url":null,"abstract":"<div><div>Precise modeling of cooling and heating systems is essential for building energy simulation, particularly for designing high-energy efficiency buildings and achieving low-carbon targets. This study evaluates how performance input conditions affect the accuracy of building energy simulation with a variable refrigerant flow (VRF) heat pump system in EnergyPlus. A validated testbed model, calibrated with field measurements from the test facility in South Korea, was used to compare three case configurations: (Case 1) default EnergyPlus VRF curves, (Case 2) default curves with manufacturer-rated coefficient of performance (COP), and (Case 3) manufacturer-derived curves with the rated COP. Results indicate that simulations using manufacturer-based curves and COP achieved the closest alignment with measured data, while default inputs produced the largest deviations. In whole-building simulations, Case 1 shows substantial error, with CV(RMSE) of 59 %, and Case 2 reduces error, with CV(RMSE) of 13 %. Case 3 presents the most accurate results, presenting CV(RMSE) of 9 %. Simulation using manufacturer-derived curves and rated COP could capture the system’s real-world temperature- and load- sensitive behaviors more effectively. The novelty of this study lies in developing and validating a manufacturer-informed curve-fitting and simulation approach that quantitatively improves EnergyPlus VRF model, including a quantified error reduction from 59 % to 9 %. These findings demonstrate the critical role of precise real-world performance data in enhancing simulation accuracy and present a practical framework for improving reliability in building energy assessments and performance-oriented HVAC system design.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108955"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and analytical evaluation of a modular permanent magnet vernier motor with two-slot-pitch winding configuration for electric motorcycle applications","authors":"Bijan Bijanipour,&nbsp;Alireza Rezazadeh Valujerdi","doi":"10.1016/j.egyr.2025.12.034","DOIUrl":"10.1016/j.egyr.2025.12.034","url":null,"abstract":"<div><div>This paper presents the design, modeling, and experimental evaluation of a 48 V, 1 kW, and 600 rpm permanent magnet vernier motor for electric motorcycle applications. The proposed motor employs a modular stator structure and incorporates a two-slot pitch winding (TSPW) to improve the power factor. Its performance is benchmarked against both a yoke-based configuration and a conventional permanent magnet synchronous motor (PMSM). An enhanced magnetic equivalent circuit (MEC) method is developed for analytical modeling, and its accuracy is validated using finite-element analysis (FEA) and experimental testing of a fabricated prototype. In addition, a comprehensive thermal analysis is performed under four operating scenarios. A three-dimensional thermal simulation, conducted in ANSYS Thermal, provides detailed temperature distributions across critical motor regions. Experimental validation is carried out using embedded thermal sensors, and the measured data show strong agreement with the simulation results.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108897"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility analysis of pumped hydro energy storage at the United States Military Academy","authors":"Benjamin Johnson,&nbsp;Joseph Koh ,&nbsp;Corey James","doi":"10.1016/j.egyr.2025.11.117","DOIUrl":"10.1016/j.egyr.2025.11.117","url":null,"abstract":"<div><div>The terrain and resources surrounding the United States Military Academy at West Point (USMA) make it a viable site for development of a pumped hydro energy storage (PHES) system. Such a system would improve the resilience of the installation’s energy supply, allowing it to maintain operations during grid disruptions and better support on-site distributed energy generation. The present work examines the economic feasibility of PHES at USMA and its potential impact on grid energy usage. Based on actual USMA energy demand, weather, and billing data, a model was developed to calculate the output and net present value (NPV) of stand-alone PHES and hybrid energy systems combining PHES with solar photovoltaics and/or wind energy. Additional analysis evaluates the potential of these systems for backup power generation in the event of a prolonged loss of access to grid energy. All proposed systems were found to be unfavorable based solely on their NPV, although the model developed provides an effective framework for evaluating proposed energy projects at other Army installations.