Energy and AI最新文献_第9页

Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM 通过将天气预报整合到ARLEM中，增强极端条件下的能源控制稳定性

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-09 DOI: 10.1016/j.egyai.2025.100617

Vahid M. Nik

Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Energy Management (ARLEM). Since ARLEM is an online model-free reward-based RL method, it does not need any form of (predictive) modelling and weather forecasts serve as an additional source of information about the agent’s environment. The impacts of incorporating weather forecasts into ARLEM are investigated for a typical urban neighborhood in Stockholm during a winter with two cold waves and considering 17 future climate scenarios for the period of 2040–2069. Multiple adaptive policy schemes are assessed considering four forecast horizons of 3, 6, 12, and 24 h. Results show that integrating weather forecasts into decision-making can significantly reduce fluctuations in the control system, leading to more stable energy management. Moreover, it enhances energy efficiency by reducing both average and peak demands, particularly during extreme weather events. Overall, this contributes to improving the climate resilience of energy systems.

提高能源管理系统的稳定性至关重要，特别是在面临极端天气事件时。防止突然振荡的一种常见方法是实施预测模型；然而，这通常需要更复杂的模型和更强大的计算能力。在这项工作中，天气预报被整合到基于自适应强化学习的能源管理（ARLEM）方法中。由于ARLEM是一种在线无模型的基于奖励的强化学习方法，它不需要任何形式的（预测）建模，天气预报可以作为智能体环境的额外信息来源。本文以斯德哥尔摩一个典型的城市社区为例，研究了在一个有两次寒潮的冬季将天气预报纳入ARLEM的影响，并考虑了2040-2069年期间的17种未来气候情景。考虑3、6、12和24 h四个预报时段，对多个自适应政策方案进行了评估。结果表明，将天气预报纳入决策可以显著减少控制系统的波动，从而实现更稳定的能源管理。此外，它通过降低平均和峰值需求来提高能源效率，特别是在极端天气事件期间。总体而言，这有助于提高能源系统的气候适应能力。

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引用次数: 0

LFTL: Lightweight feature transfer learning with channel-independent LSTM for distributed PV forecasting LFTL：基于信道独立LSTM的轻量级特征迁移学习，用于分布式PV预测

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-08 DOI: 10.1016/j.egyai.2025.100616

Yuanjing Zhuo, Huan Long, Zhi Wu, Wei Gu

Distributed photovoltaic (PV) power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data. This paper proposes a lightweight feature transfer learning (LFTL) method that enables rapid and accurate forecasting of new distributed PVs. Firstly, the raw fluctuating PV data are preprocessed through decomposition to separate low- and high-frequency components. These components are then multi-scale segmented to capture diverse temporal characteristics. Following feature compression and LSTM temporal modeling, the informative features from the source domain enable lightweight transfer. For the target domain, a channel-independent encoder is designed to prevent negative interactions between heterogeneous frequencies. The frequency-fused segment-independent decoder equipped with positional embeddings enables local temporal analysis and reduces error accumulation of multi-step forecasts. LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity. LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.

分布式光伏发电系统的功率预测由于其固有的随机波动性和有限的历史数据而面临挑战。本文提出了一种轻量级特征迁移学习（LFTL）方法，能够快速准确地预测新的分布式pv。首先，对原始波动PV数据进行分解预处理，分离出低频和高频分量；然后对这些组件进行多尺度分割，以捕获不同的时间特征。在特征压缩和LSTM时态建模之后，来自源域的信息特征支持轻量级传输。对于目标域，设计了信道无关的编码器，以防止异构频率之间的负相互作用。配备位置嵌入的频率融合段独立解码器能够进行局部时间分析，并减少多步预测的误差积累。LFTL训练采用联合训练策略，避免了域差异带来的负迁移。LFTL始终优于最先进的时间序列预测模型，同时保持相对较低的基于实际分布式PV数据的计算开销。

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引用次数: 0

An enhanced sorting framework for retired batteries based on multi-dimensional features and an integrated clustering approach 基于多维特征和集成聚类方法的改进退役电池分类框架

