Energy and AI最新文献_第2页

Assessing long-term electricity market design for ambitious decarbonization targets using multi-agent reinforcement learning 使用多智能体强化学习评估雄心勃勃的脱碳目标的长期电力市场设计

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2025.100665

Javier Gonzalez-Ruiz , Carlos Rodriguez-Pardo , Iacopo Savelli , Alice Di Bella , Massimo Tavoni

Electricity systems are key to transforming today’s society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to support policymakers and other stakeholders in designing, testing, and evaluating long-term markets, this work presents a multi-agent reinforcement learning model capable of capturing the key features of decarbonizing energy systems. Profit-maximizing generation companies make investment decisions in the wholesale electricity market, responding to system needs, competitive dynamics, and policy signals. The model employs independent proximal policy optimization, which was selected for suitability to the decentralized and competitive environment. Nevertheless, given the inherent challenges of independent learning in multi-agent settings, an extensive hyperparameter search ensures that decentralized training yields market outcomes consistent with competitive behavior. The model is applied to a stylized version of the Italian electricity system and tested under varying levels of competition, market designs, and policy scenarios. Results highlight the critical role of market design for decarbonizing the electricity sector and avoiding price volatility. The proposed framework allows assessing long-term electricity markets in which multiple policy and market mechanisms interact simultaneously, with market participants responding and adapting to decarbonization pathways.

电力系统是将当今社会转变为无碳经济的关键。长期电力市场机制，包括拍卖、支持计划和其他政策工具，对形成发电组合至关重要。鉴于需要更先进的工具来支持政策制定者和其他利益相关者设计、测试和评估长期市场，这项工作提出了一个多智能体强化学习模型，能够捕捉脱碳能源系统的关键特征。利润最大化的发电公司根据系统需求、竞争动态和政策信号，在批发电力市场上做出投资决策。该模型采用独立的最近邻策略优化，选择最近邻策略以适应分散的竞争环境。然而，考虑到在多智能体设置中独立学习的固有挑战，广泛的超参数搜索确保分散训练产生与竞争行为一致的市场结果。该模型应用于意大利电力系统的一个程式化版本，并在不同程度的竞争、市场设计和政策场景下进行了测试。结果强调了市场设计对电力部门脱碳和避免价格波动的关键作用。拟议的框架允许评估长期电力市场，其中多种政策和市场机制同时相互作用，市场参与者对脱碳途径作出反应和适应。

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引用次数: 0

Towards secure federated learning for energy forecasting under adversarial attacks 面向对抗性攻击下能源预测的安全联邦学习

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2026.100680

Jonas Sievers , Krupali Kumbhani , Thomas Blank , Frank Simon , Andreas Mauthe

Federated learning is increasingly used in energy forecasting, enabling buildings to collaboratively predict load, photovoltaic generation, and prosumption while preserving data privacy. However, this collaborative nature introduces new vulnerabilities, as manipulations by a single participant can propagate across the network. Such attacks can undermine grid balancing, limit flexibility provision, and reduce trust in decentralized energy systems. This work presents a comprehensive study of adversarial threats and defenses in federated energy forecasting. We compare structured manipulations generated with Generative Adversarial Networks against simple random perturbations in two attack scenarios: (i) data poisoning, where corrupted training data degrade global accuracy, and (ii) backdoors, where hidden triggers distort predictions in targeted time windows. Our experiments show that poisoning can increase global forecasting errors by up to 131 %, while backdoors raise local errors by up to 48 %. In both cases, Generative Adversarial Network-based attacks are consistently more effective than random perturbations, with backdoors proving especially challenging to detect due to their localized effect. To mitigate these threats, we evaluate four defense strategies: weighted aggregation, larger participant clusters, local retraining, and their coordinated integration into a secure framework. Results demonstrate that these defenses substantially reduce the impact of attacks, and in some cases even improve baseline accuracy, thereby enhancing the resilience of federated energy forecasting against adversarial manipulation.

