Energy and AI最新文献_第4页

Thermal conductivity prediction of BN composites based on Enhanced Co-ANN combined with physical attention mechanisms 基于增强Co-ANN结合物理注意机制的BN复合材料导热系数预测

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 Epub Date: 2025-12-10 DOI: 10.1016/j.egyai.2025.100663

Chen Liu , Tong Li , Yuandong Guo , Guiping Lin

This study proposes an enhanced co-training artificial neural network (Enhanced Co-ANN) guided by the physical attention mechanisms for the effective thermal conductivity prediction of high filler volume fraction polymer/BN composites. The thermal conductivity of BN composites is influenced by multiple factors, including the morphology of the fillers, interface thermal resistance, and experimental noise. This model tackles complex physical processes by integrating a customized multi-head physical attention layer to emphasize key features, along with a physics-constrained loss function to ensure prediction consistency. A collaborative training strategy based on curriculum learning and consistency discrimination is adopted. The model is optimized using 3174 labeled experimental datasets and 50,000 unlabeled data generated from physical models. Weight distribution is systematically designed across three core levels: model architecture, loss function, and training strategy. This approach differs from traditional parameter weight adjustments, as it emphasizes key features, especially volume fraction (vf), and balances different learning objectives through a physically guided mechanism and dynamic training strategies. Attention visualization indicates that the model adaptively focuses on the volume fraction of the packing material and the interface effect, verifying the effectiveness of the physically guided design. Six groups of samples with different packing volume fractions were made for testing and validation. This model has high accuracy (R² = 0.982; MAE = 0.045 W/m K) and is extremely consistent with physical laws. This network framework provides a method with broad application prospects for the rapid calculation, screening, and efficient design of high-performance polymer/BN thermal conductive materials.

本研究提出了一种以物理注意机制为指导的增强型协同训练人工神经网络（enhanced Co-ANN），用于高填料体积分数聚合物/BN复合材料的有效导热系数预测。BN复合材料的导热性能受填料形态、界面热阻和实验噪声等多种因素的影响。该模型通过集成定制的多头物理注意层来强调关键特征，以及物理约束损失函数来确保预测一致性，从而解决复杂的物理过程。采用基于课程学习和一致性判别的协同训练策略。该模型使用3174个标记实验数据集和5万个物理模型生成的未标记数据集进行优化。权重分布系统地设计在三个核心层面：模型架构，损失函数和训练策略。这种方法不同于传统的参数权重调整，因为它强调关键特征，特别是体积分数（vf），并通过物理引导机制和动态训练策略平衡不同的学习目标。注意可视化表明，该模型自适应地关注包装材料的体积分数和界面效应，验证了物理引导设计的有效性。制作了6组不同包装体积分数的样品进行检测和验证。该模型精度高（R²= 0.982;MAE = 0.045 W/m K），与物理规律极为吻合。该网络框架为高性能聚合物/BN导热材料的快速计算、筛选和高效设计提供了一种具有广阔应用前景的方法。

{"title":"Thermal conductivity prediction of BN composites based on Enhanced Co-ANN combined with physical attention mechanisms","authors":"Chen Liu , Tong Li , Yuandong Guo , Guiping Lin","doi":"10.1016/j.egyai.2025.100663","DOIUrl":"10.1016/j.egyai.2025.100663","url":null,"abstract":"<div><div>This study proposes an enhanced co-training artificial neural network (Enhanced Co-ANN) guided by the physical attention mechanisms for the effective thermal conductivity prediction of high filler volume fraction polymer/BN composites. The thermal conductivity of BN composites is influenced by multiple factors, including the morphology of the fillers, interface thermal resistance, and experimental noise. This model tackles complex physical processes by integrating a customized multi-head physical attention layer to emphasize key features, along with a physics-constrained loss function to ensure prediction consistency. A collaborative training strategy based on curriculum learning and consistency discrimination is adopted. The model is optimized using 3174 labeled experimental datasets and 50,000 unlabeled data generated from physical models. Weight distribution is systematically designed across three core levels: model architecture, loss function, and training strategy. This approach differs from traditional parameter weight adjustments, as it emphasizes key features, especially volume fraction (vf), and balances different learning objectives through a physically guided mechanism and dynamic training strategies. Attention visualization indicates that the model adaptively focuses on the volume fraction of the packing material and the interface effect, verifying the effectiveness of the physically guided design. Six groups of samples with different packing volume fractions were made for testing and validation. This model has high accuracy (R² = 0.982; MAE = 0.045 W/m K) and is extremely consistent with physical laws. This network framework provides a method with broad application prospects for the rapid calculation, screening, and efficient design of high-performance polymer/BN thermal conductive materials.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100663"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718921","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

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

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications.