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100612

Zhuo Liu , Bumin Meng , Rui Pan , Juan Zhou

Retired batteries for secondary use offer significant economic benefits and environmental value. Accurate sorting of retired batteries with diverse characteristics can further enhance their application efficiency. However, in practical sorting processes, the presence of redundant features, noise interference, and distribution discrepancies in the data severely limits the accuracy of sorting outcomes. To address these challenges, this paper proposes an enhanced retired battery sorting strategy that incorporates feature selection and a clustering algorithm, aiming to optimize the sorting process from the perspective of feature data. To address feature redundancy and high dimensionality issues, this paper proposes an entropy screening method. The Local Outlier Factor algorithm is used to remove anomalous samples. Subsequently, an ensemble clustering approach is developed based on K-means, Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Model, and Spectral clustering, to handle diverse data distributions. The proposed method is validated on 100 retired batteries as well as the large-scale dataset. Additionally, its strong sorting capability and engineering applicability are further demonstrated through carefully designed aging-controlled experiments.

退役电池二次利用具有显著的经济效益和环境价值。对具有不同特性的退役电池进行准确分类，可以进一步提高其应用效率。然而，在实际的分拣过程中，数据中存在冗余特征、噪声干扰和分布差异严重限制了分拣结果的准确性。针对这些挑战，本文提出了一种结合特征选择和聚类算法的增强退役电池分拣策略，旨在从特征数据的角度优化分拣过程。针对特征冗余和高维问题，提出了一种熵筛选方法。采用局部离群因子算法去除异常样本。随后，基于K-means、基于密度的噪声应用空间聚类、高斯混合模型和光谱聚类，提出了一种集成聚类方法来处理不同的数据分布。该方法在100个退役电池和大规模数据集上进行了验证。通过精心设计的老化控制实验，进一步证明了其强大的分选能力和工程适用性。

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引用次数: 0

District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment 基于自适应情景感知预测环境的强化学习的住宅区域供热优化

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100603

Sai Sushanth Varma Kalidindi , Hadi Banaee , Hans Karlsson , Amy Loutfi

As district heating networks evolve to meet climate-neutral objectives, optimizing their control under heterogeneous building characteristics and dynamic environmental conditions remains a significant challenge. Traditional control strategies often lack the adaptability necessary to account for building-specific dynamics and to ensure real-time adherence to operational safety constraints. In this work, we present an integrated machine learning-based framework that combines an adaptive context-aware transformer model with deep reinforcement learning to address these limitations. The proposed approach introduces an adaptive context-aware transformer as a predictive environment within a Deep Q-Network (DQN) framework, enabling data-driven, building-specific control of district heating systems. Utilizing real-world data from 148 residential buildings across Sweden and Finland, the model incorporates contextual embeddings and temporal features to predict indoor temperature trajectories with high accuracy, achieving root mean square error values between 0.18–0.24 °C for Swedish buildings and 0.26–0.32 °C for Finnish buildings. The DQN agent leverages these predictions to optimize heating control while ensuring compliance with operational safety limits and preserving occupant comfort. Experimental results demonstrate significant energy savings, with mid-rise buildings achieving up to 14.85% reduction in energy consumption, and peak seasonal savings exceeding 20% during spring months. This integrated approach illustrates the potential for substantial energy optimization and reliable indoor climate management in future district heating networks.

随着区域供热网络向气候中和目标的发展，在异质建筑特征和动态环境条件下优化其控制仍然是一个重大挑战。传统的控制策略通常缺乏必要的适应性，无法考虑建筑物特定的动态，也无法确保实时遵守操作安全约束。在这项工作中，我们提出了一个基于机器学习的集成框架，该框架将自适应上下文感知转换器模型与深度强化学习相结合，以解决这些限制。提出的方法引入了一个自适应的环境感知变压器，作为Deep Q-Network （DQN）框架内的预测环境，实现数据驱动的、特定于建筑的区域供热系统控制。该模型利用来自瑞典和芬兰148座住宅建筑的真实数据，结合上下文嵌入和时间特征，以高精度预测室内温度轨迹，瑞典建筑的均方根误差值为0.18-0.24°C，芬兰建筑的均方根误差值为0.26-0.32°C。DQN代理利用这些预测来优化加热控制，同时确保符合操作安全限制并保持乘员舒适度。实验结果显示节能效果显著，中层建筑能耗降低高达14.85%，春季季节节能峰值超过20%。这种综合方法说明了未来区域供热网络中大量能源优化和可靠的室内气候管理的潜力。