联邦学习越来越多地用于能源预测，使建筑物能够在保护数据隐私的同时协同预测负荷、光伏发电和消耗。然而，这种协作性引入了新的漏洞，因为单个参与者的操作可以通过网络传播。这种攻击会破坏电网平衡，限制灵活性供应，并降低对分散能源系统的信任。这项工作对联邦能源预测中的对抗性威胁和防御进行了全面研究。我们比较了生成式对抗网络在两种攻击场景下对简单随机扰动产生的结构化操作：(i)数据中毒，其中损坏的训练数据会降低全局准确性，以及（ii）后门，其中隐藏的触发器会扭曲目标时间窗口中的预测。我们的实验表明，中毒可以使全局预测误差增加高达131%，而后门可以使局部误差增加高达48%。在这两种情况下，基于生成对抗网络的攻击始终比随机扰动更有效，由于其局部效应，后门被证明特别具有挑战性。为了减轻这些威胁，我们评估了四种防御策略：加权聚合、更大的参与者集群、本地再培训以及它们协调集成到一个安全框架中。结果表明，这些防御大大减少了攻击的影响，在某些情况下甚至提高了基线准确性，从而增强了联邦能源预测对对抗性操纵的弹性。

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引用次数: 0

A graph neural network enhanced decision transformer for efficient optimization in dynamic smart charging environments 一种基于图神经网络的决策变压器，用于动态智能充电环境的高效优化

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2026.100679

Stavros Orfanoudakis, Nanda Kishor Panda, Peter Palensky, Pedro P. Vergara

Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements. Mathematical optimization becomes too slow at scale, while online reinforcement learning struggles with sparse rewards and safety. This paper proposes GNN-DT, a topology-aware Decision Transformer that combines graph neural network embeddings with sequence modeling to learn charging policies from offline trajectories. The method operates over variable numbers of vehicles and chargers without retraining. Evaluated on realistic smart charging scenarios, GNN-DT achieves near-optimal performance, reaching rewards within 5 percent of an oracle solver while using up to 10

\times

fewer training trajectories than baseline methods. It consistently outperforms online and offline reinforcement learning approaches and generalizes to unseen fleet sizes and network topologies. Inference runs in milliseconds, making the approach suitable for real-time deployment in large-scale charging systems.

电动汽车智能充电要求在不确定条件下快速决策，同时严格执行电网和用户要求。数学优化在规模上变得太慢，而在线强化学习在稀疏的奖励和安全性上挣扎。本文提出了一种拓扑感知决策转换器GNN-DT，它将图神经网络嵌入与序列建模相结合，从离线轨迹中学习充电策略。该方法可以在不需要再培训的情况下在可变数量的车辆和充电器上运行。通过对现实智能充电场景的评估，GNN-DT实现了近乎最佳的性能，在5%的oracle求解器内获得奖励，同时使用的训练轨迹比基线方法少10倍。它始终优于在线和离线强化学习方法，并推广到看不见的车队规模和网络拓扑。推理以毫秒为单位运行，使该方法适合大规模收费系统的实时部署。

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引用次数: 0

Dual-path feature extraction with MoE optimization for ultra-short-term wind power forecasting 基于MoE优化的双路径特征提取超短期风电预测

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2026.100684

Yongsheng Wang , Xuehui Wang , Guangchen Liu , Shijie Guan , Zhe Zhang , Hailong Li

Accurate ultra-short-term wind power forecasting is critical for maintaining the stability and efficiency of modern power systems, yet it remains challenging due to the high volatility and nonlinear dynamics of wind energy. This study proposes a hybrid forecasting framework that integrates deep learning-based temporal modeling, fluctuation-aware feature engineering, and expert-driven hyperparameter optimization. A convolutional attention network is designed to capture both local temporal patterns and long-term dependencies in wind power time series, while financial technical indicators are incorporated to enhance the representation of short-term fluctuation characteristics. To further improve forecasting accuracy and robustness, a mixture of experts strategy is employed to jointly optimize model hyperparameters and indicator construction. The final power forecasting is performed using a gradient boosting decision tree model with strong generalization capability. Experimental evaluations conducted on multiple real-world wind farm datasets demonstrate that the proposed framework consistently outperforms state of the art machine learning and deep learning approaches in terms of forecasting accuracy and efficiency. For example, on Dataset 1, the proposed method achieves a 5.03% reduction in mean square error compared with the strongest deep learning baseline. The results indicate that the proposed approach effectively captures both rapid fluctuations and underlying temporal trends, providing a reliable and practical solution for ultra-short-term wind power forecasting in complex operational environments.

