Energy and AI最新文献

{"title":"Uncovering the location of photovoltaic power plants using heterogeneous remote sensing imagery","authors":"Siyuan Wang ,&nbsp;Bowen Cai ,&nbsp;Dongyang Hou ,&nbsp;Qiance Liu ,&nbsp;Xiaoyu Zheng ,&nbsp;Jinyang Wang ,&nbsp;Zhenfeng Shao","doi":"10.1016/j.egyai.2025.100527","DOIUrl":"10.1016/j.egyai.2025.100527","url":null,"abstract":"<div><div>Accurate monitoring of photovoltaic (PV) spatial distribution using remote sensing imagery is critical for understanding energy production dynamics. The integration of spatial and spectral features facilitates precise identification of diverse PV installation scenarios. However, existing methods primarily depend on single-source multispectral or high-resolution imagery, limiting their ability to balance spatial detail and spectral richness. To address this, this paper proposes a spatial-spectral differential semantic fusion network named FusionPV to comprehensively map PV locations within complex geographical environments. First, a spatial-spectral differential semantic aware module (SDAM) is proposed to extract spatial and spectral features related to PV discrimination from multimodal images. Subsequently, a dual-domain adaptive cross-fusion module (DAFM) is designed to deeply aggregate and cross-focus multimodal information using a cross-attention mechanism. Furthermore, a local-global semantic aggregation module (LGAM) is introduced to construct global descriptors by locally encoding and aggregating images, thereby enhancing contextual comprehension of intricate scenes. We construct a multimodal PV dataset by integrating GF-2 and Sentinel-2 imagery, focusing on Hubei Province, China. Experimental results demonstrate that FusionPV outperforms five state-of-the-art methods, achieving Kappa coefficient improvements ranging from 3.78 % to 7.23 %. Additionally, a comparison with four existing PV products indicates that FusionPV is a superior solution for acquiring a high-quality, extensive database of PV locations.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100527"},"PeriodicalIF":9.6,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning-based energy management for hybrid electric vehicles: A comprehensive up-to-date review on methods, challenges, and research gaps","authors":"Mohamed Nadir Boukoberine ,&nbsp;Muhammad Fahad Zia ,&nbsp;Tarek Berghout ,&nbsp;Mohamed Benbouzid","doi":"10.1016/j.egyai.2025.100514","DOIUrl":"10.1016/j.egyai.2025.100514","url":null,"abstract":"<div><div>Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles. Reinforcement learning algorithms offer various advantages, including fast convergence, broad applicability, stability, and robustness, particularly with the integration of deep and transfer learning. This paper provides a comprehensive understanding of reinforcement learning principles and a critical review of various reinforcement learning methods, states, actions, and rewards used to optimize the energy management performance of hybrid electric vehicles. Furthermore, the advantages and limitations of these algorithms are also discussed. This review reveals that deep reinforcement learning techniques show superior performance in handling complex energy management tasks thanks to their ability to learn from high-dimensional state spaces. Nevertheless, their implementation faces notable obstacles, including computational complexity and generalization across diverse driving conditions. Finally, key research directions for future work and challenges are highlighted in the domain of reinforcement-learning-based hybrid electric vehicle energy management.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100514"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging neural networks to optimize heliostat field aiming strategies in Concentrating Solar Power Tower plants","authors":"Antonio Alcántara ,&nbsp;Pablo Diaz-Cachinero ,&nbsp;Alberto Sánchez-González ,&nbsp;Carlos Ruiz","doi":"10.1016/j.egyai.2025.100520","DOIUrl":"10.1016/j.egyai.2025.100520","url":null,"abstract":"<div><div>Concentrating Solar Power Tower (CSPT) plants rely on heliostat fields to focus sunlight onto a central receiver. Although simple aiming strategies, such as directing all heliostats to the receiver’s equator, can maximize energy collection, they often result in uneven flux distributions that cause hotspots, thermal stresses, and reduced receiver lifetimes. This paper presents a novel, data-driven approach that combines constraint learning, neural network-based surrogates, and mathematical optimization to address these challenges. The methodology learns complex heliostat-to-receiver flux interactions from simulation data and embeds the resulting surrogate model in a tractable optimization framework. By maximizing a tailored quality score that balances energy collection with flux uniformity, the approach produces smoothly distributed flux profiles and mitigates excessive thermal peaks. An iterative refinement process, guided by a trust region strategy and progressive data sampling, ensures continual improvement of the surrogate model by exploring new solution spaces at each iteration. Results from a real CSPT case study show that the proposed approach outperforms conventional heuristic methods, delivering flatter flux distributions with nearly a 10% reduction in peak values and safer thermal conditions (reflected by up to a 50% decrease in deviations from safe concentration distributions), without significantly compromising overall energy capture.