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Machine learning for battery quality classification and lifetime prediction using formation data 使用形成数据进行电池质量分类和寿命预测的机器学习
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-01 DOI: 10.1016/j.egyai.2024.100451
Jiayu Zou , Yingbo Gao , Moritz H. Frieges , Martin F. Börner , Achim Kampker , Weihan Li
Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.
在电池出厂前对电池质量进行准确的分类和寿命预测,将带来经济效益和安全效益。在这里,我们提出了一种数据驱动的方法,通过机器学习来对电池质量进行分类,并在使用前仅使用形成数据来预测电池寿命。我们从原始地层数据中提取了三类特征,考虑了统计方面、差分分析和电化学特征。基于老化机理,分析了提取的100多个特征与电池寿命的相关性。开发了机器学习模型,通过与电池老化高度相关的特征对电池质量进行分类并预测电池寿命。验证结果表明,对于25°C和45°C老化电池,质量分类模型的准确率分别达到89.74%和89.47%。在25°C和45°C老化条件下,寿命预测模型预测电池寿命的平均百分比误差分别为6.50%和5.45%。这项工作强调了来自生产线的电池形成数据在质量分类和寿命预测方面的潜力。
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引用次数: 0
Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia 基于神经网络电位的乙烯和氨热分解机理分子研究
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-01 DOI: 10.1016/j.egyai.2024.100454
Zhihao Xing, Rodolfo S.M. Freitas, Xi Jiang
This study developed neural network potentials (NNPs) specifically tailored for pure ethylene and ethylene-ammonia blended systems for the first time. The NNPs were trained on a dataset generated from density functional theory (DFT) calculations, combining the computational accuracy of DFT with a calculation speed comparable to reactive force field methods. The NNPs are employed in reactive molecular dynamics simulations to explore the thermal decomposition reaction mechanisms of ethylene and ammonia. The simulation results revealed that adding ammonia reduces the activation energy for ethylene decomposition, thereby accelerating ethylene consumption. Furthermore, the addition of ammonia uncovers a new reaction pathway for hydrogen radical consumption, which reduces the occurrence of H-abstraction reactions from ethylene by hydrogen radicals. The inhibition effect of ammonia addition on soot formation mainly acts in two aspects: on the one hand, ammonia decomposition products react with carbon-containing species, ultimately producing C1N products, thereby decreasing the carbon numbers involved in soot formation. This significantly reduces the concentrations of C5C9 molecules and key polycyclic aromatic hydrocarbons (PAHs) precursors like C2H2 and C3H3. On the other hand, ammonia promotes the ring-opening reactions of six-membered carbon rings at high-temperature conditions, thereby reducing the formation of PAHs precursors. The results show that with the addition of ammonia, six-membered carbon rings tend to convert into seven-membered carbon rings at lower temperatures, while at higher temperatures, they are more likely to transform into three- and five-membered carbon rings. These variations in the transformation of six-membered carbon rings may also affect soot formation. The insights gained from understanding these fundamental chemical reaction mechanisms can guide the development of ethylene-ammonia co-firing systems.
该研究首次开发了专门针对纯乙烯和乙烯-氨混合系统的神经网络电位(NNPs)。nnp在密度泛函理论(DFT)计算生成的数据集上进行训练,将DFT的计算精度与与反作用力场方法相当的计算速度相结合。利用NNPs进行反应分子动力学模拟,探讨乙烯和氨的热分解反应机理。模拟结果表明,氨的加入降低了乙烯分解的活化能,从而加快了乙烯的消耗。此外,氨的加入为氢自由基的消耗开辟了一条新的反应途径,减少了氢自由基从乙烯中提取h的反应。氨的加入对烟灰形成的抑制作用主要表现在两个方面:一方面,氨分解产物与含碳物质反应,最终生成C1N产物,从而降低了烟灰形成所涉及的碳数。这大大降低了C5C9分子和关键的多环芳烃(PAHs)前体如C2H2和C3H3的浓度。另一方面,氨在高温条件下促进六元碳环的开环反应,从而减少多环芳烃前体的形成。结果表明,随着氨的加入,六元碳环在较低温度下倾向于转化为七元碳环,而在较高温度下更容易转化为三元和五元碳环。这些六元碳环转变的变化也可能影响烟尘的形成。从了解这些基本化学反应机制中获得的见解可以指导乙烯-氨共烧系统的发展。
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引用次数: 0
Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU 利用 LSTM 和 GRU,通过具有差分隐私的联合学习加强光伏发电上网功率预测
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-23 DOI: 10.1016/j.egyai.2024.100452
Pascal Riedel , Kaouther Belkilani , Manfred Reichert , Gerd Heilscher , Reinhold von Schwerin
Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.