准确的超短期风电预测对于维持现代电力系统的稳定性和效率至关重要，但由于风能的高波动性和非线性动态特性，超短期风电预测仍然具有挑战性。本研究提出了一个混合预测框架，该框架集成了基于深度学习的时间建模、波动感知特征工程和专家驱动的超参数优化。设计了一个卷积关注网络，以捕捉风力发电时间序列中的当地时间模式和长期依赖关系，同时纳入金融技术指标，以增强对短期波动特征的表示。为了进一步提高预测精度和鲁棒性，采用混合专家策略对模型超参数和指标构建进行联合优化。最后利用具有较强泛化能力的梯度增强决策树模型进行电力预测。在多个实际风电场数据集上进行的实验评估表明，所提出的框架在预测准确性和效率方面始终优于最先进的机器学习和深度学习方法。例如，在数据集1上，与最强深度学习基线相比，所提出的方法实现了5.03%的均方误差降低。结果表明，该方法有效地捕捉了快速波动和潜在的时间趋势，为复杂运行环境下的超短期风电预测提供了可靠和实用的解决方案。

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引用次数: 0

Evolving power system operator rules for real-time congestion management 基于实时拥塞管理的电力系统算子规则演化

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2025.100672

Ferinar Moaidi , Ricardo J. Bessa

The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and redispatching. Traditional expert-driven decision-making rules may become insufficient to manage the increasing complexity of real-time grid operations and derive remedial actions under the N-1 contingency. This work proposes a novel hybrid AI framework for power grid topology control that integrates genetic network programming (GNP), reinforcement learning, and decision trees. A new variant of GNP is introduced that is capable of evolving the decision-making rules by learning from data in a reinforcement learning framework. The graph-based evolutionary structure of GNP and decision trees enables transparent, traceable reasoning. The proposed method outperforms both a baseline expert system and a state-of-the-art deep reinforcement learning agent on the IEEE 118-bus system, achieving up to an 28% improvement in a key performance metric used in the Learning to Run a Power Network (L2RPN) competition.

可再生能源的日益一体化和能源需求的广泛电气化大大减少了电网的容量边际。这需要一种更灵活的电网运行方式，例如，将实时拓扑优化和重新调度相结合。传统的专家驱动的决策规则可能不足以管理日益复杂的实时电网运行，并在N-1突发事件下制定补救措施。本研究提出了一种用于电网拓扑控制的新型混合人工智能框架，该框架集成了遗传网络规划（GNP）、强化学习和决策树。引入了一种新的GNP变体，它能够通过从强化学习框架中的数据中学习来进化决策规则。GNP和决策树的基于图的进化结构支持透明的、可追溯的推理。所提出的方法在IEEE 118总线系统上优于基线专家系统和最先进的深度强化学习代理，在学习运行电网（L2RPN）竞赛中使用的关键性能指标提高了28%。

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引用次数: 0

Bubble segmentation and heat flux estimation in nucleate boiling using a resource-efficient deep learning framework 基于资源高效深度学习框架的核沸腾气泡分割和热流估计

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2025.100668

UngJin Na , Bruno Pinheiro Serrao , JunYoung Seo , Youngjoon Suh , Juliana Pacheco Duarte , Yoonjin Won , HangJin Jo

Boiling is a critical heat transfer mechanism in high-power-density systems driven by bubble ebullition cycles that efficiently manage thermal loads. However, the simultaneous challenges of the millisecond-scale lifecycle of strongly deformable bubbles and the heavy computational burden of processing gigabytes of high-speed photographic data demand immense resources with meticulous parameter tuning for conventional image processing techniques. Herein, we present an end-to-end resource-efficient deep learning framework that overcomes the diagnostic bottlenecks of bubble dynamics visualization and delivers quantitative insights into these processes using a Mask R-CNN model trained on a synthetically augmented boiling dataset. The model extracts spatiotemporal bubble characteristics by segmenting bubble contours across varying heat fluxes ranging from 100 to 1000 kW/m², tracking unique bubbles, and detecting bubble departure from the surface, enabling the estimation of bubble-level contributions to macroscopic cooling performance. Our resource-efficient approach can be rapidly retuned for new boiling experiments with low manual effort, independent of the setup.