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108858"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal and electrical performance enhancement of solar cell modules using thermally conductive EVA/PVDF/AIN composite encapsulation materials","authors":"He Yongtai ,&nbsp;Li Wentao ,&nbsp;Liu Tingsen ,&nbsp;Rao Hongjiang ,&nbsp;Wang Jianqiu ,&nbsp;Xiao Lixian","doi":"10.1016/j.egyr.2025.12.016","DOIUrl":"10.1016/j.egyr.2025.12.016","url":null,"abstract":"<div><div>To enhance heat dissipation from solar cells, reduce cell temperature, and mitigate the adverse effects of temperature increase on electrical characteristics, a thermal transfer theory and finite element heat transfer model based on the encapsulation structure and materials of the modules for solar cell modules were developed. A study was conducted on the impact of encapsulation material’s thermal conductivity on the temperatures of the module's front surface, on the solar cell itself, and on the back surface. Using aluminum nitride (AIN) as a filler, the EVA/AIN and PVDF/AIN, thermally conductive encapsulation composite materials were prepared. Subsequently, studies were conducted to test the thermal conductivity and mechanical properties of these composite materials. Based on the structure and fabrication process of solar cell modules, three types of thermally conductive solar cell modules were designed using EVA/PVDF/AIN composite with thermal conductivities of 0.395, 0.486, and 0.879 W/(m·K). The electrical characteristics (Short-circuit current <em>I</em>_sc, Open-circuit voltage <em>V</em>_oc, fill factor <em>FF</em>, and Conversion efficiency <em>η</em>) and temperature characteristics of the thermally conductive solar cell modules were investigated. The results indicated that as the thermal conductivity of the encapsulation materials increased, the front surface temperature decreased while the back surface temperature increased. The Temperature of bake surface show a rise of 2.31°C compared to standard modules. Experimental results were generally consistent with theoretical analysis, and the changes in <em>I</em>_sc, <em>V</em>_oc, <em>FF</em>, and <em>η</em> gradually decreased with increasing thermal conductivity.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108879"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental validation and impact analysis of dynamic energy exchange between building and rooftop greenhouse (part 1)","authors":"Jeong-hwa Cho ,&nbsp;In-bok Lee","doi":"10.1016/j.egyr.2025.12.007","DOIUrl":"10.1016/j.egyr.2025.12.007","url":null,"abstract":"<div><div>Rooftop greenhouses (RTGs) integrated with buildings offer a dual benefit of reducing urban carbon emissions and improving overall energy efficiency through dynamic heat exchange between the two systems. This study aims to experimentally validate and analyse the quantitative impact of dynamic energy exchange between an RTG and its host building by coupling Computational Fluid Dynamics (CFD) and Building Energy Simulation (BES) models based on field experiments at a rooftop greenhouse–office testbed in Korea. Natural ventilation was simulated with CFD, and the RNG k–ε turbulence model provided the best agreement with measurements (RMSE = 0.88), representing an 11–17 % improvement over other tested models. Experiments in summer confirmed vertical thermal stratification of 2.8–3.0 °C between upper and lower layers, which was incorporated into a multi-zone BES model. Winter measurements of temperature and energy use were then applied for BES validation. The calibrated model achieved small bias in energy consumption (NMBE: RTG = 2.0 %, Office = 2.1 %), with CvRMSE values of 10.9 % for the greenhouse and 34.2 % for the office zone. Agreement with internal air temperature was strong (R² = 94–98 %; MAE = 0.7–1.7 °C; RMSE = 0.9–2.1 °C), and total energy consumption differed by only 3.6 % for the RTG and 6.1 % for the office. Overall, the validated framework satisfied international protocols and quantitatively demonstrated the energy-saving impact and dynamic thermal interactions between the RTG and the building. These results provide a robust foundation for scenario analyses of energy savings and carbon reduction.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108870"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning for electric vehicle routing: A proximal policy optimization approach with a token-based reward system","authors":"Aishwarya S. ,&nbsp;Nayanatara C. ,&nbsp;Baskaran J. ,&nbsp;Sharmila P.","doi":"10.1016/j.egyr.2025.108928","DOIUrl":"10.1016/j.egyr.2025.108928","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) faces significant challenges due to limited driving range and insufficient charging infrastructure, complicating route planning and increasing travel costs. Although various optimization algorithms have been proposed for the Electric Vehicle Routing Problem (EVRP), critical issues such as trip duration, travel cost, and charging logistics remain. This research presents a novel approach based on Proximal Policy Optimization (PPO) to optimize EV routing, considering factors such as battery capacity, charging time, and travel duration. To enhance system efficiency and sustainability, the framework integrates Distributed Generation (DG) modeling to reduce grid dependency and generation costs. Furthermore, a token-based reward system is introduced to incentivize timely charging, improving charging station utilization and promoting demand-side flexibility. The proposed PPO framework is evaluated against state-of-the-art algorithms on multiple benchmark scenarios, demonstrating up to a 35% improvement in routing efficiency while Distributed Generation–Demand Side Management (DG-DSM) coordination reduces marginal charging prices by approximately 14% and improves the load factor from 0.536 to 0.579. A Monte–Carlo sensitivity analysis using load variations from an Indian Utility 17-bus system, combined with probability density function to assess robustness under stochastic traffic and grid fluctuations. The results underscore its scalability and potential as a practical solution for sustainable EV route planning.