Energy and AI最新文献_第6页

Graph reinforcement learning for power grids: A comprehensive survey 电网图强化学习：综合综述

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-02 DOI: 10.1016/j.egyai.2025.100671

Mohamed Hassouna , Clara Holzhüter , Pawel Lytaev , Josephine Thomas , Bernhard Sick , Christoph Scholz

引用次数: 0

Interpretable transformer based intra-day solar forecasting with spatiotemporal satellite and numerical weather prediction inputs 基于时空卫星和数值天气预报输入的可解释变压器日内太阳活动预报

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.egyai.2025.100667

Shanlin Chen , Tao Jing , Mengying Li , Hiu Hung Lee , Ming Chun Lam , Siqi Bu

{"title":"Interpretable transformer based intra-day solar forecasting with spatiotemporal satellite and numerical weather prediction inputs","authors":"Shanlin Chen , Tao Jing , Mengying Li , Hiu Hung Lee , Ming Chun Lam , Siqi Bu","doi":"10.1016/j.egyai.2025.100667","DOIUrl":"10.1016/j.egyai.2025.100667","url":null,"abstract":"<div><div>With the increasing capacity addition of solar energy systems, solar forecasting is vital and cost-effective to mitigate solar variability and to support their operation. The temporal fusion transformer (TFT) has shown great potential in both solar irradiance and power output forecasting using multiple one-dimensional time series data. Since spatiotemporal information is more beneficial for solar forecasting, this work applies a simple yet effective way to incorporate two-dimensional spatiotemporal satellite- and numerical weather prediction (NWP)-based inputs with TFT for more skillful irradiance forecasts. Results show that spatiotemporal inputs with simple spatial averaging can generally lead to better irradiance forecasts with 4-h ahead skill scores up to 12.24%, compared to the use of single-location data. The benefit of using spatiotemporal information is more pronounced for forecasts under cloudy conditions, whereas it might result in some misrepresentations when the sky is clear or less cloudy. NWP data can generally be used to improve the intra-day solar forecasting performance with TFT, and the interpretability analysis shows that NWP irradiance products have a larger impact (up to 22.07%) on the overall results. Although NWP products are beneficial for intra-day solar forecasting when integrated with satellite-based data, their influences under different sky conditions and forecast horizons might be different. A proper analysis of these impacts should be performed and interpreted in practical applications for the reliability of energy systems. This work on improved irradiance forecasts with TFT and interpretability analysis is crucial for the operation of solar energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100667"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791893","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}

引用次数: 0

An explainable artificial intelligence feature selection framework for transparent, trustworthy, and cost-efficient energy forecasting 一个可解释的人工智能特征选择框架，用于透明、可信和成本效益的能源预测

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-11-25 DOI: 10.1016/j.egyai.2025.100648

Leonard Kost, Sarah K. Lier, Michael H. Breitner

{"title":"An explainable artificial intelligence feature selection framework for transparent, trustworthy, and cost-efficient energy forecasting","authors":"Leonard Kost, Sarah K. Lier, Michael H. Breitner","doi":"10.1016/j.egyai.2025.100648","DOIUrl":"10.1016/j.egyai.2025.100648","url":null,"abstract":"<div><div>Accurate forecasting of renewable power generation is crucial for grid stability and cost efficiency. Feature selection in AI-based forecasting remains challenging due to high data acquisition cost, lack of transparency, and limited user control. We introduce a transparent and cost-sensitive feature selection framework for renewable power forecasting that leverages Explainable Artificial Intelligence (XAI). We integrate SHapley Additive exPlanations (SHAP) and Explain Like I’m 5 (ELI5) to identify dominant and redundant features. This approach enables systematic dataset reduction without compromising model performance. Our case study, based on Photovoltaic (PV) generation data, evaluates the approach across four experimental setups. Experimental results indicate that our XAI-based feature selection reduces the dominance index from 0.37 to 0.17, maintains high predictive accuracy (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> = 0.94, drop < 0.04), and lowers data acquisition costs. Furthermore, eliminating dominant features improves robustness to noise and reduces performance variance by a factor of three compared to the baseline scenario. The developed framework enhances interpretability, supports human-in-the-loop decision-making, and introduces a cost-sensitive objective function for feature selection. By combining transparency, robustness, and efficiency, we contribute to the development and implementation of Trustworthy AI (TAI) applications in energy forecasting, providing a scalable solution for industrial deployment.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100648"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614665","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}

