Energy Reports最新文献

{"title":"Integrated optimization of fin geometry and nanoparticle-enhanced PCMs in shell-and-tube thermal storage systems: A CFD–ML framework","authors":"Fatemeh Isania ,&nbsp;Francesca Martelletto ,&nbsp;Luca Doretti ,&nbsp;Paolo Scotton ,&nbsp;Antonio Galgaro","doi":"10.1016/j.egyr.2025.109009","DOIUrl":"10.1016/j.egyr.2025.109009","url":null,"abstract":"<div><div>This study presents a comprehensive numerical and machine learning-based investigation of a shell-and-tube latent heat thermal energy storage (LHTES) system enhanced through the integration of optimized fin geometry and nanoparticle-enriched phase change materials (NEPCMs). Using Docosane as the base PCM, the effects of various nanoparticles — graphene nanoplatelets (GNP), aluminum oxide (Al<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>), and multi-walled carbon nanotubes (MWCNT) — were evaluated in conjunction with geometric variations in fin number, thickness, length, and material. A detailed computational fluid dynamics (CFD) model was developed using the enthalpy-porosity approach to simulate the phase change process. Results show that increasing fin thickness and length significantly accelerates PCM melting, with 22<!--> <!-->mm-long, 5<!--> <!-->mm-thick copper fins achieving the best performance. Among NEPCMs, MWCNTs at 0.6<!--> <!-->wt% concentration reduced melting time by up to 44% compared to the pure PCM baseline. However, diminishing returns were observed beyond certain fin counts and nanoparticle loadings. To enhance generalizability and design insight, machine learning regression models were trained on CFD data, achieving high prediction accuracy (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>99</mn></mrow></math></span>). Feature importance analysis revealed that fin count and thickness had the strongest influence on melting time, followed by nanoparticle type and concentration. The combined CFD–ML framework enabled the development of optimized design guidelines that balance thermal performance, material cost, and system scalability. These findings demonstrate the potential of integrated fin–nano design strategies in advancing the efficiency of compact thermal energy storage systems for applications such as smart buildings, solar thermal systems, and industrial heat recovery. The integration of ML not only improves predictive capabilities but also reveals non-obvious interactions between fin geometry and NEPCM composition, enabling synergistic thermal optimization beyond conventional parametric studies.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109009"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921511","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":"An enhanced particle swarm optimizer – LSTM model with adaptive variational mode decomposition for solar radiation forecasting","authors":"Blessing Olatunde Abisoye ,&nbsp;Yanxia Sun ,&nbsp;Wang Zenghui","doi":"10.1016/j.egyr.2025.108960","DOIUrl":"10.1016/j.egyr.2025.108960","url":null,"abstract":"<div><div>Improvements in solar radiation forecasting models have several positive effects on the solar energy industry. The existing improved models exhibit shortcomings, including loss of diversity, premature convergence, and convergence to local optima. These can undermine solar energy production. To address these shortcomings, this study proposed a novel hybrid adaptive variational mode decomposition with an enhanced particle swarm optimization for long short-term memory networks (AVMD-EPSO-LSTM). The AVMD strategy eliminates the high-frequency components from the solar radiation dataset using Harris Hawk Optimization (HHO) and VMD. The EPSO comprises a bi-diversity and a tournament elite selection method. The bi-diversity involves affinity propagation clustering (APC), which groups the swarm into subswarms, and a ring topology that searches for the best neighbor in each subswarm. The tournament elite selection finds the best solution from the archived best neighbors. The EPSO algorithm obtains the best LSTM’s hyperparameters that revamp the forecasting model's capability. Several performance metrics and models are used to evaluate and benchmark the proposed model, respectively. The experimental outcomes revealed that the AVMD-EPSO-LSTM model demonstrates exceptional performance in handling loss of diversity, premature convergence, and local optima. Furthermore, the Wilcoxon signed-rank test improvement analysis revealed the model's reliability. Consequently, the AVMD-EPSO-LSTM model can serve as a valuable operational tool for solar energy stakeholders, ensuring the sustainable development of renewable energy.