Energy and AI最新文献

{"title":"Data-driven analysis of hysteresis and stability in perovskite solar cells using machine learning","authors":"Sharun Parayil Shaji ,&nbsp;Wolfgang Tress","doi":"10.1016/j.egyai.2025.100503","DOIUrl":"10.1016/j.egyai.2025.100503","url":null,"abstract":"<div><div>Perovskite solar cells are promising photovoltaic devices because of the high defect tolerance and desirable optoelectronic properties of the metal-halide perovskite absorber materials. The transition from lab to industry is still an open problem, which is mainly limited by upscaling and stability. In this study we try to use tools from data science namely Pearson correlation and random forest regressor applied to the data from the open-source platform “Perovskite Database” to understand the correlations with material choice, fabrication techniques, and current-voltage key features to the stability and hysteresis index. We find that the cell stack as a whole plays a crucial role in hysteresis and not a single layer. We statistically confirm that p-i-n and higher-efficient solar cells generally show reduced hysteresis. We identify certain cross correlations, which would lead to wrong conclusions e.g. claiming an open-circuit voltage not correlated with the hysteresis or some apparent correlations with material parameters, which originate from the historical development. Regarding stability, we are not able to obtain good performance from the machine learning model. Reasons are non-standardized measurements and lack of sufficient data.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100503"},"PeriodicalIF":9.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Reinforcement Learning for decentralized peer-to-peer energy trading","authors":"Zhian Ye ,&nbsp;Dawei Qiu ,&nbsp;Shuangqi Li ,&nbsp;Zhong Fan ,&nbsp;Goran Strbac","doi":"10.1016/j.egyai.2025.100500","DOIUrl":"10.1016/j.egyai.2025.100500","url":null,"abstract":"<div><div>The rapid development of distributed energy resources has led to an increasing number of prosumers enhancing their energy utilization, thereby raising the demands on energy management technologies. As a result, the development of future smart grids is becoming increasingly important, with a particular emphasis on integrating demand-side flexibility into electricity market. To facilitate distributed interaction among prosumers, the double-side auction market enables peer-to-peer (P2P) energy trading, maximizing the social welfare within the dynamic local electricity market. In this setup, prosumers can set their own bidding prices and optimize their operations and trading strategies. However, trading in double-side auction market faces limitations due to the complexity of the market clearing algorithm and the difficulty of predicting other participants’ bidding behaviors. To address these challenges, this paper models the P2P energy trading problem in the double-side auction market as a multi-agent reinforcement learning (MARL) task. The concept of federated learning is introduced to enhance scalability among market participants while protecting the private information of individual prosumers. Additionally, the parameter-sharing framework is proposed to accelerate the learning process. To further improve the stability of MARL training, the global information of P2P energy trading price is integrated into the critic network. The proposed federated MARL algorithm is evaluated using a real-world open-source dataset from an European residential community of 250 households with a 15-minute resolution. The evaluation assesses both the training performance of the algorithm as well as the economic and operational benefits of the P2P energy trading market compared to a traditional electricity retail market.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100500"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient estimation of convective cooling of photovoltaic arrays: A physics-informed machine learning approach","authors":"Dapeng Wang,&nbsp;Zhaojian Liang,&nbsp;Ziqi Zhang,&nbsp;Mengying Li","doi":"10.1016/j.egyai.2025.100499","DOIUrl":"10.1016/j.egyai.2025.100499","url":null,"abstract":"<div><div>Convective cooling by wind is crucial for large-scale photovoltaic (PV) systems, as power generation inversely correlates with panel temperature. Therefore, accurately determining the convective heat transfer coefficient for PV arrays with various geometric configurations is essential to optimize array design. Traditional methods to quantify the effects of configuration utilize either Computational Fluid Dynamics (CFD) simulations or empirical methods. These approaches often face challenges due to high computational demands or limited accuracy, particularly with complex array configurations. Machine learning approaches, especially hybrid learning models, have emerged as effective tools to address challenges in heat transfer design optimization. This study introduces a method that combines Physics-Informed Machine Learning with a Deep Convolutional Neural Network (PIML-DCNN) to predict convective heat transfer rates with high accuracy and computational efficiency. Additionally, an innovative loss function, termed the ”Pocket Loss”, is developed to enhance the interpretability and robustness of the PIML-DCNN model. The proposed model achieves relative estimation errors of 2.5% and 2.7% on the validation and test datasets, respectively, when benchmarked against comprehensive CFD simulations. These results highlight the potential of the proposed model to efficiently guide the configuration design of PV arrays, thereby enhancing power generation in real-world operations.