Energy and AI最新文献_第4页

Fast and generalisable parameter-embedded neural operators for lithium-ion battery simulation 用于锂离子电池仿真的快速、通用参数嵌入神经算子

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-12-01 DOI: 10.1016/j.egyai.2025.100647

Amir Ali Panahi , Daniel Luder , Billy Wu , Gregory Offer , Dirk Uwe Sauer , Weihan Li

Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring, control, and design at system scale. Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed. In this work, we introduce machine learning surrogates that learn physical dynamics. Specifically, we benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO), which conditions each spectral layer on particle radius and solid-phase diffusivity. We extend the comparison to classical machine-learning baselines by including U-Nets. Models are trained on simulated trajectories spanning four current families (constant, triangular, pulse-train, and Gaussian-random-field) and a full range of State-of-Charge (SOC) (0 % to 100 %). DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. The basic FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver. Consequently, PE-FNO’s capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation, recovering anode and cathode diffusivities with 1.14 % and 8.4 % mean absolute percentage error, respectively, and 0.5918 percentage points higher error in comparison with classical methods. These results pave the way for neural operators to meet the accuracy, speed and parametric flexibility demands of real-time battery management, design-of-experiments and large-scale inference. PE-FNO outperforms conventional neural surrogates, offering a practical path towards high-speed and high-fidelity electrochemical digital twins.

锂离子电池的数字孪生体越来越多地用于系统规模的预测监测、控制和设计。提高它们的能力需要提高它们的物理保真度，同时保持亚毫秒的计算速度。在这项工作中，我们引入了学习物理动力学的机器学习代理。具体来说，我们对单粒子模型（SPM）的三种算子学习替代方法进行了基准测试：深度算子网络（DeepONets）、傅立叶神经算子（FNOs）和新提出的参数嵌入傅立叶神经算子（PE-FNO），该算子根据粒子半径和固相扩散率来约束每个光谱层。我们通过包括U-Nets将比较扩展到经典的机器学习基线。模型在模拟轨迹上进行训练，这些轨迹跨越四个电流族（常量、三角形、脉冲序列和高斯随机场）和全范围的充电状态（SOC）（0%至100%）。DeepONet精确地复制了恒流行为，但在处理更多动态负载时遇到了困难。基本FNO保持网格不变性，将浓度误差保持在1%以下，所有负载类型的电压平均绝对误差低于1.7 mV。引入参数嵌入会略微增加误差，但可以泛化到不同的半径和扩散系数。PE-FNO的执行速度大约是16线程SPM求解器的200倍。因此，在贝叶斯优化参数估计任务中探索了PE-FNO在逆任务中的能力，回收阳极和阴极扩散系数的平均绝对百分比误差分别为1.14%和8.4%，与经典方法相比误差提高了0.5918个百分点。这些结果为神经算子满足实时电池管理、实验设计和大规模推理的准确性、速度和参数灵活性需求铺平了道路。PE-FNO优于传统的神经替代物，为实现高速高保真的电化学数字孪生提供了切实可行的途径。

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引用次数: 0

Deep reinforcement learning for joint dispatch of battery storage and gas turbines in renewable-powered microgrids 可再生微电网中电池储能与燃气轮机联合调度的深度强化学习

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-12-01 DOI: 10.1016/j.egyai.2025.100653

Manuel Sage , Khalil Al Handawi , Yaoyao Fiona Zhao

This study introduces a novel deep reinforcement learning (DRL) framework for the joint dispatch of Gas Turbines (GTs) and Battery Energy Storage Systems (BESs) in microgrids that face the variability of renewable energy sources and demands. BESs can store surplus renewable energy for nearly instantaneous use, while GTs offer sustained energy output over longer periods, offering complementary benefits. Previous studies often oversimplified GT operations, neglecting critical factors such as ramp-up times and increased degradation from frequent starts. This research addresses these gaps by proposing an advanced modeling framework that accurately captures the dynamic interaction between GTs and BESs, including GT ramp-up times and maintenance costs associated with operational cycles. Through extensive case studies involving diverse microgrid configurations, we demonstrate that DRL effectively learns dispatch policies directly from historical data, outperforming traditional optimization techniques. Deploying DRL to our framework yields more realistic dispatch policies, reducing GT maintenance costs by avoiding frequent starts. The proposed framework has significant potential to improve energy management strategies and to streamline the planning of hybrid energy systems. To encourage further research, we have released our codebase to the public, enabling the scientific community to build upon our findings.