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108960"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921512","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":"Enhancing wind power forecasting accuracy with hybrid temporal convolutional networks and adaptive stacked ensemble learning","authors":"Farhan Ullah ,&nbsp;Xuexia Zhang ,&nbsp;Mansoor Khan ,&nbsp;Muhammad Asghar Khan ,&nbsp;Essam A. Al-Ammar","doi":"10.1016/j.egyr.2025.108959","DOIUrl":"10.1016/j.egyr.2025.108959","url":null,"abstract":"<div><div>The increasing demand for renewable energy sources highlights the importance of accurate wind power forecasting. This paper proposes a novel hybrid adaptive stacked ensemble-TCN approach for improving wind power forecasting accuracy by combining adaptive stacked ensemble learning and temporal neural networks. This approach preprocesses data by removing outliers and identifying important features for accurate forecasting, utilizing data from MERRA2 instruments. The variables of the hybrid adaptive stacked ensemble-TCN approach have been optimized to get optimal results in evaluating it on multiple wind farms in Europe (EuroWind 1, EuroWind 2, and EuroWind 3). The energy effectiveness metrics show that this hybrid approach significantly reduces errors on all three datasets. In particular, the hybrid approach RMSE obtained for EuroWind 1 is 0.0017 (as opposed to 0.0027 for random forest), and its MAE evaluation is 0.0011 (as opposed to 0.0013 for random forest). The RMSE number for EuroWind 2 is 0.0015, the comparison is 0.0019 for random forest, while the MAE value is 0.0006, as opposed to 0.0008 for random forest. Regarding EuroWind 3, the RMSE result is 0.0176 (as opposed to 0.1755 for random forest) while the MAE value is 0.0158 (as opposed to 0.0863 for random forest). These findings show that the hybrid adaptive stacked ensemble-TCN approach is more effective than the current techniques, with RMSE results from 0.0014 to 0.0176 and MAE results between 0.0010 and 0.0158. This approach greatly enhances grid stability, forecasts wind power, and encourages the production of renewable energy sources.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108959"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921515","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":"Self-cleaning behaviour of hydrophobic nanocoating for solar PV panels: Enhancing efficiency and thermal management in dusty environments","authors":"N. Sathiesh Kumar ,&nbsp;Debabrata Barik ,&nbsp;Rebwar Nasir Dara ,&nbsp;Milon Selvam Dennison ,&nbsp;Ayyar Dinesh ,&nbsp;Saravanan Rajendran ,&nbsp;Seepana Praveenkumar","doi":"10.1016/j.egyr.2025.109030","DOIUrl":"10.1016/j.egyr.2025.109030","url":null,"abstract":"<div><div>Solar photovoltaic (PV) panels play a major role in global clean energy generation. This study examines the effect of nanocoatings on the performance of PV panels when exposed to real, harsh outdoor conditions. For understanding the coating effect on the PV panel performance, the panels were coated with PDMS/Zn-TiO₂, PDMS/Zn-Al₂O₃, and PDMS/Zn-SiO₂ nanocomposites. The performance of coated PV panels was compared with manually cleaned and uncleaned panels. The nanocoating applied was hydrophobic in nature. The panels were characterized using high-resolution transmission electron microscopy (HRTEM), UV-Vis spectroscopy, surface wettability, and scanning electron microscopy (SEM) to study the nanocoating molecular distribution, rate of absorption of solar energy, self-cleaning nature, and surface morphology. The results revealed that nanocoated PV panels exhibited superior electrical and thermal performance compared to manually cleaned and uncleaned panels. Notably, the PDMS/Zn-SiO₂-coating allows 300 nm to 400 nm wavelength of solar radiation, which gives a growth in electrical efficiency by 3.67 %, a drop in panel surface temperature by 9 °C, and a drop in convective and radiative heat transfer rates of 48.8 W/m² and 71.6 W/m², respectively, in comparison to uncleaned panels. All panels were tested over a period of 7 weeks, and among them, the PDMS/Zn-TiO₂, PDMS/Zn-Al₂O₃, and PDMS/Zn-SiO₂ nanocomposite-coated panels showed only a 0.4 % drop in electrical efficiency in comparison to uncleaned panels, which exhibited a drop of about 3.1 % in electrical efficiency. The nanocoating provides a durable, passive, and self-cleaning solution that naturally improves thermal regulation, boosts energy output, and reduces operational costs.