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引用次数: 0

Prediction of geothermal heat flow for sustainable energy applications with sparse geological data using machine learning 利用机器学习的稀疏地质数据预测可持续能源应用中的地热热流

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100615

Qianqian Li , Shuaibin Wan , Haoming Li , Jishan He , Dongxu Ji

Geothermal heat flow (GHF) is a crucial metric in the assessment of geothermal reservoirs. To circumvent the high cost of conventional GHF measurement techniques, there is a growing interest in leveraging machine learning models to predict GHF based on geological datasets imputed by the Kriging method. However, the spatial distribution of some geological features exhibits complex data patterns and missing values, justifying the need for a more accurate and efficient alternative to the conventional Kriging method. In this study, we present a novel machine learning-based framework for predicting GHF based on sparse geological data. Specifically, a machine learning model (here MissForest) is employed to impute the missing values of geological data. The MissForest model, by leveraging spatial correlations among geological parameters (e.g., upper crust thickness, Moho depth, and rock type), achieves superior imputation accuracy (R² = 0.90 at 20% missing rate) over the conventional Kriging method (R² = 0.84). Based on the imputed datasets, machine learning regression models are trained to capture the mapping of geological features to GHF. Our best model achieves a low error of 10.18 % for predicting GHF across various regions, surpassing the previous studies. Furthermore, the machine learning-based framework successfully predicts the GHF globally, shedding new light on the distribution patterns of geothermal resources and their exploitation potential worldwide.

地热热流（GHF）是地热储层评价的重要指标。为了规避传统GHF测量技术的高成本，利用机器学习模型来预测基于Kriging方法输入的地质数据集的GHF越来越受到关注。然而，一些地质特征的空间分布表现出复杂的数据模式和缺失值，证明需要一种更准确和有效的替代传统的克里格方法。在这项研究中，我们提出了一种新的基于机器学习的框架，用于基于稀疏地质数据预测GHF。具体来说，使用机器学习模型（这里是MissForest）来估算地质数据的缺失值。MissForest模型通过利用地质参数（如上地壳厚度、莫霍深度和岩石类型）之间的空间相关性，比传统的Kriging方法（R2 = 0.84）具有更高的输入精度（R2 = 0.90，缺失率为20%）。基于输入的数据集，训练机器学习回归模型来捕获地质特征到GHF的映射。该模型预测各区域GHF的误差较低，仅为10.18%，优于以往的研究结果。此外，基于机器学习的框架成功地预测了全球GHF，为全球地热资源的分布模式及其开发潜力提供了新的思路。

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引用次数: 0

Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis 人工智能在水电解电催化剂设计中的机遇与前景

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-02 DOI: 10.1016/j.egyai.2025.100606

Qing Wang , Lizhen Wu , Qiang Zheng , Liang An

As a key pathway for green hydrogen production, water electrolysis is expected to play a central role in the future energy landscape. However, its large-scale deployment is hindered by challenges related to cost, performance, and durability. The emergence of artificial intelligence (AI) has transformed this field by offering powerful and efficient tools for the design and optimization of electrocatalysts. This review outlines an AI-driven multiscale design framework, highlighting its role at the microscopic scale for identifying atomic-level active sites and key descriptors, at the mesoscopic scale for structural and morphological characterization, and at the macroscopic scale for multi-objective optimization and intelligent control. This multiscale framework demonstrates the potential of AI to accelerate the development of next-generation electrocatalysts. In addition, the integration of generative AI and automated experimental techniques is highlighted as promising strategies to further enhance electrocatalyst discovery and promote the practical implementation of water electrolysis technologies.

作为绿色制氢的关键途径，水电解有望在未来的能源格局中发挥核心作用。然而，它的大规模部署受到成本、性能和耐用性等挑战的阻碍。人工智能（AI）的出现为电催化剂的设计和优化提供了强大而有效的工具，从而改变了这一领域。本文概述了人工智能驱动的多尺度设计框架，强调了其在微观尺度上用于识别原子水平的活性位点和关键描述符，在中观尺度上用于结构和形态表征，以及在宏观尺度上用于多目标优化和智能控制的作用。这种多尺度框架展示了人工智能加速下一代电催化剂开发的潜力。此外，生成式人工智能和自动化实验技术的集成被强调为进一步加强电催化剂的发现和促进水电解技术的实际实施的有前途的策略。

