Energy and AI最新文献

{"title":"Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes","authors":"Bo Wu ,&nbsp;Xiuli Wang ,&nbsp;Bangyan Wang ,&nbsp;Yaohong Xie ,&nbsp;Shixiong Qi ,&nbsp;Wenduo Sun ,&nbsp;Qihang Huang ,&nbsp;Xiang Ma","doi":"10.1016/j.egyai.2025.100513","DOIUrl":"10.1016/j.egyai.2025.100513","url":null,"abstract":"<div><div>This paper proposes an innovative framework for medium-term wind power forecasting, employing a robust, multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons. The framework consists of three key components: an internal–external learning process, a vertical–horizontal learning process, and a residual-based robust forecasting method. The internal–external process combines Variational Mode Decomposition with a stacked N-BEATS model, achieving stable and accurate forecasts across nearly 200 time steps. The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit, enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data. We compared our proposed method with nine classical and state-of-the-art techniques and found that it delivers higher accuracy in medium-term prediction, extending to nearly 200 steps. The residual-based method addresses uncertainties by generating 95% confidence intervals, enhancing the model’s robustness in practical applications. By simulating real-world conditions, this framework provides reliable medium-term forecasts, making it an effective tool for renewable energy system dispatch and precise error control.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100513"},"PeriodicalIF":9.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced day-ahead scheduling of HVAC demand response control using novel strategy of Q-learning, model predictive control, and input convex neural networks","authors":"Rahman Heidarykiany,&nbsp;Cristinel Ababei","doi":"10.1016/j.egyai.2025.100509","DOIUrl":"10.1016/j.egyai.2025.100509","url":null,"abstract":"<div><div>In this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, minimize user discomfort, and honor in a best-effort way the recommendations from utility/aggregator, which in turn has impact on the overall well being of the distribution network controlled by the aggregator. The proposed Q-Learning optimization algorithm, based on epsilon-model predictive control (<span><math><mi>ϵ</mi></math></span>-MPC), can be implemented as a control agent that is executed by the smart house energy management (SHEM) system that we assume exists in the smart home, which can interact with the energy provider of the distribution network, i.e., utility/aggregator, via the smart meter. The output generated by the proposed control agent represents day-ahead local DR signals in the form of temperature setpoints for the HVAC system that are found by the optimization process to lead to desired trade-offs between electricity cost and user discomfort. The proposed algorithm can be used in smart homes with passive HVAC controllers, which solely react to end-user setpoints, to transform them into smart homes with active HVAC controllers. Such systems not only respond to the preferences of the end-user but also incorporate an external control signal provided by the utility or aggregator. Simulation experiments conducted with a custom simulation tool demonstrate that the proposed optimization framework can offer significant benefits. It achieves 87% higher success rate in optimizing setpoints in the desired range, thereby resulting in up to 15% energy savings and zero temperature discomfort.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100509"},"PeriodicalIF":9.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data","authors":"Mazin Al-Mahrouqi ,&nbsp;Abdollah Shafieezadeh ,&nbsp;Jieun Hur ,&nbsp;Jae-Wook Jung ,&nbsp;Jeong-Gon Ha ,&nbsp;Daegi Hahm","doi":"10.1016/j.egyai.2025.100511","DOIUrl":"10.1016/j.egyai.2025.100511","url":null,"abstract":"<div><div>Weather-induced outages pose a significant threat to power grid reliability, with transmission systems particularly vulnerable to environmental stressors. Despite numerous tools developed to address this issue, the persistent challenge of weather-related interruptions highlights the need for an accurate fragility model for transmission line interruptions. This paper proposes a novel data-driven approach to model wind-induced transmission line fragility, addressing critical gaps in current methodologies. Our model integrates a novel synthetic data generation approach that creates highly informative synthetic data points, enhancing the representation of rare events. Additionally, we develop an advanced active learning framework that efficiently selects the most relevant instances from large, imbalanced datasets for model training. We further enhance model interpretability through comprehensive sensitivity analysis using SHAP (SHapley Additive exPlanations) values. Results on unseen testing data show significant improvement compared to conventional methods, achieving a 5% improvement in accuracy (from 0.89 to 0.94) in predicting wind-induced transmission line interruptions. Notably, it shows a 16% accuracy improvement (from 0.64 to 0.80) when applied to highly uncertain cases, highlighting its capabilities in high-uncertainty situations. Sensitivity analysis reveals wind gust and mean sea level pressure as the most critical factors influencing interruptions, while also uncovering complex temperature effects where, in a subset of situations, temperature has a significant impact on the interruption probability of lines. This advanced fragility model can offer valuable insights for both real-time dispatch decisions and long-term risk-informed planning, contributing to enhanced power grid resilience in the face of increasing weather-related challenges.