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109030"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921604","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":"Optimal control strategy to charging and discharging techniques for electric vehicle battery pack optimization based on genetic algorithm and machine learning","authors":"Sercan Yalçın ,&nbsp;Muhammed Yildirim ,&nbsp;Muhammad Attique Khan ,&nbsp;Yang Li ,&nbsp;Bayan Alabdullah ,&nbsp;Yongwon Cho ,&nbsp;Yunyoung Nam","doi":"10.1016/j.egyr.2025.109011","DOIUrl":"10.1016/j.egyr.2025.109011","url":null,"abstract":"<div><div>This paper investigates optimal control strategies for charging and discharging battery packs, aiming to maximize lifespan and performance. The focus is on developing efficient techniques based on Genetic Algorithms (GAs) and machine learning (ML) to optimize battery pack operation. This study uses a proposed GA as a global search engine for optimal control parameters, while integrating Support Vector Machine (SVM) to improve the prediction accuracy of the battery state affected by these parameters. Furthermore, the Deep Reinforcement Learning (DRL) agent is trained in a physics-based simulation environment, such as PyBaMM, directly learning physics-informed, dynamic charging current profiles, unlike traditional DRL studies.The research explores various control parameters, including charging/discharging rates, current profiles, and temperature management, to minimize degradation and maximize energy efficiency. This approach effectively searches the vast solution space to identify optimal control strategies that balance immediate energy demands with long-term battery health. Simulation results demonstrate the effectiveness of the proposed GA-based optimization framework in achieving significant improvements in battery pack lifespan, energy efficiency, and overall performance compared to conventional control methods.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109011"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921678","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":"Optimal parameter extraction of fuel cells based on interval branch-and-bound optimization algorithm","authors":"Raphaël Chenouard ,&nbsp;Ragab A. El-Sehiemy","doi":"10.1016/j.egyr.2025.108932","DOIUrl":"10.1016/j.egyr.2025.108932","url":null,"abstract":"<div><div>Fuel cells play an important role in reducing environmental impacts to produce cleaner electricity. Numerical models are used to simulate their performance and build efficient observers in real use. The accuracy of these models is a major concern, as they can be parameterized by several values. Most of the previous works study the estimation of these parameters using various metaheuristics. While these methods are stochastic and do not provide any proof of optimality, the current paper introduces a global optimization method to accurately bound the optimal root mean square error between the parameterized model and some experimental data. The proposed algorithm is based on a deterministic Interval Branch-and-Bound optimization (IBBO) framework. Interval arithmetic ensures set-based computations to safely bound the objective function value. Four types of fuel cells, with their experimental data, are used to demonstrate the efficiency of the proposed methods. IBBO results are compared with some competing optimization methods used in the literature. They show a better accuracy for the computed feasible solutions (upper bounds) and a guaranteed value of the best possible solutions (lower bounds). This last information is not possible to obtain with metaheuristic algorithms. Compared to other Branch-and-Bound algorithms, IBBO proposes a new mix of mechanisms (e.g. advanced constraint propagation, specific search heuristic and feasible point finding method). Due to the deterministic nature of IBBO, results can be repeated. Its convergence analysis is detailed on four fuel cells from which a real test system based on Scribner technology is used to demonstrate the accuracy and robustness of IBBO on several usage scenarios.