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108928"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybridized forecasting and climate change impact analysis of six solar PV technologies under CMIP6-SSP scenarios","authors":"Samuel Chukwujindu Nwokolo ,&nbsp;Theyab R. Alsenani ,&nbsp;Paul C. Okonkwo ,&nbsp;Edson L. Meyer ,&nbsp;Chinedu Christian Ahia","doi":"10.1016/j.egyr.2025.108972","DOIUrl":"10.1016/j.egyr.2025.108972","url":null,"abstract":"<div><div>This study offers a comprehensive investigation into the predicting of global solar radiation and the evaluation of climate change effects on insolation, as well as six photovoltaic (PV) technologies—mono-crystalline silicon (m-Si), poly-crystalline silicon (p-Si), amorphous silicon (a-Si), hybrid silicon (h-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS)—across three carbon-intensive economies: South Africa, India, and China, over three timeframes: 2015–2050, 2051–2100, and 2015–2100. Employing Coupled Model Intercomparison Project – Phase 6 (CMIP6) climate datasets within Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), the authors developed 20 empirical, statistical, ensemble, and machine learning models, resulting in the novel SARIMA-CARIMA-GPM hybrid model, which attained exceptional accuracy (R² &gt; 0.94; RMSE &lt; 0.001) in predicting monthly mean global solar radiation. Findings indicate geographical disparities in prospective solar potential: India demonstrates significant improvements in insolation (up to +2.95 %) under SSP585, facilitating long-term solar growth, particularly during the monsoon and autumn seasons. South Africa exhibits annual stability but experiences significant seasonal declines during MAM (−3.0 %), indicating a necessity for focused seasonal photovoltaic deployment. Conversely, China exhibits sustained long-term reductions in irradiance (up to −3.8 %), primarily attributable to enduring pollutants and cloud cover, with winter insolation remaining alarmingly low. To assess performance variations in photovoltaic modules, two innovative analytical instruments—Seasonal Elasticity Analysis Model (SEAM) and Time Horizon Decomposition Model (THDM)—were created, demonstrating that thin-film (a-Si, CdTe, CIGS) and hybrid (h-Si) modules exhibit greater climate resilience compared to traditional crystalline silicon. The results offer practical guidance for enhancing photovoltaic system design, technology choice, and climate-responsive policy measures.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108972"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic assessment of electric vehicle-based decentralized electricity delivery for enhanced power resilience during outages","authors":"Rehman Zafar ,&nbsp;Asitha Sanjaya Indatissa ,&nbsp;Pei Huang ,&nbsp;Anna-Lena Lane ,&nbsp;BjornssonLars-Henrik Björnsson","doi":"10.1016/j.egyr.2025.108947","DOIUrl":"10.1016/j.egyr.2025.108947","url":null,"abstract":"<div><div>The integration of electric vehicles (EVs) into power systems offers a promising pathway to enhance energy resilience during grid outages. Most of the existing studies treat EV outage support as a short-term, technical problem, with limited attention to economics, business models (e.g., ownership and stakeholders), or long-term outages. To bridge the gap, this study presents a comprehensive framework to evaluate the techno-economic feasibility of using EV mobility as a decentralized electricity delivery solution during both short-term and long-term outages. A rule-based control strategy is developed to operate EVs under realistic operational constraints, with the primary objective of maximizing energy delivery and minimizing unmet demand during outages. The framework incorporates real-world data, including household and critical load profiles, EV battery capacities, ownership types, outage durations, travel distances, and seasonal variations. Scenario-based simulations are performed to compare various technical and economical key performance indicators. Results show that EVs with personal ownership type and large capacity (100 kWh) can effectively meet residential energy needs during short-term outages. In long-term outages, expanding the EV fleet effectively reduce unmet demand, and higher solar generation in summer and spring significantly improves energy availability compared to winter, where unmet load is more than twice as high under identical conditions. The findings offer practical insights for stakeholders and policymakers in developing EV-based resilience strategies for future energy systems. Furthermore, the study highlights the importance of designing appropriate business models to support the integration of mobile energy services into resilience planning.