本研究引入了一种新的深度强化学习（DRL）框架，用于面对可再生能源和需求变化的微电网中燃气轮机（gt）和电池储能系统（BESs）的联合调度。BESs可以储存多余的可再生能源，几乎可以即时使用，而GTs可以在更长的时间内提供持续的能源输出，提供互补的好处。以前的研究往往过于简化了GT操作，忽略了一些关键因素，如加速时间和频繁启动导致的性能下降。本研究通过提出一种先进的建模框架来解决这些差距，该框架准确地捕获了GT和BESs之间的动态交互，包括GT的启动时间和与操作周期相关的维护成本。通过涉及不同微电网配置的广泛案例研究，我们证明了DRL直接从历史数据中有效地学习调度策略，优于传统的优化技术。将DRL部署到我们的框架中可以产生更现实的调度策略，通过避免频繁启动来降低GT维护成本。提出的框架在改善能源管理战略和简化混合能源系统规划方面具有重大潜力。为了鼓励进一步的研究，我们向公众发布了我们的代码库，使科学界能够在我们的发现的基础上进行构建。

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引用次数: 0

An intelligent multi-layer framework for attack conduction, detection and reconstruction in the smart grid with renewable energies 一种面向可再生能源智能电网攻击传导、检测与重构的多层智能框架

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-11-19 DOI: 10.1016/j.egyai.2025.100636

Mostafa Mohammadpourfard , Yang Weng , Mahdi Eghbali , Manohar Chamana , Suhas Pol

The rapid proliferation of renewable energy sources (RESs) has enhanced operational flexibility but intensified cybersecurity concerns in modern power systems. In this work, we investigate how attackers can exploit the increased variability introduced by RESs to orchestrate false data injection attacks (FDIAs). First, we propose a targeted attack strategy based on Jensen–Shannon divergence (JSD) and the Kolmogorov–Smirnov (KS) test. This two-stage procedure identifies measurements that exhibit minimal distributional shifts after RES integration. False data are then injected into these stable measurements, blending seamlessly into the expanded measurement space and increasing attack stealth. Second, we develop a customized hybrid deep learning model, combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) units to capture both spatial correlations and temporal dynamics in power system measurements. This design explicitly addresses concept drift arising from fluctuating load and generation profiles, ensuring persistent detection accuracy. Third, we integrate an Autoencoder (AE)–based reconstruction mechanism to repair compromised measurements upon detection, mitigating denial-of-service (DoS) scenarios that could result from discarding suspect data. Our evaluations on the IEEE 14-bus and 118-bus systems, using real-world load profiles, confirm that the JSD–KS approach boosts attack stealth while the CNN–LSTM–AE pipeline achieves robust detection and recovery. Our experiments on the IEEE 14-bus and 118-bus systems demonstrate F1-score gains of up to 3% over the strongest CLSTM baseline under traditional FDIA scenarios, and up to 13% under our intelligent FDIA, while also reducing AE reconstruction RMSE by approximately 6%–7%. This integrated strategy offers a multi-layered defense against evolving cyber threats in renewable-rich smart grids.

可再生能源（RESs）的快速扩散提高了现代电力系统的运行灵活性，但也加剧了对网络安全的担忧。在这项工作中，我们研究了攻击者如何利用RESs引入的增加的可变性来编排虚假数据注入攻击（FDIAs）。首先，我们提出了一种基于Jensen-Shannon散度（JSD）和Kolmogorov-Smirnov （KS）检验的针对性攻击策略。这个两阶段的程序确定了RES整合后表现出最小分布变化的测量值。然后将虚假数据注入这些稳定的测量中，无缝地融合到扩展的测量空间中，并增加攻击的隐蔽性。其次，我们开发了一个定制的混合深度学习模型，将卷积神经网络（cnn）和长短期记忆（LSTM）单元结合起来，以捕获电力系统测量中的空间相关性和时间动态。这种设计明确地解决了由波动负载和发电剖面引起的概念漂移，确保了持久的检测精度。第三，我们集成了基于自动编码器（AE）的重建机制，以在检测时修复受损的测量，减轻因丢弃可疑数据而导致的拒绝服务（DoS）场景。我们对IEEE 14总线和118总线系统的评估，使用真实的负载配置文件，证实了JSD-KS方法提高了攻击的隐身性，而CNN-LSTM-AE管道实现了强大的检测和恢复。我们在IEEE 14总线和111总线系统上的实验表明，在传统FDIA场景下，f1得分比最强CLSTM基线提高了3%，在我们的智能FDIA场景下提高了13%，同时也将声发射重建RMSE降低了大约6%-7%。这种综合战略为可再生能源丰富的智能电网提供了多层次的防御，以应对不断发展的网络威胁。