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108932"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921681","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":"Smart grid cybersecurity against power system MiTM threats and machine learning-based attack classification","authors":"M Mithul Pranav ,&nbsp;Rithan S. ,&nbsp;Rayappa David Amar Raj ,&nbsp;Archana Pallakonda ,&nbsp;Rama Muni Reddy Yanamala ,&nbsp;Krishna Prakasha K.","doi":"10.1016/j.egyr.2025.12.035","DOIUrl":"10.1016/j.egyr.2025.12.035","url":null,"abstract":"<div><div>Modern power systems increasingly rely on Industrial Internet of Things (IIoT) devices, making them vulnerable to cyber threats, particularly Man-in-the-Middle (MitM) attacks that can intercept and manipulate SCADA communications. This study presents a cybersecurity framework designed to detect, prevent, and localize MitM attacks in smart grids. The framework integrates machine learning-based Intrusion Detection Systems (IDS), encryption using Advanced Encryption Standard in Galois/Counter Mode (AES-GCM) and Salsa20, and attack localization methods. Binary and multiclass classification models are trained to distinguish between benign and malicious traffic, achieving accuracies of 99.80% and 99.90%, respectively. The multiclass model is deployed on PYNQ Z2 board and Google Coral Dev Board, demonstrating real-time inference with hardware-level acceleration. To evaluate encryption robustness, a series of cryptographic security tests were conducted. AES-GCM resisted over 1 million brute-force attempts and flagged all instances of ciphertext tampering via MAC check failures in bit-flipping tests. Chi-square tests produced p-values above 0.95, indicating statistically strong randomness. Salsa20 similarly exhibited high resilience against brute-force attempts. Hamming distances averaged 127.8 bits for 256-bit ciphertexts, confirming sensitivity to key changes. Nonce tests showed keystream divergence above 95%, and Salsa20 ciphertext entropy exceeded 7.98 bits per byte, ensuring unpredictability. This integrated approach addresses critical gaps in existing literature, offering a scalable, high-performance solution for enhancing cybersecurity in power systems against MitM threats.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108898"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921758","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":"Optimizing landfill gas emissions mitigation in Kuwait: Waste-to-energy solutions enhanced by machine learning integration","authors":"Naser S. Alselahi ,&nbsp;Hamad A. AlSaqabi ,&nbsp;Nayef Z. Al-Mutairi","doi":"10.1016/j.egyr.2025.108974","DOIUrl":"10.1016/j.egyr.2025.108974","url":null,"abstract":"<div><div>Rapid population growth and increasing waste generation are intensifying municipal solid waste (MSW) management challenges in Kuwait. Annual MSW production exceeds 2.5 million tons, with cumulative waste expected to reach approximately 61.6 million tons between 2018 and 2038, placing substantial pressure on non-engineered landfills and exacerbating greenhouse gas (GHG) emissions. This study evaluates national-scale waste-to-energy (WtE) strategies—landfill gas (LFG) recovery and incineration—to mitigate environmental impacts and enhance energy recovery. The U.S. EPA LandGEM model was integrated with five machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Neural Network, and Gradient Boosting) to improve methane generation predictions. The optimized Gradient Boosting model reduced prediction error from ∼25 % to &lt; 8 % and achieved ∼95 % accuracy, enabling reliable 20-year power forecasts. Three WtE scenarios were assessed: (A1) incineration-only, (A2) full LFG recovery, and (A3) a hybrid system combining both technologies. Scenario A1, represented by the Kabd WtE facility, treats roughly 1.2 million tons of MSW annually and generates ∼650 GWh/year. Scenario A2 achieves a maximum of ∼6.48 × 10⁵ MWh/year and sustains energy recovery for decades after landfill closure. The hybrid Scenario A3 delivers the strongest performance, producing an average of ∼8.2 × 10⁵ MWh/year and reaching a peak of ∼865 GWh/year—equivalent to ∼3 % of Kuwait’s residential electricity demand—while reducing GHG emissions by ∼48 %. The economic assessment, incorporating avoided landfill operating costs, reduced landfilled tonnage, and electricity revenues, indicates that the hybrid WtE system could yield annual savings of approximately US$30 million while enabling electricity export to neighboring regions. These results demonstrate that ML-enhanced LFG modeling, combined with a hybrid WtE system, can shift Kuwait’s MSW management from disposal-oriented practices toward an integrated resource-recovery framework, offering a scalable model for other rapidly urbanizing arid regions.