Energy and AI最新文献

{"title":"Machine learning for battery quality classification and lifetime prediction using formation data","authors":"Jiayu Zou ,&nbsp;Yingbo Gao ,&nbsp;Moritz H. Frieges ,&nbsp;Martin F. Börner ,&nbsp;Achim Kampker ,&nbsp;Weihan Li","doi":"10.1016/j.egyai.2024.100451","DOIUrl":"10.1016/j.egyai.2024.100451","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100451"},"PeriodicalIF":9.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745679","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}

{"title":"Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia","authors":"Zhihao Xing,&nbsp;Rodolfo S.M. Freitas,&nbsp;Xi Jiang","doi":"10.1016/j.egyai.2024.100454","DOIUrl":"10.1016/j.egyai.2024.100454","url":null,"abstract":"<div><div>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 C₁_<img>N products, thereby decreasing the carbon numbers involved in soot formation. This significantly reduces the concentrations of C₅_<img>C₉ 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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100454"},"PeriodicalIF":9.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745676","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}

{"title":"Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU","authors":"Pascal Riedel ,&nbsp;Kaouther Belkilani ,&nbsp;Manfred Reichert ,&nbsp;Gerd Heilscher ,&nbsp;Reinhold von Schwerin","doi":"10.1016/j.egyai.2024.100452","DOIUrl":"10.1016/j.egyai.2024.100452","url":null,"abstract":"<div><div>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 <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> 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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100452"},"PeriodicalIF":9.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723726","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}

{"title":"Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems","authors":"Julian Ruddick ,&nbsp;Glenn Ceusters ,&nbsp;Gilles Van Kriekinge ,&nbsp;Evgenii Genov ,&nbsp;Cedric De Cauwer ,&nbsp;Thierry Coosemans ,&nbsp;Maarten Messagie","doi":"10.1016/j.egyai.2024.100448","DOIUrl":"10.1016/j.egyai.2024.100448","url":null,"abstract":"<div><div>Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (<span>OptLayerPolicy</span>) and a metaheuristic algorithm generating a decision tree control policy (<span>TreeC</span>), 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, <span>TreeC</span>, 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 <span>TreeC</span> and addressing errors in the model predictive control implementation caused by its reliance on accurate data from various sources. The <span>OptLayerPolicy</span> 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 <span>TreeC</span> 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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100448"},"PeriodicalIF":9.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723725","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}

{"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}

{"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}

{"title":"A self-growth convolution network for thermal and mechanical fault detection with very limited engine data","authors":"Gou Xin ,&nbsp;Zhu Xiaolong ,&nbsp;Wang Xinwei ,&nbsp;Wang Hui ,&nbsp;Zhang Junhong ,&nbsp;Lin Jiewei","doi":"10.1016/j.egyai.2024.100449","DOIUrl":"10.1016/j.egyai.2024.100449","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100449"},"PeriodicalIF":9.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701189","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}

{"title":"Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems: A systematic review","authors":"Ferial ElRobrini ,&nbsp;Syed Muhammad Salman Bukhari ,&nbsp;Muhammad Hamza Zafar ,&nbsp;Nedaa Al-Tawalbeh ,&nbsp;Naureen Akhtar ,&nbsp;Filippo Sanfilippo","doi":"10.1016/j.egyai.2024.100438","DOIUrl":"10.1016/j.egyai.2024.100438","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100438"},"PeriodicalIF":9.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701129","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}

{"title":"Multi-objective decoupling control of thermal management system for PEM fuel cell","authors":"Jun-Hong Chen ,&nbsp;Pu He ,&nbsp;Ze-Hong He ,&nbsp;Jia-Le Song ,&nbsp;Xian-Hao Liu ,&nbsp;Yun-Tian Xiao ,&nbsp;Ming-Yang Wang ,&nbsp;Lu-Zheng Yang ,&nbsp;Yu-Tong Mu ,&nbsp;Wen-Quan Tao","doi":"10.1016/j.egyai.2024.100447","DOIUrl":"10.1016/j.egyai.2024.100447","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100447"},"PeriodicalIF":9.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701130","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}

{"title":"Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps","authors":"Thomas Dengiz,&nbsp;Max Kleinebrahm","doi":"10.1016/j.egyai.2024.100441","DOIUrl":"10.1016/j.egyai.2024.100441","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100441"},"PeriodicalIF":9.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701188","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}