引用次数: 0

Taming deep reinforcement learning agents with pricing mechanism: Validation in power distribution systems 用定价机制驯服深度强化学习代理：在配电系统中的验证

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.egyai.2025.100635

Haoyang Zhang , Georgios Tsaousoglou , Sen Zhan , Koen Kok , Nikolaos G. Paterakis

{"title":"Taming deep reinforcement learning agents with pricing mechanism: Validation in power distribution systems","authors":"Haoyang Zhang , Georgios Tsaousoglou , Sen Zhan , Koen Kok , Nikolaos G. Paterakis","doi":"10.1016/j.egyai.2025.100635","DOIUrl":"10.1016/j.egyai.2025.100635","url":null,"abstract":"<div><div>Distributed energy resources, connected to power distribution systems, are increasingly operated by intelligent/learning agents. Such agents, looking to optimize their own payoff, can discover harmful ways to exploit the system. Hence, shielding critical systems of harmful agent behavior is of crucial importance. In this paper, the problem of designing an efficient operating mechanism for a power distribution system is taken on, considering the realistic case where the system’s resources do not possess this information and instead learn to improve their policies through experience. To that end, a multi-agent reinforcement learning algorithm is developed to model the participants’ learning-to-act process and consider the agents’ learning under different pricing schemes that shape the agents’ reward functions. Two popular pricing schemes (pay-as-bid and distribution locational marginal pricing) are presented, exposing that learning agents can discover ways to exploit them, resulting in severe dispatch inefficiency. A game-theoretic pricing scheme is presented that theoretically incentivizes truthful agent behavior, and empirically demonstrate that this property improves the efficiency of the resulting dispatch also in the presence of learning agents. In particular, the proposed scheme is able to outperform the popular distribution locational marginal pricing scheme, in terms of efficiency, by a factor of 15–17%.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100635"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416161","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}

引用次数: 0

Presolving convexified optimal power flow with mixtures of gradient experts 梯度专家混合求解凸型最优潮流

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.egyai.2025.100609

Shourya Bose, Kejun Chen, Yu Zhang

{"title":"Presolving convexified optimal power flow with mixtures of gradient experts","authors":"Shourya Bose, Kejun Chen, Yu Zhang","doi":"10.1016/j.egyai.2025.100609","DOIUrl":"10.1016/j.egyai.2025.100609","url":null,"abstract":"<div><div>Convex relaxations and approximations of the optimal power flow (OPF) problem have gained significant research and industrial interest for planning and operations in electric power networks. One approach for reducing their solve times is <em>presolving</em> which eliminates constraints from the problem definition, thereby reducing the burden of the underlying optimization algorithm. To this end, we propose a presolving framework for convexified optimal power flow (C-OPF) problems, which uses a novel deep learning-based architecture called <span><math><mstyle><mi>M</mi><mi>o</mi><mi>G</mi><mi>E</mi></mstyle></math></span> (Mixture of Gradient Experts). In this framework, problem size is reduced by learning the mapping between C-OPF parameters and optimal dual variables (the latter being representable as gradients), which is then used to screen constraints that are non-binding at optimum. The validity of using this presolve framework across arbitrary families of C-OPF problems is theoretically demonstrated. We characterize generalization in <span><math><mstyle><mi>M</mi><mi>o</mi><mi>G</mi><mi>E</mi></mstyle></math></span> and develop a post-solve recovery procedure to mitigate possible constraint classification errors. Using two different C-OPF models, we show via simulations that our framework reduces solve times by upto 34% across multiple PGLIB and MATPOWER test cases, while providing an identical solution as the full problem.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100609"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219588","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}

引用次数: 0

Consumer phase identification in distribution grids using Graph Neural Networks based on synthetic and measured power profiles 基于综合和实测功率分布的图神经网络在配电网中的用户相位识别

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.egyai.2025.100607

Chandra Sekhar Charan Dande , Nikolaos A. Efkarpidis , Matthias Christen , Mirko Ginocchi , Antonello Monti