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100511"},"PeriodicalIF":9.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel weight-based ensemble method for emerging energy players: an application to electric vehicle load prediction","authors":"Joan Tomàs Villalonga Palou ,&nbsp;Javier Serrano González ,&nbsp;Jesús Manuel Riquelme Santos ,&nbsp;Juan Manuel Roldán Fernández","doi":"10.1016/j.egyai.2025.100510","DOIUrl":"10.1016/j.egyai.2025.100510","url":null,"abstract":"<div><div>The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations. It is common for these agents to have different sources of forecasts (from specialized consultants or meteorological services, among others).</div><div>The proposed approach aims to obtain more accurate predictions by optimally combining a set of predictions obtained by different techniques. In this way it is possible to obtain a resulting prediction that improves the error and uncertainty associated with each of the individual forecasts. The objective is achieved by the analytical minimization of the errors obtained by each of the individual predictors. This allows to obtain dynamically the optimized weights assigned to each of the algorithms so that the combination outperforms the individual behaviour of each of them. The proposed ensemble approach has been successfully tested on a real time series of electric vehicle charging.</div><div>Likewise, the results obtained have been compared exhaustively with other ensemble techniques consolidated in the literature based on different methods, including dynamic ensembles as machine learning approaches. The results obtained show an appreciable improvement of the errors obtained in the predictions using the proposed techniques.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100510"},"PeriodicalIF":9.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection","authors":"Sherko Salehpour ,&nbsp;Aref Eskandari ,&nbsp;Amir Nedaei ,&nbsp;Mohammadreza Aghaei","doi":"10.1016/j.egyai.2025.100512","DOIUrl":"10.1016/j.egyai.2025.100512","url":null,"abstract":"<div><div>Early detection of faults in photovoltaic (PV) arrays has always been the center of attention to maintain system efficiency and reliability. However, conventional protection devices have shown various deficiencies, especially when dealing with less severe faults. Hence, artificial intelligence (AI) models, specifically machine learning (ML) have complemented the conventional protection devices to compensate for their limitations. Despite their obvious advantages, ML models have also shown several shortcomings, such as (i) most of them relied on a massive amount of training dataset to provide a fairly satisfying accuracy, (ii) not many of them were able to detect less severe faults, and (iii) those which were able to detect less severe faults could not produce high accuracy. To this end, the present paper proposes a state-of-the-art deep reinforcement learning (DRL) model based on deep Q-network (DQN) to overcome all the existing challenges in previous ML models for PV arrays fault detection and diagnosis. The model carries out a two-stage process employing two DQN-based agents which is not only able to accurately detect and classify (first stage) various faults in PV arrays, but it is also able to assess the severity of line-to-line (LL) and line-to-ground (LG) faults (second stage) in PV arrays using only a small training dataset. The training and testing datasets include several voltage and current values on PV array current-voltage (I-V) characteristic curve which is extracted using the variable load technique for PV array I-V curve extraction. The model has been implemented on an experimental standalone PV array and the results show outstanding accuracies of 98.61 % and 100 % when it is verified by testing datasets in the first and the second stage, respectively.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100512"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial energy forecasting using dynamic attention neural networks","authors":"Nicholas Majeske ,&nbsp;Shreyas Sunil Vaidya ,&nbsp;Ryan Roy ,&nbsp;Abdul Rehman ,&nbsp;Hamed Sohrabpoor ,&nbsp;Tyson Miller ,&nbsp;Wenhui Li ,&nbsp;C.R. Fiddyment ,&nbsp;Alexander Gumennik ,&nbsp;Raj Acharya ,&nbsp;Vikram Jadhao ,&nbsp;Prateek Sharma ,&nbsp;Ariful Azad","doi":"10.1016/j.egyai.2025.100504","DOIUrl":"10.1016/j.egyai.2025.100504","url":null,"abstract":"<div><div>We develop a comprehensive framework for storing, analyzing, forecasting, and visualizing industrial energy systems consisting of multiple devices and sensors. Our framework models complex energy systems as a dynamic knowledge graph, utilizes a novel machine learning (ML) model for energy forecasting, and visualizes continuous predictions through an interactive dashboard. At the core of this framework is A-RNN, a simple yet efficient model that uses dynamic attention mechanisms for automated feature selection. We validate the model using datasets from two manufacturers and one university testbed containing hundreds of sensors. Our results show that A-RNN forecasts energy usage within 5% of observed values. These enhanced predictions are as much as 50% more accurate than those produced by standard RNN models that rely on individual features and devices. Additionally, A-RNN identifies key features that impact forecasting accuracy, providing interpretability for model forecasts. Our analytics platform is computationally and memory efficient, making it suitable for deployment on edge devices and in manufacturing plants.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100504"},"PeriodicalIF":9.