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100520"},"PeriodicalIF":9.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated reinforcement learning for sustainable and cost-efficient energy management","authors":"J. Sievers ,&nbsp;P. Henrich ,&nbsp;M. Beichter ,&nbsp;R. Mikut ,&nbsp;V. Hagenmeyer ,&nbsp;T. Blank ,&nbsp;F. Simon","doi":"10.1016/j.egyai.2025.100521","DOIUrl":"10.1016/j.egyai.2025.100521","url":null,"abstract":"<div><div>Integrating renewable energy sources into the electricity grid introduces volatility and complexity, requiring advanced energy management systems. By optimizing the charging and discharging behavior of a building’s battery system, reinforcement learning effectively provides flexibility, managing volatile energy demand, dynamic pricing, and photovoltaic output to maximize rewards. However, the effectiveness of reinforcement learning is often hindered by limited access to training data due to privacy concerns, unstable training processes, and challenges in generalizing to different household conditions. In this study, we propose a novel federated framework for reinforcement learning in energy management systems. By enabling local model training on private data and aggregating only model parameters on a global server, this approach not only preserves privacy but also improves model generalization and robustness under varying household conditions, while decreasing electricity costs and emissions per building. For a comprehensive benchmark, we compare standard reinforcement learning with our federated approach and include mixed integer programming and rule-based systems. Among the reinforcement learning methods, deep deterministic policy gradient performed best on the Ausgrid dataset, with federated learning reducing costs by 5.01<!--> <!-->% and emissions by 4.60<!--> <!-->%. Federated learning also improved zero-shot performance for unseen buildings, reducing costs by 5.11<!--> <!-->% and emissions by 5.55<!--> <!-->%. Thus, our findings highlight the potential of federated reinforcement learning to enhance energy management systems by balancing privacy, sustainability, and efficiency.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100521"},"PeriodicalIF":9.6,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combustion parameter prediction for mining conveyor belts by using convolutional neural network–long short-term memory","authors":"Wei-le Chen ,&nbsp;Jun Deng ,&nbsp;Ze-qun Wang ,&nbsp;Tong-shuang Liu ,&nbsp;Yong-jun He ,&nbsp;Yang Xiao ,&nbsp;Cai-ping Wang ,&nbsp;Guang-xing Bai","doi":"10.1016/j.egyai.2025.100524","DOIUrl":"10.1016/j.egyai.2025.100524","url":null,"abstract":"<div><div>The combustion characteristic parameters of mining conveyor belts represent a crucial index for measuring the fire performance and hazard posed by combustible materials. An accurate prediction of its value provides important guidance on preventing conveyor belt fires. The critical parameters of a flame–retardant polyvinyl chloride gum elastic conveyor belt were measured under different radiative heat fluxes, including mass loss rate, heat release rate, effective heat of combustion and gas production rates for CO and CO₂. The prediction method for the combustion characteristics of conveyor belts was proposed by combining a convolutional neural network with long short-term memory. Results indicated that the peak values of the mass loss, heat release, smoke production and gas production rates of CO and CO₂ were positively correlated with radiative heat flux, whilst the time required to reach the peak value was negatively correlated with it. The peak time of the effective heat of combustion occurred earlier. Through deep learning modelling, mean absolute error, root mean square error and coefficient of determination were determined as 2.09, 3.45 and 9.93 × 10⁻¹, respectively. Compared with convolutional neural network, long short-term memory and multilayer perceptron, mean absolute error decreased by 26.92%, 24.82% and 25.09%, root mean square error declined by 27.82%, 29.59% and 29.59% and coefficient of determination increased by 0.05 × 10⁻¹, 0.06 × 10⁻¹ and 0.06 × 10⁻¹, respectively. The findings provide a quantitative reference benchmark for the development of conveyor belt fires and offer new technical support for the construction of early warning systems for conveyor belt fires in coal mines.