鉴于光伏发电固有的波动性,准确预测太阳能输出和电网馈入对于优化电网运行至关重要。数据驱动方法有助于智能电网中有效的供需管理,但由于天气依赖性和数据隐私限制,预测太阳能发电量仍具有挑战性。传统的深度学习(DL)方法需要访问集中的训练数据,从而导致安全和隐私风险。为了应对这些挑战,本研究利用联合学习(FL)预测低压电网的上网电量。我们提出了一种自下而上、保护隐私的预测方法,利用差分隐私(DP)来增强用户侧能源分析的数据隐私。本研究旨在利用安装在德国南部城市的三个住宅光伏系统的三年电表数据,结合辐照度天气数据来准确预测光伏发电量,从而证明增强型 FL 方法的可行性。在实验中,DL 模型长短期记忆(LSTM)和门控递归单元(GRU)与 DP 进行了联合和集成。因此,联合 LSTM 和 GRU 模型与集中式和本地基线模型进行了滚动 5 倍交叉验证比较,以评估它们各自的性能。通过利用 FedYogi 和 FedAdam 等先进的 FL 算法,我们提出的方法不仅能高精度预测连续能源数据,R2 达到 97.68%,还能遵守严格的隐私标准,为应对智能电网分析挑战提供可扩展的解决方案,从而清楚地表明所提出的方法大有可为,值得进一步研究。
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引用次数: 0
Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems 基于安全强化学习、模型预测控制和决策树的家庭能源管理系统的实际验证
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-22 DOI: 10.1016/j.egyai.2024.100448
Julian Ruddick , Glenn Ceusters , Gilles Van Kriekinge , Evgenii Genov , Cedric De Cauwer , Thierry Coosemans , Maarten Messagie
Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic algorithm generating a decision tree control policy (TreeC), have shown promise. However, their effectiveness has only been demonstrated in computer simulations. This paper presents the real-world validation of these methods, comparing them against model predictive control and simple rule-based control benchmarks. The experiments were conducted on the electrical installation of four reproductions of residential houses, each with its own battery, photovoltaic, and dynamic load system emulating a non-controllable electrical load and a controllable electric vehicle charger. The results show that the simple rules, TreeC, and model predictive control-based methods achieved similar costs, with a difference of only 0.6%. The reinforcement learning based method, still in its training phase, obtained a cost 25.5% higher to the other methods. Additional simulations show that the costs can be further reduced by using a more representative training dataset for TreeC and addressing errors in the model predictive control implementation caused by its reliance on accurate data from various sources. The OptLayerPolicy safety layer allows safe online training of a reinforcement learning agent in the real world, given an accurate constraint function formulation. The proposed safety layer method remains error-prone; nonetheless, it has been found beneficial for all investigated methods. The TreeC method, which does require building a realistic simulation for training, exhibits the safest operational performance, exceeding the grid limit by only 27.1 Wh compared to 593.9 Wh for reinforcement learning.
基于机器学习的能源管理方法,特别是带有安全层的强化学习(OptLayerPolicy)和生成决策树控制策略(TreeC)的元启发式算法,最近取得了长足的进步。然而,它们的有效性只在计算机模拟中得到过验证。本文介绍了这些方法的实际验证情况,并将其与模型预测控制和基于规则的简单控制基准进行了比较。实验是在四栋复制品住宅的电气装置上进行的,每栋住宅都有自己的电池、光伏和动态负载系统,模拟了一个非可控电力负载和一个可控电动汽车充电器。结果表明,基于简单规则、TreeC 和模型预测控制的方法实现了相似的成本,差异仅为 0.6%。基于强化学习的方法仍处于训练阶段,其成本比其他方法高出 25.5%。其他模拟结果表明,通过为 TreeC 使用更具代表性的训练数据集,并解决模型预测控制实施过程中因依赖各种来源的准确数据而产生的错误,可以进一步降低成本。OptLayerPolicy 安全层允许在现实世界中对强化学习代理进行安全的在线训练,并给出准确的约束函数表述。所提出的安全层方法仍然容易出错,但它对所有研究方法都有益处。TreeC 方法需要建立一个真实的模拟来进行训练,它的运行性能最安全,仅超出电网限制 27.1 Wh,而强化学习方法则超出 593.9 Wh。
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引用次数: 0
Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning 通过本地能源市场和深度强化学习分散协调分布式能源资源
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-19 DOI: 10.1016/j.egyai.2024.100446
Daniel C. May , Matthew Taylor , Petr Musilek
As the energy landscape evolves towards sustainability, the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid. One significant aspect of this issue is the notable increase in net load variability at the grid edge.