沸腾是一个关键的传热机制，在高功率密度系统驱动的气泡沸腾循环，有效地管理热负荷。然而，同时面临的挑战是，强烈可变形气泡的毫秒级生命周期和处理千兆字节高速摄影数据的沉重计算负担需要大量的资源和对传统图像处理技术进行细致的参数调整。在此，我们提出了一个端到端资源高效的深度学习框架，该框架克服了气泡动力学可视化的诊断瓶颈，并使用在综合增强沸腾数据集上训练的Mask R-CNN模型提供了对这些过程的定量见解。该模型通过在100至1000 kW/m²的不同热通量范围内分割气泡轮廓，跟踪独特的气泡，并检测气泡偏离表面，从而提取时空气泡特征，从而能够估计气泡水平对宏观冷却性能的贡献。我们的资源高效的方法可以快速返回新的沸腾实验与低人工，独立的设置。

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引用次数: 0

An analytical solutions-enhanced neural network for high-accuracy modeling: A case study on gas turbine 用于高精度建模的解析解增强神经网络：以燃气轮机为例

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2026-01-01 DOI: 10.1016/j.egyai.2025.100674

Zepeng Han , Wei Han , Wenjing Ma , Jichao Li , Jun Sui

Physics-informed neural networks have gained wide application due to their data efficiency and generation ability. Nevertheless, their application is restricted in practical engineering problems where complex physical mechanisms make it difficult to formulate or solve the corresponding partial differential equations. To solve the above problems, Analytical solutions are obtained by finite-step algebraic computation. Based on these solutions, an analytical solutions-enhanced physical model is trained using a multi-layer perceptron to replace conventional partial differential equations. By incorporating weighted loss functions into the multi-layer perceptron, an analytical solutions-enhanced neural network is developed to address complex problems in practical engineering applications. Taking gas turbines in the energy sector as a case, the proposed model is compared with conventional prediction models. Results indicate that the proposed model achieves a mean square error of 0.0606, a mean absolute error of 0.0867 on the test set, corresponding to maximum reductions of 99.17 % and 90.98 % relative to conventional prediction models, respectively. Additionally, sample size analyses indicate that the proposed model can reduce the sample size by at least 35.45 %. The analytical solutions-enhanced neural network established in this study provides a novel approach to integrating physical models and neural networks, thereby expending the application of physics-informed neural networks to real-word engineering problems.

基于物理的神经网络以其数据效率和生成能力得到了广泛的应用。然而，它们在实际工程问题中的应用受到限制，因为复杂的物理机制使其难以表述或求解相应的偏微分方程。针对上述问题，采用有限步代数计算得到了解析解。基于这些解，使用多层感知器训练了一个解析解增强的物理模型，以取代传统的偏微分方程。通过将加权损失函数整合到多层感知器中，开发了一种分析解增强神经网络，以解决实际工程应用中的复杂问题。并以能源行业燃气轮机为例，与常规预测模型进行了比较。结果表明，该模型在测试集上的均方误差为0.0606，平均绝对误差为0.0867，与常规预测模型相比，最大误差分别降低了99.17%和90.98%。此外，样本量分析表明，该模型可以减少至少35.45%的样本量。本研究建立的分析解增强神经网络提供了一种整合物理模型和神经网络的新方法，从而扩展了物理信息神经网络在实际工程问题中的应用。

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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 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

Battery aging and behavioral pattern identification: A fleet analytics framework for regulatory compliance testing 电池老化和行为模式识别：用于法规遵从性测试的车队分析框架

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-12-22 DOI: 10.1016/j.egyai.2025.100669

Robin Saam , Ludwig Hagen Letzig , Jens Grabow , Ralf Benger , Ines Hauer

Battery Electric Vehicles (BEVs) face stringent regulatory requirements for battery durability, with Euro 7 and Advanced Clean Cars II (ACCII) regulations mandating State of Health (SoH) thresholds of 70 % after 8 years or 160,000 km and 10 years or 240,000 km, respectively. Current testing protocols rely on homogeneous profiles that inadequately represent real-world usage variability, creating recall and over-engineering risks. This paper addresses this gap by proposing a data-driven framework linking usage behavior patterns with battery aging dynamics to identify representative test profiles.

The framework consists of three integrated steps: (1) predicting battery age and usage at regulatory boundaries through mathematical formulation, (2) estimating SoH using machine learning models including ensemble methods and statistical approaches, and (3) clustering vehicles into groups with homogeneous aging behavior. Unlike existing approaches, it accounts for real-world heterogeneity across diverse usage patterns without assuming predefined load profiles.