{"title":"Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis","authors":"Qing Wang , Lizhen Wu , Qiang Zheng , Liang An","doi":"10.1016/j.egyai.2025.100606","DOIUrl":"10.1016/j.egyai.2025.100606","url":null,"abstract":"<div><div>As a key pathway for green hydrogen production, water electrolysis is expected to play a central role in the future energy landscape. However, its large-scale deployment is hindered by challenges related to cost, performance, and durability. The emergence of artificial intelligence (AI) has transformed this field by offering powerful and efficient tools for the design and optimization of electrocatalysts. This review outlines an AI-driven multiscale design framework, highlighting its role at the microscopic scale for identifying atomic-level active sites and key descriptors, at the mesoscopic scale for structural and morphological characterization, and at the macroscopic scale for multi-objective optimization and intelligent control. This multiscale framework demonstrates the potential of AI to accelerate the development of next-generation electrocatalysts. In addition, the integration of generative AI and automated experimental techniques is highlighted as promising strategies to further enhance electrocatalyst discovery and promote the practical implementation of water electrolysis technologies.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100606"},"PeriodicalIF":9.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007632","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

Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems 生成式人工智能：开创智能能源系统研究和教育的新范式

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-02 DOI: 10.1016/j.egyai.2025.100610

Xiaojie Lin , Zheng Luo , Liuliu Du-Ikonen , Xueru Lin , Yihui Mao , Haoyu Jiang , Shuai Wang , Chongshuo Yuan , Wei Zhong , Zitao Yu

Promoting low-carbon energy systems as a centerpiece of global sustainable development goals is essential. As part of this low-carbon transition, smart energy systems have been an active area of research and education, where artificial intelligence (AI) intersects with energy science. It is an emerging area where research and education face new challenges as new knowledge keeps coming in. During this process, generative artificial intelligence (GAI) plays a critical role in education and research activities. However, GAI's impact on smart energy systems research and education is less discussed. Especially, its impact on education is rarely discussed when compared to research. GAI reshapes both the research process and the roles of teachers and students in the course. This perspective offers insights into the ongoing research and education paradigm shifts observed in the smart energy system. This perspective synthesizes existing studies on "GAI for Science" and "GAI for Education" practices in the field of smart energy systems. In research, the impact of GAI is discussed from both macro and micro levels. In education, this perspective examines how a GAI-driven teaching approach addresses the challenges of teaching smart energy systems compared to the traditional approach. This perspective could benefit the discussion of GAI-reshaped research and education in energy science.

促进低碳能源系统作为全球可持续发展目标的核心至关重要。作为低碳转型的一部分，智能能源系统一直是人工智能（AI）与能源科学交叉的一个活跃的研究和教育领域。这是一个新兴领域，随着新知识的不断涌入，研究和教育面临着新的挑战。在这一过程中，生成式人工智能（GAI）在教育和研究活动中发挥着至关重要的作用。然而，GAI对智能能源系统研究和教育的影响却很少被讨论。特别是，与研究相比，它对教育的影响很少被讨论。GAI重塑了研究过程以及教师和学生在课程中的角色。这一观点提供了对智能能源系统中正在进行的研究和教育范式转变的见解。这一视角综合了智能能源系统领域现有的“科学GAI”和“教育GAI”实践研究。在研究中，从宏观和微观两个层面探讨了GAI的影响。在教育方面，这一视角考察了与传统方法相比，人工智能驱动的教学方法如何解决智能能源系统教学的挑战。这一观点可能有利于讨论人工智能重塑的能源科学研究和教育。

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引用次数: 0

Whale algorithm optimized anode pressure controller for fuel cell systems in ejector recirculation mode 鲸鱼算法优化了燃料电池系统在喷射器再循环模式下的阳极压力控制器

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-02 DOI: 10.1016/j.egyai.2025.100611