Transactive energy, implemented through local energy markets, has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized, indirect demand response on a community level. Model-free control approaches, such as deep reinforcement learning (DRL), show promise for the decentralized automation of participation within this context. Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability.
This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven, autonomous local energy market (ALEX). In this setting, agents do not share information and only prioritize individual bill optimization. The study unveils a clear correlation between bill reduction and reduced net load variability. The impact on net load variability is assessed over various time horizons using metrics such as ramping rate, daily and monthly load factor, as well as daily average and total peak export and import on an open-source dataset.
To examine the performance of the proposed DRL method, its agents are benchmarked against a near-optimal dynamic programming method, using a no-control scenario as the baseline. The dynamic programming benchmark reduces average daily import, export, and peak demand by 22.05%, 83.92%, and 24.09%, respectively. The RL agents demonstrate comparable or superior performance, with improvements of 21.93%, 84.46%, and 27.02% on these metrics. This demonstrates that DRL can be effectively employed for such tasks, as they are inherently scalable with near-optimal performance in decentralized grid management.
随着能源环境向可持续方向发展,分布式能源的加速整合对电网的可操作性和可靠性提出了挑战。这一问题的一个重要方面是电网边缘的净负荷可变性显著增加。通过本地能源市场实施的交互式能源最近引起了人们的关注,它是一种有前途的解决方案,可以在社区层面以分散、间接需求响应的形式应对电网挑战。无模型控制方法,如深度强化学习(DRL),显示了在此背景下分散式自动化参与的前景。本研究通过训练一组深度强化学习代理,使最终用户自动参与经济驱动的自主本地能源市场(ALEX),弥补了这一空白。在这种情况下,代理不会共享信息,只会优先优化个人账单。研究揭示了账单减少与净负荷变化减少之间的明显相关性。在不同的时间跨度内,使用斜率、日和月负荷率以及开放源数据集上的日均和总峰值进出口等指标评估了对净负荷变异性的影响。为考察所建议的 DRL 方法的性能,以无控制情景为基准,将其代理与接近最优的动态编程方法进行了比较。动态编程基准将日均进口、出口和峰值需求分别降低了 22.05%、83.92% 和 24.09%。RL 代理在这些指标上分别提高了 21.93%、84.46% 和 27.02%,性能相当或更优。这表明 DRL 可以有效地用于此类任务,因为它们本身具有可扩展性,在分散式电网管理中具有接近最佳的性能。
{"title":"Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning","authors":"Daniel C. May ,&nbsp;Matthew Taylor ,&nbsp;Petr Musilek","doi":"10.1016/j.egyai.2024.100446","DOIUrl":"10.1016/j.egyai.2024.100446","url":null,"abstract":"<div><div>As the energy landscape evolves towards sustainability, the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid. One significant aspect of this issue is the notable increase in net load variability at the grid edge.</div><div>Transactive energy, implemented through local energy markets, has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized, indirect demand response on a community level. Model-free control approaches, such as deep reinforcement learning (DRL), show promise for the decentralized automation of participation within this context. Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability.</div><div>This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven, autonomous local energy market (ALEX). In this setting, agents do not share information and only prioritize individual bill optimization. The study unveils a clear correlation between bill reduction and reduced net load variability. The impact on net load variability is assessed over various time horizons using metrics such as ramping rate, daily and monthly load factor, as well as daily average and total peak export and import on an open-source dataset.</div><div>To examine the performance of the proposed DRL method, its agents are benchmarked against a near-optimal dynamic programming method, using a no-control scenario as the baseline. The dynamic programming benchmark reduces average daily import, export, and peak demand by 22.05%, 83.92%, and 24.09%, respectively. The RL agents demonstrate comparable or superior performance, with improvements of 21.93%, 84.46%, and 27.02% on these metrics. This demonstrates that DRL can be effectively employed for such tasks, as they are inherently scalable with near-optimal performance in decentralized grid management.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100446"},"PeriodicalIF":9.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Photovoltaic power forecasting: A Transformer based framework 光伏发电功率预测:基于变压器的框架
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-19 DOI: 10.1016/j.egyai.2024.100444
Gabriele Piantadosi , Sofia Dutto , Antonio Galli , Saverio De Vito , Carlo Sansone , Girolamo Di Francia
The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration.