Validation used a large-scale Volkswagen AG fleet of roughly 850,000 vehicles across five countries (China, Germany, Italy, Norway, United States). Analysis across regions demonstrates how usage behavior diversity impacts battery aging, validating the need for behavioral pattern-aware methodologies. The framework was complemented by Center for Advanced Life Cycle Engineering (CALCE) battery cycling validation (192 cells, 24 test groups), achieving Homogeneity 0.9157 and Completeness 0.9509.

The modular design enables future enhancements including multi-view clustering and advanced SoH prediction models. This framework provides manufacturers a systematic approach to regulatory compliance while reducing unnecessary testing and mitigating over-engineering risk.

纯电动汽车（bev）在电池耐久性方面面临严格的监管要求，欧7和先进清洁汽车II （ACCII）法规要求，在8年或16万公里和10年或24万公里后，健康状况（SoH）的门槛分别达到70%。当前的测试协议依赖于同构的配置文件，这些配置文件不能充分地代表真实世界的使用可变性，从而产生召回和过度工程的风险。本文提出了一个数据驱动的框架，将使用行为模式与电池老化动态联系起来，以识别具有代表性的测试配置文件，从而解决了这一差距。该框架由三个集成步骤组成：(1)通过数学公式预测监管边界的电池寿命和使用情况；(2)使用包括集成方法和统计方法在内的机器学习模型估计SoH；(3)将具有均匀老化行为的车辆聚类成组。与现有的方法不同，它考虑了不同使用模式的实际异质性，而无需假设预定义的负载配置文件。验证使用了大众汽车公司在五个国家（中国、德国、意大利、挪威和美国）的约85万辆大型车队。跨地区的分析显示了使用行为多样性如何影响电池老化，验证了行为模式感知方法的必要性。对框架进行了CALCE （Advanced Life Cycle Engineering Center）电池循环验证（192个电池，24个试验组），齐性0.9157，完备性0.9509。模块化设计支持未来的增强功能，包括多视图聚类和高级SoH预测模型。该框架为制造商提供了一种系统的方法来遵守法规，同时减少了不必要的测试并减轻了过度工程的风险。

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引用次数: 0

Combining values and images in deep learning models for time series forecasting: An electricity market case study 结合时间序列预测的深度学习模型中的值和图像：电力市场案例研究

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-12-22 DOI: 10.1016/j.egyai.2025.100673

S. Sáez-Bombín , L. Melgar-García , A. Troncoso

Most machine learning algorithms for time series forecasting focus on the real values of the time series, ignoring the information that can be found in its graphical representation. On the other hand, the results obtained by deep learning models in terms of extracting the relationships and patterns hidden in the data motivate the development of hybrid or multimodal models in which both the temporal and graphical information of the time series are used. This work explores the combination of this information in the field of deep learning applied to time series forecasting. Thus, this paper proposes a hybrid deep learning model based on the combination of time series images and their real values for time series forecasting. First, a deep convolutional neural network architecture obtains an initial approximation to the time series predictions from images. Secondly, these predictions along with the actual values of the time series feed a recurrent neural network based on gate recurrent units including attention mechanisms to obtain the final forecasts. Results using three electricity-related datasets have been reported, showing that lower errors are obtained with a shorter training time when considering the graphical representation of the time series together with attention mechanisms in the recurrent networks.

大多数用于时间序列预测的机器学习算法都关注于时间序列的真实值，而忽略了可以在其图形表示中找到的信息。另一方面，深度学习模型在提取隐藏在数据中的关系和模式方面获得的结果激发了混合或多模态模型的发展，其中同时使用了时间序列的时间和图形信息。这项工作探索了将这些信息在深度学习领域的组合应用于时间序列预测。因此，本文提出了一种基于时间序列图像与其真实值相结合的混合深度学习模型，用于时间序列预测。首先，深度卷积神经网络架构从图像中获得时间序列预测的初始近似。其次，这些预测与时间序列的实际值一起馈送一个基于门递归单元（包括注意机制）的递归神经网络，以获得最终的预测。使用三个电相关数据集的结果已经被报道，表明在考虑时间序列的图形表示以及循环网络中的注意机制时，以更短的训练时间获得更低的误差。

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引用次数: 0