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100524"},"PeriodicalIF":9.6,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A holistic multimodal approach for real-time anomaly detection and classification in large-scale photovoltaic plants","authors":"Zoubir Barraz ,&nbsp;Imane Sebari ,&nbsp;Hicham Oufettoul ,&nbsp;Kenza Ait el kadi ,&nbsp;Nassim Lamrini ,&nbsp;Ibtihal Ait Abdelmoula","doi":"10.1016/j.egyai.2025.100525","DOIUrl":"10.1016/j.egyai.2025.100525","url":null,"abstract":"<div><div>This paper presents a holistic multimodal approach for real-time anomaly detection and classification in large-scale photovoltaic plants. The approach encompasses segmentation, geolocation, and classification of individual photovoltaic modules. A fine-tuned Yolov7 model was trained for the individual module’s segmentation of both modalities; RGB and IR images. The localization of individual solar panels relies on photogrammetric measurements to facilitate maintenance operations. The localization process also links extracted images of the same panel using their geographical coordinates and preprocesses them for the multimodal model input. The study also focuses on optimizing pre-trained models using Bayesian search to improve and fine-tune them with our dataset. The dataset was collected from different systems and technologies within our research platform. It has been curated into 1841 images and classified into five anomaly classes. Grad-CAM, an explainable AI tool, is utilized to compare the use of multimodality to a single modality. Finally, for real-time optimization, the ONNX format was used to optimize the model further for deployment in real-time. The improved ConvNext-Tiny model performed well in both modalities, with 99 % precision, recall, and F1-score for binary classification and 85 % for multi-class classification. In terms of latency, the segmentation models have an inference time of 14 ms and 12 ms for RGB and IR images and 24 ms for detection and classification. The proposed holistic approach includes a built-in feedback loop to ensure the model’s robustness against domain shifts in the production environment.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100525"},"PeriodicalIF":9.6,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative of control strategies on electrical vehicle fleet charging management strategies under uncertainties","authors":"Zhewei Zhang ,&nbsp;Rémy Rigo-Mariani ,&nbsp;Nouredine Hadjsaid","doi":"10.1016/j.egyai.2025.100522","DOIUrl":"10.1016/j.egyai.2025.100522","url":null,"abstract":"<div><div>The growing penetration of Electric Vehicles (EVs) in transportation brings challenges to power distribution systems due to uncertain usage patterns and increased peak loads. Effective EV fleet charging management strategies are needed to minimize network impacts, such as peak charging power. While existing studies have addressed uncertainties in future arrivals, they often overlook the uncertainties in user-provided inputs of current ongoing charging EVs, such as estimated departure time and energy demand. This paper analyzes the impact of these uncertainties and evaluates three management strategies: a baseline Model Predictive Control (MPC), a data-hybrid MPC, and a fully data-driven Deep Reinforcement Learning (DRL) approach. For data-hybrid MPC, we adopted a diffusion model to handle user input uncertainties and a Gaussian Mixture Model for modeling arrival/departure scenarios. Additionally, the DRL method is based on a Partially Observable Markov Decision Process (POMDP) to manage uncertainty and employs a Convolutional Neural Network (CNN) for feature extraction. Robustness tests under different user uncertainty levels show that the data hybrid MPC performs better on the baseline MPC by 20 %, while the DRL-based method achieves around 10 % improvement.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100522"},"PeriodicalIF":9.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rescheduling costs and adaptive asymmetric errors guided closed-loop prediction of power loads in mine integrated energy systems","authors":"Qi Miao ,&nbsp;Xiaoyan Sun ,&nbsp;Chen Ma ,&nbsp;Yong Zhang ,&nbsp;Dunwei Gong","doi":"10.1016/j.egyai.2025.100516","DOIUrl":"10.1016/j.egyai.2025.100516","url":null,"abstract":"<div><div>The development of an integrated energy system for mining that efficiently recycles multiple resources is a crucial strategy for achieving dual carbon reduction targets in the mining sector. Precise load forecasting is fundamental to ensuring the safe and efficient scheduling of this system. However, existing studies often overlook the coupling between load forecasting and scheduling results, treating them independently, which frequently leads to high rescheduling costs due to forecasting errors. To address this issue, we propose a closed-loop load forecasting algorithm that incorporates rescheduling costs and asymmetric errors. We first proposed a data generation and model construction strategy by using real load, predicted load, and rescheduling costs to capture the relationship between load forecasting and rescheduling costs. Considering the different impacts of under-forecasting and over-forecasting on scheduling results, the rescheduling cost model is further integrated with asymmetric prediction errors to define the loss function of the Bi-LSTM based forecasting model. Additionally, an optimization strategy for self-tuning asymmetric prediction error fusion coefficients is designed to ensure the accuracy of load forecasting. The proposed algorithm is applied to the power load forecasting of an integrated energy system in a coal mine in Shanxi. The results demonstrate the effectiveness of the algorithm in reducing system rescheduling costs while ensuring forecasting accuracy, highlighting its potential application in power load forecasting for mine integrated energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100516"},"PeriodicalIF":9.