准确预测光伏(PV)发电量是优化太阳能并入电网和最大化能源市场可再生能源交易利益的关键任务。本文通过比较不同的机器学习技术和不同气象预报提供商的影响,对文献进行了系统和定量分析。该方法由一个辐照度模型和一个气象预报提供商组成;这一组合消除了本地辐照度测量的限制。通过使用 OpenMeteo 数据对变压器神经网络架构进行训练和测试,该架构的性能优于其他组合,其 MAE 为 1.22 kW(0.95%),MAPE 为 2.21%。我们的研究结果表明,采用综合方法,整合当地气象数据、模拟辐照度和光伏电站配置数据,可以显著提高光伏发电功率预测的准确性,从而促进更有效的技术和经济整合。
{"title":"Photovoltaic power forecasting: A Transformer based framework","authors":"Gabriele Piantadosi ,&nbsp;Sofia Dutto ,&nbsp;Antonio Galli ,&nbsp;Saverio De Vito ,&nbsp;Carlo Sansone ,&nbsp;Girolamo Di Francia","doi":"10.1016/j.egyai.2024.100444","DOIUrl":"10.1016/j.egyai.2024.100444","url":null,"abstract":"<div><div>The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100444"},"PeriodicalIF":9.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A self-growth convolution network for thermal and mechanical fault detection with very limited engine data 利用非常有限的发动机数据进行热故障和机械故障检测的自生长卷积网络
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-17 DOI: 10.1016/j.egyai.2024.100449
Gou Xin , Zhu Xiaolong , Wang Xinwei , Wang Hui , Zhang Junhong , Lin Jiewei
Severe faults occur infrequently but are critical for the prognostics and health management (PHM) of power machinery. Due to the scarcity of fault data, diagnostic models are always facing a very limited data problem. Basic convolutional neural networks require a large number of samples to train, and widely used data augmentation methods are influenced by data quality, which can exacerbate overfitting. To address this issue, a self-growth convolution network (SGNet) is proposed to make the deep learning process a self-growing scheme in both depth and width dimensions. The direct similarity measurement is utilized to supervise the depth-growth in the layer-by-layer training process. The feature redundancy metric is employed to control the width expansion. The self-growth scheme is proposed to disrupt the coadaptation between layers and that between kernels in order to mitigate the overfitting issue of small-sample cases. The SGNet is verified and implemented in the PHM of a heavy-duty diesel engine. It exhibits remarkable diagnostic capabilities in extremely sample-limited scenarios. With only three training samples per faulty type, the recognition rates of SGNet for the misfire fault and the gear tooth fracture fault are 88.44% and 98.11%, respectively. Further, the feature contrast, the information transmission, the noise resistance, and the frequency domain activation heat of SGNet are discussed by the ablation experiment in detail. The results indicate a novel path to solve the data-limitation problem in the PHM of important power machinery.