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108947"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PILoN: Physics-informed logistic nonlinear model for photovoltaic performance modelling","authors":"Ambe Harrison","doi":"10.1016/j.egyr.2025.109026","DOIUrl":"10.1016/j.egyr.2025.109026","url":null,"abstract":"<div><div>For over 20 years, Sandia National Laboratories, USA, have been working on improving photovoltaic (PV) performance models. Between 1991 and 2003, they proposed the Sandia Array Performance Model (SAPM), which has become an integral component of the System Advisor Model (SAM) distributed by NREL. This study raises concerns about the suitability of SAPM and exposes accuracy loopholes stemming from repetitive translations of field measurements, which distort the inherent temperature–irradiance physics of PV cells. To address these limitations, this paper proposes a Physics-Informed Logistic Nonlinear (PILoN) model for high-performance PV module/array modelling. PILoN replaces SAPM’s complex, multi-step regression procedure with a unified, physics-informed framework that uses surrogate models for open-circuit voltage and short-circuit current, eliminating the need for difficult-to-measure variables such as air mass. It captures the non-separable coupling between irradiance and temperature using bounded logistic functions for the ratios of maximum power point voltage to open-circuit voltage and current to short-circuit current. A Physics-Informed Particle Swarm Optimization embeds physical constraints and monotonicity priors directly into the calibration objective. Extensive validation across several commercial technologies shows that PILoN achieves superior accuracy, especially under low irradiance where SAPM’s linear assumptions fail. For three benchmark modules (SM55, KC200GT and ST40), PILoN attains voltage RMSE values of 0.16, 0.31 and 0.53 V and current RMSE values of 0.0029, 0.0082 and 0.0046 A, corresponding to voltage and current accuracies above 96 % and 99.5 %. On the Sandia SM55 module, the mean current error of SAPM over low-to-high irradiance is about 46 %, whereas PILoN reduces this to 3.3 % while preserving physical consistency across the full operating range, positioning it as a less measurement-intensive and more robust alternative to SAPM for design, optimization and performance prediction of modern PV systems.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109026"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-inspired puma optimization algorithm for time-series energy consumption forecasting","authors":"Sayed Elkenawy ,&nbsp;Amel Ali Alhussan ,&nbsp;Doaa Sami Khafaga ,&nbsp;Marwa M. Eid","doi":"10.1016/j.egyr.2025.109022","DOIUrl":"10.1016/j.egyr.2025.109022","url":null,"abstract":"<div><div>Accurate energy consumption forecasting represents a critical challenge in smart home environments, where consumption patterns are influenced by complex interactions between internal appliance usage and external environmental factors. Traditional forecasting approaches often struggle to capture the non-linear temporal dependencies inherent in energy consumption data, necessitating advanced methodologies that can effectively model these intricate relationships for enhanced energy management systems. This study presents a novel hybrid forecasting framework integrating the Bio-Inspired Puma Optimization (PO) algorithm with Long Short-Term Memory (LSTM) networks for time-series energy consumption prediction. The methodology employs a comprehensive dataset containing real-time energy consumption data from household appliances coupled with meteorological variables including temperature, humidity, and wind speed. The framework incorporates systematic hyperparameter optimization through the PO algorithm and implements binary Particle Swarm Optimization-guided Whale Optimization Algorithm (bPSO-WOA) for intelligent feature selection. The proposed PO-optimized LSTM model demonstrates superior performance across multiple evaluation metrics, achieving a Root Mean Squared Error (RMSE) of 0.00582 and coefficient of determination (R²) of 0.98060. Comparative analysis reveals significant performance improvements over traditional machine learning approaches, including Gated Recurrent Units, Recurrent Neural Networks, and Artificial Neural Networks. The integration of feature selection techniques resulted in substantial computational complexity reduction while simultaneously enhancing model robustness and interpretability.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109022"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}