{"title":"Consumer phase identification in distribution grids using Graph Neural Networks based on synthetic and measured power profiles","authors":"Chandra Sekhar Charan Dande , Nikolaos A. Efkarpidis , Matthias Christen , Mirko Ginocchi , Antonello Monti","doi":"10.1016/j.egyai.2025.100607","DOIUrl":"10.1016/j.egyai.2025.100607","url":null,"abstract":"<div><div>Most distribution system operators may not accurately record or completely maintain the phase connections for the numerous LV customers. Different consumer phase identification (CPI) approaches based on voltages, powers or other measurements are proposed in the literature. Due to the technical challenges in collecting voltage measurements, power measurement based approaches are preferable. Hence, this paper proposes a novel power based CPI methodology applying Graph Neural Networks (GNNs). The CPI methodology generates synthetic transformer power profiles assuming random combinations of phases for the measured load profiles, which are used altogether to train the GNN model. The GNN model is then tested using measured transformer and load power profiles. The performance of the methodology is evaluated in a test low voltage grid of 55 loads under various conditions of Photovoltaic penetration, Photovoltaics under maintenance, measurement errors, unmetered consumption, uncertain grid asset parameters and inaccurate phase connections. Further tests on a real low voltage grid with 111 loads prove the scalability of the methodology. The attained results show that the GNN model can achieve accuracy above 90% in most cases, outperforming various state-of-the-art methods.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100607"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045867","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}

引用次数: 0

A hybrid Artificial Intelligence method for estimating flicker in power systems 电力系统闪变估计的混合人工智能方法

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.egyai.2025.100614

Javad Enayati , Pedram Asef , Alexandre Benoit

{"title":"A hybrid Artificial Intelligence method for estimating flicker in power systems","authors":"Javad Enayati , Pedram Asef , Alexandre Benoit","doi":"10.1016/j.egyai.2025.100614","DOIUrl":"10.1016/j.egyai.2025.100614","url":null,"abstract":"<div><div>This paper introduces a novel hybrid method combining H-<span><math><mi>∞</mi></math></span> filtering and an adaptive linear neuron (ADALINE) network for flicker component estimation in power distribution systems. The proposed method leverages the robustness of the H-<span><math><mi>∞</mi></math></span> filter to extract the voltage envelope under uncertain and noisy conditions, followed by the use of ADALINE to accurately identify the relative amplitudes of flicker components (<span><math><mrow><mi>Δ</mi><msub><mrow><mi>V</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>/</mo><msub><mrow><mi>V</mi></mrow><mrow><mi>t</mi></mrow></msub></mrow></math></span>) at standard IEC-defined frequencies embedded in the envelope. This synergy enables efficient time-domain estimation with rapid convergence and noise resilience, addressing key limitations of existing frequency-domain approaches. Unlike conventional techniques, this hybrid model handles complex power disturbances without prior knowledge of noise characteristics or extensive training. To validate the method’s performance, we conduct simulation studies based on IEC Standard 61000-4-15, supported by statistical analysis, Monte Carlo simulations, and real-world data. Results demonstrate superior accuracy, robustness, and reduced computational load compared to Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT)-based estimators.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100614"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045869","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}

引用次数: 0

Scenario generation via moments-informed normalizing flows for stochastic optimization of local energy markets 基于时刻信息的规范化流的情景生成，用于局部能源市场的随机优化

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-11-27 DOI: 10.1016/j.egyai.2025.100649

Xu Zhou, Vassilis M. Charitopoulos

{"title":"Scenario generation via moments-informed normalizing flows for stochastic optimization of local energy markets","authors":"Xu Zhou, Vassilis M. Charitopoulos","doi":"10.1016/j.egyai.2025.100649","DOIUrl":"10.1016/j.egyai.2025.100649","url":null,"abstract":"<div><div>Scenario generation is a critical step in stochastic programming for energy systems applications, where accurate representation of uncertainty directly impacts the decision quality. Normalizing flows (NFs), a class of invertible deep generative models, offer flexibility in learning complex distributions by maximizing the likelihood, but often suffer from limited accuracy in reproducing key statistical properties of real-world data. In this work we propose a moments-informed Normalizing Flows (MI-NF) framework, in which moment constraints are incorporated into the NF training process to improve the accuracy of scenario-based probabilistic forecasts. Furthermore, Gaussian Processes (GPs) are employed to adaptively determine the moment regularization weight. Case studies on the open-access dataset of the Global Energy Forecasting Competition 2014 demonstrate that scenarios generated by the MI-NF model achieve over 40% lower mean absolute error on the testing set. When applied within a stochastic programming framework for a local electricity–hydrogen market, the improved scenario accuracy leads to more cost-effective and robust operational decisions under uncertainty.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100649"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680887","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}

引用次数: 0

Large format battery SoC estimation: An ultrasonic sensing and deep transfer learning predictions for heterogeneity 大尺寸电池SoC估计：超声传感和深度迁移学习预测异质性