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引用次数: 0

Autonomous wind turbine performance curve modeling based on SCADA data: A vision intelligence powered method 基于SCADA数据的自主风力机性能曲线建模：一种视觉智能驱动方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-11-07 DOI: 10.1016/j.egyai.2025.100645

Chufan Wu , Luoxiao Yang , Zijun Zhang

A central question in efficient wind farm big-data analytics is how to design an algorithm for autonomously extracting performance curves of wind turbines based on data collected via wind farm supervisory control and data acquisition (SCADA) systems. This paper investigates this question systematically, focusing on a challenging setting: the end-to-end autonomous analytics for directly generating mathematical functions of wind turbine performance curves from raw SCADA data. We propose a vision generative modeling (VGM) paradigm for autonomous development of wind turbine performance curve models. We discover that, compared with prevalently discussed numerical fitting-based performance curve modeling (NFM) methods, VGM directly working on raw data without any data preprocessing and model parameter tuning offers more generalizable and accurate results in deriving performance curves as well as their mathematical forms. The success of VGM is achieved by three computational steps developed in this study. By comparing with a set of state-of-the-art NFM benchmarks in multiple performance curve modeling tasks, we observe that VGM consistently performs more advantageously by achieving a 75.1% accuracy improvement in wind power curve modeling with insufficient SCADA data and an 84.3% improvement in modeling the rotor speed curve based on faulty field data. This work presents a milestone in autonomous wind turbine SCADA data analytics, which possesses a great potential of spanning to autonomous analytics of measured data of other industrial systems.

高效风电场大数据分析的一个核心问题是，如何设计一种算法，根据风电场监控和数据采集（SCADA）系统收集的数据，自动提取风力涡轮机的性能曲线。本文系统地研究了这个问题，重点研究了一个具有挑战性的设置：从原始SCADA数据直接生成风力涡轮机性能曲线的数学函数的端到端自主分析。我们提出了一种视觉生成建模（VGM）范式，用于风力发电机性能曲线模型的自主开发。我们发现，与目前广泛讨论的基于数值拟合的性能曲线建模（NFM）方法相比，VGM直接处理原始数据而不进行任何数据预处理和模型参数调优，在导出性能曲线及其数学形式方面提供了更通用和准确的结果。VGM的成功是通过本研究开发的三个计算步骤实现的。通过在多个性能曲线建模任务中与一组最先进的NFM基准进行比较，我们观察到VGM始终表现出更大的优势，在SCADA数据不足的情况下，风电曲线建模精度提高了75.1%，在基于故障现场数据的转子转速曲线建模精度提高了84.3%。这项工作是风力发电机SCADA数据自主分析的一个里程碑，具有跨越其他工业系统测量数据自主分析的巨大潜力。

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引用次数: 0

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics 可解释的人工智能将钙钛矿发光图像与电流-电压指标联系起来

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-11-03 DOI: 10.1016/j.egyai.2025.100640

Andrew Glaws , Jackson W. Schall , Andrew Ballen , Amy E. Louks , Kristopher O. Davis , Axel F. Palmstrom , Juliette Ugirumurera , Dana B. Kern

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current–voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

随着对低成本、高效率太阳能技术的需求不断增长，金属卤化物钙钛矿（MHP）太阳能电池因其高功率转换效率而成为下一代光伏电池的有希望的候选者。然而，它们的耐久性差和制造一致性问题仍然是商业化的重大障碍。在这项工作中，我们开发了深度学习模型来支持材料表征，并提供对影响性能的特征和过程的洞察。使用预训练模型的迁移学习来训练模型，以基于MHP器件的输入电致发光（EL）和光致发光（PL）图像的不同组合来预测相关的电流-电压（IV）指标。我们研究了哪些图像类型在准确预测不同的静脉注射指标方面最具信息性。此外，我们使用可解释的人工智能（XAI）技术来提供对设备中驱动性能差异的特定空间特征的见解。我们发现稳定的发光图像（例如，在器件偏置至少1分钟后收集的图像）更适合预测开路电压（通过PL）和短路电流（通过带有EL的PL）的指标，但预测填充因子和总功率输出可能使用EL图像的时间演变。基于每个器件性能指标的集成梯度生成的归因掩模，我们进一步提出了与大小空间缺陷类别相关的不同损耗机制。总的来说，这个案例研究强调了XAI方法在简化MHP设备分析和加速空间缺陷与性能影响之间关系的详细理解方面的潜在适用性。

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引用次数: 0

A new approach to fundamental electricity market modelling: Exploring market dynamics and speculator influence 电力市场基本模型的新方法：探索市场动态和投机者的影响

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-11-01 DOI: 10.1016/j.egyai.2025.100639