Wenjun Guo , Renkang Wang , Yu Qiu , Linhong Wu , Kai Li , Hao Tang

The anode pressure control in proton exchange membrane fuel cells (PEMFCs) significantly influences the stable operation of the hydrogen supply system and the internal gas circulation within the fuel cell. An efficient anode pressure control strategy is imperative for enhancing the overall system efficiency and mitigating lifespan degradation. Effective anode pressure control can prevent hydrogen starvation and instability in output performance under rapid load changes and purge disturbances. Fuzzy control has been extensively employed in anode pressure control studies. However, creating fuzzy rules in the control parameter’s tuning process in existing studies is predominantly dependent on expert knowledge, resulting in concerns about control accuracy. This study investigates the potential of employing the whale optimization algorithm to optimize the selection of fuzzy parameters. We first developed a control-oriented model to address the nonlinearity, coupling, and uncertainty in the hydrogen supply system. Then, based on the model and considering load variations and purge disturbances, we integrated feedforward compensation and fuzzy control into the conventional Proportional-Integral (PI) controller to suppress input disturbances, enhance control accuracy, and reduce the pressure response lag. Finally, an innovative fuzzy PI controller with the whale optimization algorithm is proposed to optimize the fuzzy parameter selection, thereby achieving precise anode pressure control. Simulation tests demonstrate that the whale-optimization-based fuzzy PI control (WFLPIF) reduces a root mean square error by 14.3 % (0.636 vs. 0.742) and a mean absolute percentage error by 28.8 % (0.037 vs. 0.052) compared to conventional PI control, while also outperforming feedforward-compensated fuzzy PI control (FLPIF) by 9.5 % in RMSE and 17.8 % in MAPE. This study substantiates the efficacy of the whale optimization algorithm in addressing the anode pressure stability control challenge of fuel cell hydrogen supply systems.

质子交换膜燃料电池（pemfc）的阳极压力控制对供氢系统的稳定运行和燃料电池内部气体循环有着重要的影响。有效的阳极压力控制策略对于提高系统整体效率和减少寿命退化是必不可少的。有效的阳极压力控制可以防止在负载快速变化和吹扫干扰下的氢饥饿和输出性能不稳定。模糊控制在阳极压力控制研究中得到了广泛应用。然而，在现有的研究中，在控制参数整定过程中模糊规则的创建主要依赖于专家知识，导致对控制精度的担忧。本研究探讨了采用鲸鱼优化算法优化模糊参数选择的潜力。我们首先开发了一个面向控制的模型来解决氢供应系统中的非线性、耦合和不确定性。然后，在此基础上，考虑负载变化和吹扫干扰，将前馈补偿和模糊控制集成到传统的比例积分（PI）控制器中，以抑制输入干扰，提高控制精度，减小压力响应滞后。最后，提出了一种创新的模糊PI控制器，采用鲸鱼优化算法对模糊参数选择进行优化，从而实现精确的阳极压力控制。仿真测试表明，与传统PI控制相比，基于鲸鱼优化的模糊PI控制（WFLPIF）的均方根误差降低了14.3% (0.636 vs. 0.742)，平均绝对百分比误差降低了28.8% (0.037 vs. 0.052)，同时在RMSE和MAPE方面也优于前馈补偿模糊PI控制（FLPIF） 9.5%和17.8%。本研究证实了鲸鱼优化算法在解决燃料电池供氢系统阳极压力稳定性控制挑战方面的有效性。

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引用次数: 0

AI challenge for safe and low carbon power grid operation 人工智能对电网安全低碳运行的挑战

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-08-28 DOI: 10.1016/j.egyai.2025.100564

Adrien Pavão , Antoine Marot , Jules Sintes , Viktor Eriksson Möllerstedt , Laure Crochepierre , Karim Chaouache , Benjamin Donnot , Van Tuan Dang , Isabelle Guyon

Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability. To tackle this systems-level bottleneck, Réseau de Transport d’Électricité (RTE) and the research community launched Learn To Run A Power Network (L2RPN), a crowd-sourced competition aiming to accelerate the integration of intermittent renewables into power-grid operations. L2RPN is based on 16 years of weekly scenarios (832 in total) on a 118-node grid under realistic constraints, and casts real-time grid operation as a Markov-Decision-Process. The six participating teams tackled the challenge by developing autonomous agents with various strategies blending heuristics, optimization, data scaling, supervised learning, and reinforcement learning. We provide a detailed overview of all six participants’ performance under the competition’s demanding design. In addition, we present an in-depth analysis of the winning solution – made publicly available – which achieves consistent decision making across scenarios, executes real-time multimodal actions in under five seconds, and performs efficient topology control via action-space reduction and a neural policy that predicts useful grid actions with over 80% accuracy. In parallel, we trained a neural alert module on 315,000 samples derived from top agents, achieving 93.9% recall in flagging dangerous states and allowing agents to predict future failure. Finally, this work not only demonstrates AI’s promise and current limits in real-time grid management but also lays a transparent foundation for more robust, trustworthy systems in the energy transition.