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-11-29 DOI: 10.1016/j.egyai.2025.100662

Hamidreza Farhadi Tolie , Benjamin Reichmann , James Marco , Zahra Sharif Khodaei , Mona Faraji Niri

{"title":"Large format battery SoC estimation: An ultrasonic sensing and deep transfer learning predictions for heterogeneity","authors":"Hamidreza Farhadi Tolie , Benjamin Reichmann , James Marco , Zahra Sharif Khodaei , Mona Faraji Niri","doi":"10.1016/j.egyai.2025.100662","DOIUrl":"10.1016/j.egyai.2025.100662","url":null,"abstract":"<div><div>Accurate state of charge (SoC) estimation is vital for safe and efficient operation of lithium-ion batteries. Methods such as Coulomb counting and open-circuit voltage measurements face challenges related to drift and accuracy, especially in large-format cells with spatial gradients in electric vehicles and grid storage usage. This study investigates ultrasonic sensing as a non-invasive and real-time technique for SoC estimation. It explores the opportunity of sensor placement using machine learning models to identify optimal actuator–receiver paths based on signal quality and pinpoints the maximum accuracy that can be achieved for SoC estimation. Based on experimentally collected ultrasound signals transmitted between four sensors installed on a large format pouch cell, a novel and customised deep learning framework enhanced by convolutional neural networks is developed to process ultrasonic signals through transformation to waveform images and leverage transfer learning from strong pre-trained models. The results demonstrate that combining bidirectional signal transmission with a dynamic deep learning-based strategy for actuator and receiver selection significantly enhances the effectiveness of ultrasonic sensing compared to traditional data analysis and pave the way for a robust and scalable SoC monitoring in large-format battery cells. Furthermore, preliminary pathways towards self-supervision are explored by examining the differentiability of ultrasonic signals with respect to SoC, offering a promising route to reduce reliance on conventional ground truths and enhance the scalability of ultrasound-based SoC estimation. The data and source code will be made available at <span><span>https://github.com/hfarhaditolie/Ultrasonic-SoC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100662"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680997","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}

引用次数: 0

Investigation of fuel cell stack performance degradation based on 1000 h durability experiments and long short-term memory prediction frameworks under dynamic load conditions 动态载荷条件下基于1000 h耐久性实验和长短期记忆预测框架的燃料电池堆性能退化研究

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-30 DOI: 10.1016/j.egyai.2025.100628

Zirong Yang , Yang Ge , Xuefeng Ji , Xiaolu Li , Daokuan Jiao , Yongping Hou , Yanyi Zhang , Dong Hao

{"title":"Investigation of fuel cell stack performance degradation based on 1000 h durability experiments and long short-term memory prediction frameworks under dynamic load conditions","authors":"Zirong Yang , Yang Ge , Xuefeng Ji , Xiaolu Li , Daokuan Jiao , Yongping Hou , Yanyi Zhang , Dong Hao","doi":"10.1016/j.egyai.2025.100628","DOIUrl":"10.1016/j.egyai.2025.100628","url":null,"abstract":"<div><div>Investigating the proton exchange membrane fuel cell (PEMFC) stack performance degradation phenomena is of vital importance for product development. In the study, the 1000 h durability experiment of a 5-kW fuel cell stack was performed under dynamic cyclic test conditions, and the test data containing 16 key parameters was utilized to develop the performance prediction framework based on long short-term memory (LSTM) model and LSTM model incorporating attention mechanism (Attention-LSTM). Data preprocessing and postprocessing for eight current modes as well as incremental learning approach were also presented. Experimental results show that the voltage degradation ratio is about 2.0 % at the total dynamic cyclic duration of 500 h and approximately 4.8 % at 1000 h. The degradation ratio at higher stack operating currents is found larger than that of lower operating currents. The calculated voltage degradation speeds among all current modes fall within the range of 25∼60 μV h<sup>-1</sup>. When it comes to model prediction performances, both LSTM and Attention-LSTM models could effectively capture the voltage variations under current rising and dropping conditions. The LSTM model exhibits superior transient prediction capabilities near current change moments while the Attention-LSTM model demonstrates smaller prediction deviations at relatively stable conditions. When the advanced forecast time reaches or exceeds 200 h, the Attention-LSTM model predictions agree better with the bench test data, and it maintains consistent prediction accuracy across different current modes. The study contributes to fuel cell stack durability performance analysis and degradation prediction.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100628"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320593","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}

引用次数: 0