Joseph Collins, Andreas Amann, Kieran Mulchrone

Fundamental short-term electricity market models typically rely on expert-driven assumptions and manual, iterative calibration, and often neglect strategic bidding behaviours. To address these issues, we develop a bottom-up (fundamental) model that forecasts day-ahead outcomes from publicly available participant order data using regularised regression, machine learning, and neural network techniques. We introduce an explainability framework that decomposes forecast errors at participant, cohort, and aggregate levels, linking forecast performance to forecast trading behaviours. Compared with a benchmark top-down model, the bottom-up approach yields lower price forecast accuracy but demonstrates an ability to capture market dynamics. Where forecast dynamics diverge from observed outcomes, many misaligned cases are attributable to specific cohorts, particularly financial traders (speculators). Beyond forecasting, the framework offers complementary applications: the modelling approach can support calibration of traditional fundamental models and serve as a stand-alone forecaster in markets beyond day-ahead where order data are available, while the explainability component can apply to both bottom-up and hybrid modelling approaches. The study highlights the challenges inherent in bottom-up fundamental models, while showing how our approach provides new insights and practical tools to support their calibration and application.

基本的短期电力市场模型通常依赖于专家驱动的假设和手动的、迭代的校准，往往忽略了战略投标行为。为了解决这些问题，我们开发了一个自下而上的（基本）模型，该模型使用正则化回归、机器学习和神经网络技术，从公开可用的参与者订单数据预测一天前的结果。我们引入了一个可解释性框架，该框架分解了参与者、队列和总体水平的预测误差，将预测绩效与预测交易行为联系起来。与基准自顶向下模型相比，自底向上方法的价格预测准确性较低，但显示出捕捉市场动态的能力。当预测动态偏离观察结果时，许多不一致的情况可归因于特定群体，特别是金融交易员（投机者）。除了预测之外，该框架还提供了互补的应用：建模方法可以支持传统基本模型的校准，并在有订单数据的市场中作为独立的预测器，而可解释性组件可以适用于自下而上和混合建模方法。该研究强调了自下而上的基本模型所固有的挑战，同时展示了我们的方法如何提供新的见解和实用工具来支持其校准和应用。

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引用次数: 0

Hybrid LSTM-MLP model with NSGA-II-based hyperparameter optimization for non-invasive occupancy estimation 基于nsga - ii超参数优化的混合LSTM-MLP模型无创占用估计

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-10-31 DOI: 10.1016/j.egyai.2025.100643

Moisés Cordeiro-Costas , Raquel Pérez-Orozco , Pablo Hernandez-Cruz , Francisco Troncoso-Pastoriza , Enrique Granada-Álvarez

Understanding occupancy patterns in buildings is critical for optimizing energy use, improving indoor comfort, and enabling smarter building management systems. Traditional methods for occupancy detection often rely on dense networks of sensors or invasive technologies such as cameras or wearables, raising concerns about cost, scalability, and privacy. In contrast, data-driven approaches based on environmental and energy-related signals offer a promising alternative: they are cost-effective, unobtrusive, and compatible with existing infrastructure. This study presents a robust and generalizable deep learning framework for occupancy estimation using easily accessible data sources, including electricity consumption, CO₂ concentration, and working-hour schedules. Through an extensive comparative analysis of different input combinations in supervised model configurations, the optimal trade-off between accuracy and computational cost for real-time deployment is identified. The results highlight the value of selecting appropriate variables and reveal how models using minimal inputs can provide reliable estimations when properly designed. By demonstrating a non-invasive, privacy-preserving, and scalable approach to occupancy modeling, this work contributes to the development of energy-aware and intelligent buildings, essential for meeting sustainability goals and enhancing user-centric building automation.

了解建筑物的使用模式对于优化能源使用、提高室内舒适度和实现更智能的建筑管理系统至关重要。传统的占用检测方法通常依赖于密集的传感器网络或侵入性技术，如摄像头或可穿戴设备，这引起了对成本、可扩展性和隐私的担忧。相比之下，基于环境和能源相关信号的数据驱动方法提供了一个很有前途的选择：它们具有成本效益，不引人注目，并且与现有基础设施兼容。本研究提出了一个强大的、可推广的深度学习框架，用于使用易于访问的数据源进行占用估计，包括电力消耗、二氧化碳浓度和工作时间安排。通过对监督模型配置中不同输入组合的广泛比较分析，确定了实时部署的精度和计算成本之间的最佳权衡。结果突出了选择适当变量的价值，并揭示了使用最小输入的模型如何在适当设计时提供可靠的估计。通过展示一种非侵入性、隐私保护和可扩展的入住率建模方法，这项工作有助于能源意识和智能建筑的发展，这对于实现可持续发展目标和增强以用户为中心的建筑自动化至关重要。