到2050年实现碳中和将要求电网运营商在保持可靠性的同时吸收前所未有的可变太阳能和风能发电量。为了解决这一系统级瓶颈，RTE和研究界发起了学习运行电网（L2RPN），这是一项众源竞赛，旨在加速将间歇性可再生能源整合到电网运营中。L2RPN基于现实约束下118节点网格上16年的每周场景（总共832次），并将实时网格操作转换为马尔可夫决策过程。六个参赛团队通过开发具有各种策略的自主代理来应对这一挑战，这些策略混合了启发式、优化、数据缩放、监督学习和强化学习。我们提供了所有六名参与者在比赛苛刻设计下的详细表现概述。此外，我们对获胜的解决方案进行了深入的分析，该解决方案实现了跨场景的一致决策，在五秒内执行实时多模态动作，并通过动作空间缩减和神经策略执行有效的拓扑控制，预测有用的网格动作的准确率超过80%。与此同时，我们对来自顶级智能体的315,000个样本进行了神经警报模块的训练，在标记危险状态时实现了93.9%的召回率，并允许智能体预测未来的故障。最后，这项工作不仅展示了人工智能在实时电网管理方面的前景和当前的局限性，还为能源转型中更强大、更值得信赖的系统奠定了透明的基础。

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引用次数: 0

A novel framework for vehicle charging pattern recognition and charging duration prediction based on EA-CAE and K-means clustering 基于EA-CAE和K-means聚类的车辆充电模式识别和充电持续时间预测新框架

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-08-27 DOI: 10.1016/j.egyai.2025.100599

Yuemeng Zhang , Longqin Guo , Zeqian Chen , Hongtao Yan , Le Liang , Chunjing Lin

Accurate prediction of electric vehicle (EV) charging duration is critical for improving user satisfaction and enabling efficient real-time charging management. This paper proposes a dynamic charging duration prediction framework for EVs, composed of four coordinated modules: data preprocessing, charging pattern classification, static prediction, and dynamic bias correction. First, raw charging data collected from the Battery Management System (BMS) is cleaned and normalized to address missing and abnormal values. An enhanced convolutional autoencoder (EV-CAE) is then employed to extract multi-scale temporal features, while K-Means clustering is used to identify representative charging behavior patterns. Based on the classified patterns, the static prediction module estimates the current charging duration by leveraging historical data and pattern labels. To enhance adaptability under dynamic conditions, a bias correction mechanism is designed, integrating linear, logarithmic, proportional, and deep learning-based strategies to adjust the prediction results in real time. Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy. In particular, the dynamic correction module increases the coefficient of determination (R²) from 0.948 to 0.960, while maintaining robust performance under fluctuating charging behavior and low-temperature conditions. These results validate the practical applicability and engineering potential of the proposed method for real-time charging duration estimation in intelligent EV charging systems.

准确预测电动汽车（EV）充电持续时间对于提高用户满意度和实现高效的实时充电管理至关重要。本文提出了一种电动汽车充电持续时间动态预测框架，该框架由数据预处理、充电模式分类、静态预测和动态偏差校正四个协调模块组成。首先，从电池管理系统（BMS）收集的原始充电数据被清理和规范化，以解决缺失和异常值。然后使用增强的卷积自编码器（EV-CAE）提取多尺度时间特征，同时使用K-Means聚类识别具有代表性的充电行为模式。基于分类模式，静态预测模块利用历史数据和模式标签来估计当前的收费持续时间。为了增强动态条件下的适应性，设计了一种偏差校正机制，集成了线性、对数、比例和基于深度学习的策略，实时调整预测结果。在实际EV数据集上的实验结果表明，该框架显著提高了预测精度。特别是，动态修正模块将决定系数（R²）从0.948增加到0.960，同时在波动充电行为和低温条件下保持稳健的性能。这些结果验证了该方法在电动汽车智能充电系统中实时充电时间估计的实用性和工程潜力。

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引用次数: 0