严重故障不常发生,但对电力机械的预报和健康管理(PHM)至关重要。由于故障数据稀缺,诊断模型始终面临着数据非常有限的问题。基本的卷积神经网络需要大量样本进行训练,而广泛使用的数据增强方法会受到数据质量的影响,从而加剧过拟合。为了解决这个问题,我们提出了一种自增长卷积网络(SGNet),使深度学习过程在深度和宽度维度上都成为一种自增长方案。在逐层训练过程中,利用直接相似度测量来监督深度增长。利用特征冗余度量来控制宽度扩展。为了减少小样本情况下的过拟合问题,提出了自生长方案来破坏层与层之间以及内核与内核之间的协同适应。SGNet 在重型柴油发动机的 PHM 中得到了验证和实施。它在样本极其有限的情况下表现出卓越的诊断能力。在每个故障类型只有三个训练样本的情况下,SGNet 对失火故障和轮齿断裂故障的识别率分别为 88.44% 和 98.11%。此外,还通过烧蚀实验详细讨论了 SGNet 的特征对比、信息传递、抗噪能力和频域激活热。这些结果为解决重要动力机械 PHM 中的数据限制问题指明了一条新路。
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引用次数: 0
Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems: A systematic review 基于联合学习和非联合学习的光伏/风能系统功率预测:系统综述
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-17 DOI: 10.1016/j.egyai.2024.100438
Ferial ElRobrini , Syed Muhammad Salman Bukhari , Muhammad Hamza Zafar , Nedaa Al-Tawalbeh , Naureen Akhtar , Filippo Sanfilippo
Renewable energy sources, particularly photovoltaic and wind power, are essential in meeting global energy demands while minimising environmental impact. Accurate photovoltaic (PV) and wind power (WP) forecasting is crucial for effective grid management and sustainable energy integration. However, traditional forecasting methods encounter challenges such as data privacy, centralised processing, and data sharing, particularly with dispersed data sources. This review paper thoroughly examines the necessity of forecasting models, methodologies, and data integrity, with a keen eye on the evolving landscape of Federated Learning (FL) in PV and WP forecasting. Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation, the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security. A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals, followed by in-depth discussions on specific non-Federated Learning approaches for each power source. The paper subsequently introduces FL and its variants, including Horizontal, Vertical, Transfer, Cross-Device, and Cross-Silo FL, highlighting the crucial role of encryption mechanisms and addressing associated challenges. Furthermore, drawing on extensive investigations of numerous pertinent articles, the paper outlines the innovative horizon of FL-based PV and wind power forecasting, offering insights into FL-based methodologies and concluding with observations drawn from this frontier.
This review synthesises critical knowledge about PV and WP forecasting, leveraging the emerging paradigm of FL. Ultimately, this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.
可再生能源,尤其是光伏发电和风力发电,对于满足全球能源需求,同时最大限度地减少对环境的影响至关重要。准确的光伏(PV)和风能(WP)预测对于有效的电网管理和可持续能源整合至关重要。然而,传统的预测方法面临着数据隐私、集中处理和数据共享等挑战,特别是在数据源分散的情况下。本综述论文深入探讨了预测模型、方法和数据完整性的必要性,并对光伏和可再生能源预测中不断发展的 "联合学习"(FL)进行了深入研究。论文首先介绍了预测模型在优化可再生能源资源利用方面的重要意义,然后深入探讨了各种预测技术,并强调了数据完整性和安全性的关键需求。在高质量期刊的基础上,对基于非联合学习的光伏和可再生能源预测进行了全面概述,随后对每种能源的具体非联合学习方法进行了深入讨论。论文随后介绍了 FL 及其变体,包括水平 FL、垂直 FL、转移 FL、跨设备 FL 和跨ilo FL,强调了加密机制的关键作用,并探讨了相关挑战。此外,在对大量相关文章进行广泛调查的基础上,本文概述了基于 FL 的光伏和风电预测的创新前景,对基于 FL 的方法提出了见解,并从这一前沿领域得出了结论。最终,这项工作将有助于推动可再生能源的整合,并可持续、安全地优化电网管理。
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引用次数: 0
Multi-objective decoupling control of thermal management system for PEM fuel cell PEM 燃料电池热管理系统的多目标解耦控制
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-15 DOI: 10.1016/j.egyai.2024.100447
Jun-Hong Chen , Pu He , Ze-Hong He , Jia-Le Song , Xian-Hao Liu , Yun-Tian Xiao , Ming-Yang Wang , Lu-Zheng Yang , Yu-Tong Mu , Wen-Quan Tao
Operating temperature is an important factor that affects the efficiency, durability, and safety of proton exchange membrane fuel cells (PEMFC). Thus, a thermal management system is necessary for controlling the appropriate temperature. In this paper, a novel thermal management system based on two-stage utilization of cooling air is first established, whose core characteristic is utilizing the temperature difference between the cooling air leaving the main radiator and the auxiliary radiator. The novel thermal management system can reduce the parasitic power of the fan by 59.27 % and improve the temperature control effect to a certain extent. The traditional feedforward decoupling control based on system identification is first adopted to control the temperature and surpasses dual-PID on all the 5 indexes, which are Integral Absolute Error Criterion (IAE) of temperature difference, IAE of inlet coolant temperature, parasitic power of fan, average overshoot of temperature difference and average overshoot of inlet coolant temperature. The multi-objective decoupling control based on multi-objective optimization is then proposed to further improve the temperature control effect on the basis of traditional feedforward decoupling control. The above 5 indexes are chosen as the optimization objectives, the decoupling coefficients are chosen as the decision variables, and the Pareto set is obtained by NSGAⅡ and NSGAⅢ. The results show that the proposed multi-objective decoupling control has the main advantages as follows: (1) It can provide comprehensive optimization options for different design preferences; (2) It can significantly optimize a certain objective while other objectives are not too extreme; (3) It has the ability to surpass traditional feedforward decoupling control on all the 5 indexes; (4) It does not rely on the system identification.