可再生能源和分布式发电的份额不断增加，需要开发深度学习方法来解决传统电网方法固有的灵活性不足的问题。在这种情况下，图神经网络是一个很有前途的解决方案，因为它们能够从图结构数据中学习。与强化学习相结合，它们可以作为确定补救行动的控制方法。本文分析了图强化学习如何改善电网应用中的表示学习和决策，特别是输配电电网。我们从图结构、图神经网络架构和强化学习方法等方面分析了所回顾的方法。尽管图强化学习已经证明了对不可预测事件和噪声数据的适应性，但它目前的阶段主要是概念验证，还没有部署到实际应用中。我们强调了现实世界应用程序的开放挑战和限制。

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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.

随着太阳能系统容量的增加，太阳能预测对于减轻太阳能变化和支持其运行至关重要且具有成本效益。时间融合变压器（TFT）在利用多个一维时间序列数据预测太阳辐照度和输出功率方面显示出巨大的潜力。由于时空信息对太阳预报更有利，本研究采用了一种简单而有效的方法，将基于卫星和数值天气预报（NWP）的二维时空输入与TFT相结合，以实现更熟练的辐照度预报。结果表明，与单位置数据相比，采用简单空间平均的时空输入通常可以更好地预测4小时前的辐照度，其技能得分高达12.24%。在多云的天气条件下，使用时空信息的好处更为明显，而在天空晴朗或较少多云的情况下，它可能会导致一些错误的陈述。可解释性分析表明，NWP辐照度产品对TFT日间太阳预报结果的影响较大，可达22.07%。虽然NWP产品在与卫星数据相结合时有利于日间太阳预报，但在不同的天空条件和预报视界下，它们的影响可能不同。在能源系统可靠性的实际应用中，应该对这些影响进行适当的分析和解释。利用TFT改进辐照度预报和可解释性分析的工作对太阳能系统的运行至关重要。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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 (

R^{2}

= 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.

可再生能源发电的准确预测对电网的稳定性和成本效率至关重要。由于数据采集成本高、缺乏透明度和用户控制有限，基于人工智能的预测中的特征选择仍然具有挑战性。我们为可再生能源预测引入了一个透明且成本敏感的特征选择框架，该框架利用可解释人工智能（XAI）。我们整合了SHapley加性解释（SHAP）和“像我5一样解释”（ELI5）来识别主要特征和冗余特征。这种方法可以在不影响模型性能的情况下实现系统的数据集缩减。我们的案例研究基于光伏发电数据，在四个实验设置中评估了该方法。实验结果表明，基于xai的特征选择将优势度指数从0.37降低到0.17，保持了较高的预测精度（R2 = 0.94, drop < 0.04），降低了数据采集成本。此外，消除主要特征可以提高对噪声的鲁棒性，并将性能差异减少到基线情景的三倍。所开发的框架增强了可解释性，支持人在环决策，并为特征选择引入了成本敏感的目标函数。通过结合透明度、稳健性和效率，我们为可信赖的人工智能（TAI）在能源预测中的应用的开发和实施做出了贡献，为工业部署提供了可扩展的解决方案。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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%.

连接到配电系统的分布式能源越来越多地由智能/学习代理操作。这些代理人希望优化自己的收益，可能会发现利用系统的有害方法。因此，屏蔽有害物质行为的关键系统至关重要。本文考虑了配电系统资源不具备这些信息，而是通过经验学习改进其政策的现实情况，研究了配电系统有效运行机制的设计问题。为此，开发了一种多智能体强化学习算法来模拟参与者的学习行为过程，并考虑智能体在不同定价方案下的学习情况，这些定价方案塑造了智能体的奖励函数。提出了两种流行的定价方案（按出价付费和配送地点边际定价），表明学习代理可以发现利用它们的方法，从而导致严重的调度效率低下。提出了一种博弈论定价方案，从理论上激励诚实的智能体行为，并通过经验证明，在有学习智能体存在的情况下，这一特性也提高了最终调度的效率。特别是，就效率而言，所提出的方案能够比流行的分配位置边际定价方案高出15-17%。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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 presolving 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

M o G E

(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

M o G E

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.