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100499"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positive and negative convolution cross-connect neural network for predicting the remaining useful life of lithium-ion batteries","authors":"Gwiman Bak ,&nbsp;Youngchul Bae","doi":"10.1016/j.egyai.2025.100507","DOIUrl":"10.1016/j.egyai.2025.100507","url":null,"abstract":"<div><div>This study introduces the positive and negative convolution cross-connect neural network (PNCCN), a novel deep learning framework designed for accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs). The model leverages the positive and negative convolution (PNC) and nonlinear cross-connect (NCC) architectures to effectively capture complex nonlinear interactions and degradation patterns in battery data. The PNCCN model was developed and evaluated using a comprehensive dataset comprising 118 battery cells, processed at 10 s intervals during charge-discharge cycles. The training, validation, and test datasets were divided in a 60:20:20 ratio to ensure robust performance evaluation across diverse operational conditions. By excluding internal resistance (IR) data, the model simplifies data acquisition, reduces dependency on costly sensors, and improves the practicality of battery management system (BMS) integration. The PNCCN model achieved an average root mean square errors (RMSEs) of 9.47 and 93.58 cycles for training and test datasets, respectively, with mean absolute percentage errors (MAPEs) of 1.03 % and 8.28 %. Comparative analysis demonstrates that the PNCCN model outperforms existing methods, offering a reliable and scalable solution for LIB RUL prediction. These results highlight the model's potential for real-world applications, emphasizing its effectiveness in reducing system complexity and enhancing predictive accuracy without relying on IR data.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100507"},"PeriodicalIF":9.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interdependent design and operation of solar photovoltaics and battery energy storage for economically viable decarbonisation of local energy systems","authors":"Dacheng Li ,&nbsp;Songshan Guo ,&nbsp;Jihong Wang ,&nbsp;Yongliang Li ,&nbsp;Chenggong Sun ,&nbsp;Geng Qiao ,&nbsp;Chaomurilige ,&nbsp;Yulong Ding","doi":"10.1016/j.egyai.2025.100505","DOIUrl":"10.1016/j.egyai.2025.100505","url":null,"abstract":"<div><div>Local energy systems are undergoing significant transformation by integrating more solar photovoltaics (PVs) and battery energy storage systems (BESS) to achieve net-zero targets in the energy sector. To ensure an affordable and sustainable decarbonisation process, optimising both system design and operation together is crucial for maximising system profitability and encouraging broader stakeholder participation in the energy transition. However, the complex interdependent influence on the system economic flows, along with the nonlinear characteristics of the system, make the economic optimisation extremely challenging. To address this, we developed a new framework based on advanced artificial intelligence to exploit a wider arbitrage margin under various trading mechanisms, including net metering, day-ahead, and dynamic frequency. We conducted optimisation study on a local energy system operating at University of Warwick using real data from demonstrated BESS and solar PVs, and the effectiveness of the proposed intelligent approach was validated, and the necessity of interdependent optimisation was highlighted. Results showed that, compared to the original campus system (20 MW-level), a carbon reduction rate of up to 61.4 % was achieved through net metering trading, while a maximum annual profit increase of 251 % was realised with dynamic frequency trading. The proposed intelligent framework can be applied to any energy systems with integrated solar PVs and BESS, where the adopted trading mechanism are associated with the system design and operation. The findings offer a practical tool for academics, investors, and policy makers to collaborate in the deployment of renewable energy and energy storage to accelerate the decarbonisation of energy supply.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100505"},"PeriodicalIF":9.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach","authors":"Sujan Ghimire ,&nbsp;Ravinesh C. Deo ,&nbsp;Konstantin Hopf ,&nbsp;Hangyue Liu ,&nbsp;David Casillas-Pérez ,&nbsp;Andreas Helwig ,&nbsp;Salvin S. Prasad ,&nbsp;Jorge Pérez-Aracil ,&nbsp;Prabal Datta Barua ,&nbsp;Sancho Salcedo-Sanz","doi":"10.1016/j.egyai.2025.100492","DOIUrl":"10.1016/j.egyai.2025.100492","url":null,"abstract":"<div><div>Accurate prediction of electricity price (<span><math><mrow><mi>E</mi><mi>P</mi></mrow></math></span>) is crucial for energy utilities and grid operators for enhancing the energy trading, grid stability studies, resource allocations and pricing strategies, thereby improving the overall grid reliability, efficiency, and cost-effectiveness. This study introduces a novel D3Net model for half-hourly <span><math><mrow><mi>E</mi><mi>P</mi></mrow></math></span> prediction, integrating Seasonal-Trend decomposition using LOESS (STL) and Variational Mode Decomposition (VMD) with Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and Tabular Neural Network (TabNet). The methodology involves applying STL to the <span><math><mrow><mi>E</mi><mi>P</mi></mrow></math></span> time-series to extract trend, seasonal, and residual components. The trend is predicted using an MLP model, the seasonal component is further decomposed with VMD into 20 Variational Mode Functions (VMFs) and predicted using an RFR model, and the residual component is decomposed with VMD and predicted using the TabNet model. Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm. The final prediction combines the trend, seasonal, and residual components’ predictions. Explainable Artificial Intelligence (<em>xAI</em>) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statistical significance of the D3Net model. The D3Net achieved the highest global performance indicator for South Australia (<span><math><mrow><mi>G</mi><mi>P</mi><mi>I</mi><mo>≈</mo><mn>11</mn><mo>.</mo><mn>068</mn></mrow></math></span>) and Tasmania (<span><math><mrow><mi>G</mi><mi>P</mi><mi>I</mi><mo>≈</mo><mn>12</mn><mo>.</mo><mn>206</mn></mrow></math></span>). These results validate the efficacy and statistical significance of the D3Net model, demonstrating the viability of integrating STL and VMD decomposition approaches with MLP, RFR, and TabNet for <span><math><mrow><mi>E</mi><mi>P</mi></mrow></math></span> prediction.