工作温度是影响质子交换膜燃料电池(PEMFC)效率、耐用性和安全性的重要因素。因此,需要一个热管理系统来控制适当的温度。本文首先建立了一种基于冷却空气两级利用的新型热管理系统,其核心特点是利用离开主散热器的冷却空气与辅助散热器之间的温差。新型热管理系统可降低风扇的寄生功率 59.27%,并在一定程度上改善了温度控制效果。首先采用基于系统辨识的传统前馈解耦控制来控制温度,并在温差积分绝对误差准则(IAE)、入口冷却剂温度积分绝对误差准则(IAE)、风扇寄生功率、温差平均过冲和入口冷却剂温度平均过冲这 5 项指标上全面超越了双 PID。然后提出基于多目标优化的多目标解耦控制,在传统前馈解耦控制的基础上进一步提高温度控制效果。选取上述 5 个指标作为优化目标,选取解耦系数作为决策变量,利用 NSGAⅡ 和 NSGAⅢ求出帕累托集。结果表明,所提出的多目标解耦控制具有以下主要优点:(1)能针对不同的设计偏好提供全面的优化方案;(2)能显著优化某一目标,而其他目标不会过于极端;(3)在所有 5 项指标上都具有超越传统前馈解耦控制的能力;(4)不依赖系统识别。
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引用次数: 0
Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps 利用人工神经网络进行模仿学习,采用启发式热泵控制方法进行需求响应
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-13 DOI: 10.1016/j.egyai.2024.100441
Thomas Dengiz, Max Kleinebrahm
The flexibility of electrical heating devices can help address the issues arising from the growing presence of unpredictable renewable energy sources in the energy system. In particular, heat pumps offer an effective solution by employing smart control methods that adjust the heat pump’s power output in reaction to demand response signals. This paper combines imitation learning based on an artificial neural network with an intelligent control approach for heat pumps. We train the model using the output data of an optimization problem to determine the optimal operation schedule of a heat pump. The objective is to minimize the electricity cost with a time-variable electricity tariff while keeping the building temperature within acceptable boundaries. We evaluate our developed novel method, PSC-ANN, on various multi-family buildings with differing insulation levels that utilize an underfloor heating system as thermal storage. The results show that PSC-ANN outperforms a positively evaluated intelligent control approach from the literature and a conventional control approach. Further, our experiments reveal that a trained imitation learning model for a specific building is also applicable to other similar buildings without the need to train it again with new data. Our developed approach also reduces the execution time compared to optimally solving the corresponding optimization problem. PSC-ANN can be integrated into multiple buildings, enabling them to better utilize renewable energy sources by adjusting their electricity consumption in response to volatile external signals.
电加热设备的灵活性有助于解决能源系统中不可预测的可再生能源日益增多所带来的问题。尤其是热泵,它采用智能控制方法,可根据需求响应信号调整热泵的功率输出,从而提供有效的解决方案。本文将基于人工神经网络的模仿学习与热泵智能控制方法相结合。我们利用优化问题的输出数据来训练模型,以确定热泵的最佳运行时间表。其目标是在电费随时间变化的情况下最大限度地降低电费,同时将建筑物的温度控制在可接受的范围内。我们开发的新方法 PSC-ANN 在不同隔热水平的多户建筑上进行了评估,这些建筑使用地暖系统作为蓄热装置。结果表明,PSC-ANN 优于文献中得到积极评价的智能控制方法和传统控制方法。此外,我们的实验表明,针对特定建筑物训练的模仿学习模型也适用于其他类似建筑物,而无需再次使用新数据进行训练。与优化解决相应的优化问题相比,我们开发的方法还能缩短执行时间。PSC-ANN 可以集成到多栋建筑中,使它们能够根据不稳定的外部信号调整耗电量,从而更好地利用可再生能源。
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Energy and AI
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