最优潮流（OPF）问题的凸松弛和逼近在电网规划和运行中获得了重要的研究和工业兴趣。减少求解时间的一种方法是求解，它消除了问题定义中的约束，从而减少了底层优化算法的负担。为此，我们提出了一个求解凸最优潮流（C-OPF）问题的框架，该框架使用了一种新的基于深度学习的架构，称为MoGE（混合梯度专家）。在这个框架中，通过学习C-OPF参数和最优对偶变量（后者可以表示为梯度）之间的映射来减少问题的规模，然后使用该对偶变量来筛选最优时非绑定的约束。从理论上证明了该求解框架在任意C-OPF问题族中的有效性。我们描述了MoGE的泛化特征，并开发了一个解后恢复程序来减轻可能的约束分类错误。使用两种不同的C-OPF模型，我们通过模拟表明，我们的框架在多个PGLIB和MATPOWER测试用例中减少了高达34%的解决时间，同时提供了与完整问题相同的解决方案。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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.

大多数配电系统操作员可能无法准确记录或完整地维护众多低压客户的相位连接。不同的消费者相识别（CPI）方法基于电压，功率或其他测量在文献中提出。由于收集电压测量的技术挑战，基于功率测量的方法是优选的。因此，本文提出了一种应用图神经网络（GNNs）的基于功率的CPI方法。CPI方法生成综合变压器功率曲线，假设所测负载曲线的相位随机组合，这些曲线一起用于训练GNN模型。然后使用测量的变压器和负载功率曲线对GNN模型进行测试。在光伏渗透、光伏维护、测量误差、未计量消耗、电网资产参数不确定和相连接不准确等多种条件下，对55个负载的低压电网进行了性能评估。在111负荷的低压电网上的进一步测试证明了该方法的可扩展性。结果表明，在大多数情况下，GNN模型的准确率可以达到90%以上，优于各种最新的方法。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

This paper introduces a novel hybrid method combining H-

\infty

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-

\infty

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 (

Δ V_{i} / V_{t}

) 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.

介绍了一种结合H-∞滤波和自适应线性神经元（ADALINE）网络的配电系统闪变分量估计新方法。该方法利用H-∞滤波器的鲁棒性提取不确定和噪声条件下的电压包络，然后使用ADALINE准确识别嵌入在包络中的iec定义的标准频率下闪烁分量的相对幅度（ΔVi/Vt）。这种协同作用使有效的时域估计具有快速收敛和噪声弹性，解决了现有频域方法的主要局限性。与传统技术不同，这种混合模型处理复杂的功率干扰，而不需要事先了解噪声特性或广泛的训练。为了验证该方法的性能，我们基于IEC标准61000-4-15进行了仿真研究，并通过统计分析、蒙特卡罗模拟和实际数据进行了支持。结果表明，与基于快速傅立叶变换（FFT）和基于离散小波变换（DWT）的估计器相比，具有更高的精度、鲁棒性和更少的计算负荷。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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.

场景生成是能源系统随机规划应用的关键步骤，其中不确定性的准确表示直接影响决策质量。归一化流（NFs）是一种可逆的深度生成模型，通过最大化似然提供了学习复杂分布的灵活性，但在再现现实世界数据的关键统计属性时，往往受到准确性的限制。在这项工作中，我们提出了一个矩通知的归一化流（MI-NF）框架，其中矩约束被纳入到NF训练过程中，以提高基于场景的概率预测的准确性。此外，采用高斯过程自适应确定矩正则化权值。对2014年全球能源预测大赛开放获取数据集的案例研究表明，MI-NF模型生成的场景在测试集上的平均绝对误差降低了40%以上。当应用于当地电力-氢市场的随机规划框架时，改进的情景准确性导致不确定性下更具成本效益和稳健的运营决策。

{"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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

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 https://github.com/hfarhaditolie/Ultrasonic-SoC.

准确的荷电状态估算对于锂离子电池的安全高效运行至关重要。库仑计数和开路电压测量等方法面临着漂移和精度方面的挑战，特别是在电动汽车和电网存储使用中具有空间梯度的大尺寸电池中。本研究探讨了超声传感作为一种非侵入性和实时的SoC评估技术。它探索了使用机器学习模型来识别基于信号质量的最佳执行器-接收器路径的传感器放置机会，并确定了SoC估计可以达到的最大精度。基于实验采集的超声波信号在安装在大尺寸袋状细胞上的四个传感器之间传输，开发了一种新颖的定制深度学习框架，通过卷积神经网络增强，通过转换到波形图像来处理超声波信号，并利用强预训练模型的迁移学习。结果表明，与传统的数据分析相比，将双向信号传输与基于动态深度学习的致动器和接收器选择策略相结合，显著提高了超声波传感的有效性，并为在大尺寸电池中实现鲁棒性和可扩展性的SoC监测铺平了道路。此外，通过检查超声信号相对于SoC的可微分性，探索了自我监督的初步途径，为减少对传统地面事实的依赖和增强基于超声SoC估计的可扩展性提供了一条有希望的途径。数据和源代码将在https://github.com/hfarhaditolie/Ultrasonic-SoC上提供。

{"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