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100492"},"PeriodicalIF":9.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data efficiency assessment of generative adversarial networks in energy applications","authors":"Umme Mahbuba Nabila ,&nbsp;Linyu Lin ,&nbsp;Xingang Zhao ,&nbsp;William L. Gurecky ,&nbsp;Pradeep Ramuhalli ,&nbsp;Majdi I. Radaideh","doi":"10.1016/j.egyai.2025.100501","DOIUrl":"10.1016/j.egyai.2025.100501","url":null,"abstract":"<div><div>This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100501"},"PeriodicalIF":9.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topology-informed machine learning for efficient prediction of solid oxide fuel cell electrode polarization","authors":"Maksym Szemer ,&nbsp;Szymon Buchaniec ,&nbsp;Tomasz Prokop ,&nbsp;Grzegorz Brus","doi":"10.1016/j.egyai.2025.100495","DOIUrl":"10.1016/j.egyai.2025.100495","url":null,"abstract":"<div><div>Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming electrode microstructure into a format compatible with artificial neural networks. Input data may range from a comprehensive digital material representation of the electrode to a selected set of microstructural parameters. The chosen representation significantly influences the performance and results of the network. Here, we show a novel approach utilizing persistence representation derived from computational topology. Using 500 microstructures and current–voltage characteristics obtained with three-dimensional first-principles simulations, we have prepared an artificial neural network model that can replicate current–voltage characteristics of unseen microstructures based on their persistent image representation. The artificial neural network can accurately predict the polarization curve of solid oxide fuel cell electrodes. The presented method incorporates complex microstructural information from the digital material representation while requiring substantially less computational resources (preprocessing and prediction time <span><math><mrow><mo>≈</mo><mtext>1</mtext><mspace></mspace><mtext>min</mtext></mrow></math></span>) compared to our high-fidelity simulations (simulation time <span><math><mrow><mo>≈</mo><mtext>1</mtext><mspace></mspace><mtext>h</mtext></mrow></math></span>) to obtain a single current-potential characteristic for one microstructure.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100495"},"PeriodicalIF":9.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning identification of Electric Vehicles from charging session data","authors":"Federico Ferretti,&nbsp;Antonio De Paola","doi":"10.1016/j.egyai.2025.100502","DOIUrl":"10.1016/j.egyai.2025.100502","url":null,"abstract":"<div><div>Alternating Current (AC) charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles (EVs). However, the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs, as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself. A straightforward and interoperable method for extracting information from charging vehicles (e.g., vehicle model, battery capacity, and State of Charge) could significantly enhance the implementation of advanced smart charging strategies, unlocking the flexibility of connected EVs, enabling cost reductions and supporting the provision of ancillary services to the grid. This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors. The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs (e.g., brand, model, year, battery capacity, End-of-Charge status) by exclusively considering their charging profile in response to specific prescribed current setpoints. Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs, an essential component in vehicle-to-grid (V2G) applications. Extensive practical demonstrations based on experimental data are provided to validate the identification procedure. An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100502"},"PeriodicalIF":9.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning","authors":"Payam Fatehi Karjou,&nbsp;Fabian Stupperich,&nbsp;Phillip Stoffel,&nbsp;Drk Müller","doi":"10.1016/j.egyai.2025.100490","DOIUrl":"10.1016/j.egyai.2025.100490","url":null,"abstract":"<div><div>Thermostatic Radiator Valves (TRVs) are a widely used technology for regulating room heating in Europe countries. Smart TRVs can provide significant energy savings, often ranging from 20–40% compared to conventional heating systems. They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. They can often be easily retrofitted to existing heating systems, making them a practical option for enhancing energy efficiency in present buildings, especially in office buildings due to their highly dynamic operational patterns. This work presents a novel human-in-the-loop control strategy for Internet of Things (IoT)-based TRVs using Deep Reinforcement Learning (DRL). A key focus of this research is enhancing the adaptability of agents’ behavior by implementing a more generic and flexible Markov Decision Process (MDP) to promote policy generalization across diverse scenarios. The study explores the challenges of transferring control behaviors from simulation environments to real-world settings, examining the performance across different thermal zones and evaluating the integration flexibility of the control strategy within building systems. Real-world occupant behavior is incorporated, including dynamic comfort preferences and occupancy predictions, to better align thermostat operation with user preferences. Furthermore, this paper discusses the practical challenges encountered during implementation, including battery consumption of IoT devices, integration of occupancy detection and prediction systems, and maintenance requirements. By addressing these issues, the proposed control strategy seeks to improve the scalability and feasibility of IoT-based TRVs, thereby providing a viable solution for their widespread deployment in buildings.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100490"},"PeriodicalIF":9.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}