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引用次数: 0

A charging demand prediction method for individual electric vehicle users based on dual-layer multisource data clustering and a LightGBM 基于双层多源数据聚类和LightGBM的个人电动汽车用户充电需求预测方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-10-29 DOI: 10.1016/j.egyai.2025.100644

Yunfei Mu , Ruichao Zhou , Kangning Zhao , Hongjie Jia , Guoqiang Zu , Ye Yang

The charging behaviors of electric vehicle (EV) users exhibit high randomness and individual heterogeneity, with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations. Compared with EV cluster-layer prediction, predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables (e.g., weather and holidays) and prediction accuracy, thereby imposing higher robustness requirements on prediction algorithms. An individual-user EV charging demand prediction method that integrates multisource data with a dual-layer clustering approach and a light gradient boosting machine (LightGBM) is proposed in this study to address these technical challenges. First, a multisource dataset that incorporates user charging behavior data and exogenous variables (meteorological factors and date types) is constructed. A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed, thereby establishing a classification feature space that characterizes different charging types and user groups. A predictive model is subsequently developed using the LightGBM algorithm, which directly incorporates classification features as its inputs, effectively mitigating the information loss associated with the traditional categorical variable encoding process. Finally, employing EV users from a typical residential community in northern China as an empirical case, comparative experiments are performed to validate the proposed method, demonstrating its effectiveness at improving prediction accuracy.

电动汽车用户充电行为具有高度随机性和个体异质性，充电时间、充电能级等关键参数波动较大。与电动汽车簇层预测相比，个体用户充电需求预测不仅需要分析更复杂的充电行为，还需要建立外生变量（如天气、节假日）与预测精度之间的耦合模型，从而对预测算法的鲁棒性提出了更高的要求。为了解决这些技术难题，本文提出了一种将多源数据与双层聚类方法和光梯度增强机（LightGBM）相结合的个人用户电动汽车充电需求预测方法。首先，构建了包含用户收费行为数据和外生变量（气象因素和日期类型）的多源数据集。采用数据层聚类识别收费类型和用户层聚类分类用户组的双层特征提取机制，建立具有不同收费类型和用户组特征的分类特征空间。随后，使用LightGBM算法开发了预测模型，该算法直接将分类特征作为输入，有效地减轻了传统分类变量编码过程中相关的信息损失。最后，以中国北方典型住宅社区的电动汽车用户为实证案例，进行了对比实验，验证了该方法在提高预测精度方面的有效性。

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引用次数: 0

A comparative analysis of regression algorithms and a real world application of multivariable energy signatures 回归算法的比较分析和多变量能量特征的实际应用

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-10-29 DOI: 10.1016/j.egyai.2025.100641

Simone Eiraudo , Daniele Salvatore Schiera , Luca Barbierato , Alena Trifirò , Lorenzo Bottaccioli , Andrea Lanzini

An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals. Such models should enhance the understanding of the energy behavior of a building, the impact of the external variables, and the causes of inefficiencies. Energy Signatures can fill this role, with particular regard to the consumption due to air conditioning. Univariate models, neglecting the impact of solar radiation, have been widely adopted for Energy Signature analysis. This paper presents Multivariable Energy Signatures considering outdoor temperature and solar radiation. The application on a real-world dataset of multivariable non-parametric approaches stands out from previous works in the ES sector. This led to a mean improvement of 0.768 to 0.804 of the coefficients of determination calculated over 103 real-world case studies. Moreover, Neural Networks outperformed several literature algorithms regarding accuracy, robustness, and scalability. The paper also discusses issues regarding the time resolution of input data and introduces appropriate visualization tools to employ Multivariable Energy Signatures as diagnostic tools.

需要一个建筑能源模型生态系统来促进改造过程，以提高能源效率和实现可持续发展目标。这样的模型应该加强对建筑能源行为的理解，外部变量的影响，以及效率低下的原因。能源签名可以填补这一角色，特别是考虑到由于空调的消耗。忽略太阳辐射影响的单变量模型已被广泛用于能量特征分析。本文提出了考虑室外温度和太阳辐射的多变量能量特征。在多变量非参数方法的真实数据集上的应用从ES部门的先前工作中脱颖而出。这导致在103个实际案例研究中计算的决定系数的平均改进为0.768至0.804。此外，神经网络在准确性、鲁棒性和可扩展性方面优于几种文献算法。本文还讨论了输入数据的时间分辨率问题，并介绍了适当的可视化工具，以使用多变量能量特征作为诊断工具。

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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 : 2025-10-28 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%。

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