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108974"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921765","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":"Electricity load forecasting using hybrid prophet–TFT model incorporating weather data","authors":"Nazrul Amin ,&nbsp;Yong-Woon Kim ,&nbsp;Chulung Kang ,&nbsp;Yung-Cheol Byun","doi":"10.1016/j.egyr.2025.11.105","DOIUrl":"10.1016/j.egyr.2025.11.105","url":null,"abstract":"<div><div>Accurate forecasting of electrical consumption is essential for controlling the integration of renewable energy sources and improving the electric grid’s performance. We introduce a new hybrid forecasting method in this study that combines Facebook Prophet’s time-series decomposition with the deep learning capabilities of a Temporal Fusion Transformer (TFT). By leveraging Prophet to model long-term trends and seasonality and using TFT to capture short-term patterns and complex multivariate relationships, the hybrid model effectively bridges statistical and neural forecasting techniques. We incorporate real-time meteorological features from Jeju Island (using data from 2012–2020) to enhance forecast adaptability to weather variations. The proposed Prophet–TFT model achieves high accuracy, outperforming each individual model and other benchmark hybrids. It significantly improves upon baseline predictions, achieving an MAE of 8.4 MW with a MAPE of about 1.43%. The inclusion of weather data, particularly during extreme temperature conditions, further enhances forecasting precision. An attention-based feature importance analysis reveals that key meteorological variables, such as Jeju’s air temperature, Seogwipo’s wind speed, and a heat-discomfort index, are among the most influential predictors. These results highlight the significant role of environmental conditions in shaping electricity consumption. The results illustrate the remarkable efficacy of the hybrid model, interpretable and flexible. It offers a practical tool for grid operators, especially in regions with significant renewable resources and weather-dependent demand, to reliably forecast loads and enhance the stability of power systems.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108846"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921853","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":"Optimization of waste heat utilization from green hydrogen PEM electrolyzers for enhanced energy efficiency in hot climates: A Persian Gulf region airport study","authors":"Ahmad M. Allan ,&nbsp;Ali N. Hasan ,&nbsp;Thokozani Shongwe","doi":"10.1016/j.egyr.2025.108923","DOIUrl":"10.1016/j.egyr.2025.108923","url":null,"abstract":"<div><div>This paper proposes an optimized approach to waste heat recovery and thermal management in green hydrogen proton exchange membrane (PEM) electrolyzers, aiming to significantly enhance both energy efficiency and overall system reliability in hot climate conditions. The integrated system utilizes thermal storage tanks, carefully optimized heat exchangers, and advanced control strategies to effectively repurpose excess heat for useful applications, such as domestic hot water heating and preheating of make-up water. The methodology involves developing a dynamic thermal model of the integrated system, simulating heat flows, storage dynamics, and cooling loads under representative Persian Gulf-region airport conditions. The model integrates heat recovery, thermal storage, and cooling systems, optimized to minimize energy use and CO₂ emissions while maintaining stable electrolyzer operation. A smart control strategy adjusts chiller operation and pump speeds in real time to minimize energy use, shift peak loads to off-peak periods, and maintain optimal operating temperatures for the electrolyzers. Novel chiller scheduling and pump operation strategies further reduce overall energy consumption and improve system performance. Analytical results obtained from simulations show that the proposed optimization reduces air-cooled chiller energy consumption by 28 %, circulation pump energy by up to 56 %, total power cost by 33 %, and CO₂ emissions by 33 % (∼94 tonnes/year). These promising findings highlight not only the technical viability but also the economic feasibility of implementing waste heat recovery systems, ultimately offering a sustainable and practical solution for renewable energy applications specifically tailored for hot climate regions.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108923"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788807","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}