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of machine learning techniques for building electrical energy consumption prediction","authors":"Yuyao Chen ,&nbsp;Wei Gong ,&nbsp;Christian Obrecht ,&nbsp;Frédéric Kuznik","doi":"10.1016/j.egyai.2025.100518","DOIUrl":"10.1016/j.egyai.2025.100518","url":null,"abstract":"<div><div>The ongoing energy transition, essential for mitigating global warming, stands to benefit significantly from advances in building energy consumption prediction. With the rise of big data, data-driven models have become increasingly effective in forecasting, with machine learning emerging as the most efficient method for constructing these predictive models. While previous reviews have typically listed various machine learning models for energy consumption prediction, they have often lacked a theoretical perspective explaining why certain models are suitable for different aspects of this domain. In contrast, this review introduces machine learning techniques based on their application phases, covering preprocessing techniques such as feature selection, extraction, and clustering, as well as state-of-the-art predictive models. We provide a comparative theoretical analysis of various models, examining their strengths, weaknesses, and suitability for different forecasting tasks. Additionally, we discuss spatial–temporal considerations in energy consumption forecasting, including the role of Graph Neural Networks and multitask learning. Furthermore, we address a significant challenge in the field, the difficulty of accurately predicting high-fluctuation electricity consumption, and propose potential solutions to tackle this issue.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100518"},"PeriodicalIF":9.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing clean energy labs: Robotic imitation learning for efficient fabrication AI-powered electrical units assembly platform","authors":"Xi Xu ,&nbsp;Yijun Gu ,&nbsp;Tianyi Zhang ,&nbsp;Jiwen Yu ,&nbsp;Stephen Skinner","doi":"10.1016/j.egyai.2025.100517","DOIUrl":"10.1016/j.egyai.2025.100517","url":null,"abstract":"<div><div>The energy industry, now in an era of digitization driven by computational design, is gradually moving towards automating the entire process from computational prediction to device assembly, aiming to minimize the reliance on time-consuming, manual trial-and-error validation. In this study, guided by computational density functional theory (DFT) predictions, a humanoid robotic arm, based on artificial intelligence (AI), was creatively utilized to assemble clean energy devices, solid oxide fuel cells (SOFCs). The material <figure><img></figure> (LBSF) was DFT-predicted to have high oxygen reduction reactions (ORRs) ability, suitable for the cathode in SOFCs compared to the conventional <figure><img></figure> (LSF). The material was made into ink then passed to the assembly platform with AI-driven robotics. AI-driven robotics was employed with an imitation learning method to effectively learn skills directly from human demonstrations, thereby alleviating researchers from labor-intensive tasks. We demonstrate our approach for autonomous SOFCs fabrication. For easy platform usage in the future, Large Language Models (LLMs) were incorporated to understand human commands. Visual information was captured by an RGBD camera to identify and locate the cathode painting spot. An imitation learning framework was then applied to learn the painting path from human operations and can be generalized to different conditions. The auto-fabricated single cells with the DFT-predicted LBSF cathode were tested and achieved a power density of 966 <span><math><mrow><mi>m</mi><mi>W</mi><mo>/</mo><mi>c</mi><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> at 700 <span><math><mrow><mo>°</mo><mi>C</mi></mrow></math></span>, more than double the performance of LSF. By integrating computational design with an AI-driven assembly platform, this study marks an initial step towards an AI-driven material lab, exponentially accelerating material design in the near future. The platform can also help disabled researchers achieve their ideas through the behavior cloning approach.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100517"},"PeriodicalIF":9.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}