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated proximal policy optimization with action masking: Application in collective heating systems","authors":"Sara Ghane ,&nbsp;Stef Jacobs ,&nbsp;Furkan Elmaz ,&nbsp;Thomas Huybrechts ,&nbsp;Ivan Verhaert ,&nbsp;Siegfried Mercelis","doi":"10.1016/j.egyai.2025.100506","DOIUrl":"10.1016/j.egyai.2025.100506","url":null,"abstract":"<div><div>This paper introduces a novel privacy-aware Federated Proximal Policy Optimization (FPPO) method combined with action masking. As a Federated Reinforcement Learning (FRL) approach, the proposed method is used for optimizing the reloading of Domestic Hot Water (DHW) storage tanks, with a focus on energy savings and DHW thermal comfort in collective heating systems. The proposed approach combines FedProx as the Federated Learning (FL) method and Proximal Policy Optimization (PPO) as the Deep Reinforcement Learning (DRL) technique to address the challenges of distributed control while ensuring data privacy. Key contributions include: (1) employing action masking to guarantee compliance with comfort level, (2) designing a global reward function to align agents actions toward collective energy savings, (3) implementing a privacy-aware design where only model parameters are shared with a global aggregator, avoiding raw data transmission, and (4) optimizing PPO’s loss function for improved performance.</div><div>PPO was benchmarked using a common FL method (FedAvg) alongside two other DRL methods, where PPO outperformed both in scalability and energy savings, especially in larger systems. Then, PPO-based FRL was refined into FPPO by integrating a proximal term with coefficient <span><math><mi>μ</mi></math></span> into the loss function to enhance the performance. Experiments were conducted with both fixed and dynamically adjusted <span><math><mi>μ</mi></math></span>, with the latter demonstrating better energy savings and comfort. Results show that FPPO achieves up to 10.08% energy savings while maintaining DHW discomfort below 8.72% in systems with at least 20 dwellings. These findings highlight FPPO as a scalable, privacy-aware, and energy-efficient solution for distributed control in collective heating systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100506"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738722","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":"AI-driven digital circular economy with material and energy sustainability for industry 4.0","authors":"Yuekuan Zhou","doi":"10.1016/j.egyai.2025.100508","DOIUrl":"10.1016/j.egyai.2025.100508","url":null,"abstract":"<div><div>Circular Economy and Sustainability in Industry 4.0 Era are promoters for carbon neutrality transformation, while their interconnected nexus and specific roles in low-carbon transition have not been clearly revealed. Furthermore, an integrated circular economy framework with buildings, PVs, battery and EVs, with overlaps in renewable-driven operational stages has not been considered in lifecycle decarbonization. This study is to reveal the nexus between Circular Economy and Sustainability in Industry 4.0 Era. Operational modes and mechanism of Circular Economy in PVs, buildings, electric vehicle industries and batteries are specifically analysed, together with energy and carbon flow analysis and optimization. Roles of Circular Economy in Sustainability have been provided, through an integrated circular economy framework with buildings, PVs, battery and electric vehicles (EVs), considering the overlap in renewable-energy driven operational stages in lifecycle decarbonization. Last but not the least, waste material recovery and waste-to-energy conversion have been analysed within the close-in-loop cycle for sustainability transition. Advanced digital technology in future Circular Economy is formulated with data-driven circular economy and internet-of-thing (IoT)-based waste-to-energy framework. Research results indicate that circular economy plays significant roles in sustainability, including cascade reuse paradigm, reverse supply chain with the recovery of end-of-life batteries, EV lifetime extension via repair and reuse, low carbon with refurbishing and remanufacturing, and less new primary materials via recycling materials, waste material recovery and waste-to-energy conversion. The renewable-driven battery-building-transportation-waste circular economy chain with the cross overlap in clean energy utilization can partially offset carbon emissions during the raw materials mining, manufacturing and recycling stages. This study can promote the waste to energy and advanced machine learning techniques with Circular Economy and Sustainability in Industry 4.0 Era.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100508"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768708","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":"Data-driven analysis of hysteresis and stability in perovskite solar cells using machine learning","authors":"Sharun Parayil Shaji ,&nbsp;Wolfgang Tress","doi":"10.1016/j.egyai.2025.100503","DOIUrl":"10.1016/j.egyai.2025.100503","url":null,"abstract":"<div><div>Perovskite solar cells are promising photovoltaic devices because of the high defect tolerance and desirable optoelectronic properties of the metal-halide perovskite absorber materials. The transition from lab to industry is still an open problem, which is mainly limited by upscaling and stability. In this study we try to use tools from data science namely Pearson correlation and random forest regressor applied to the data from the open-source platform “Perovskite Database” to understand the correlations with material choice, fabrication techniques, and current-voltage key features to the stability and hysteresis index. We find that the cell stack as a whole plays a crucial role in hysteresis and not a single layer. We statistically confirm that p-i-n and higher-efficient solar cells generally show reduced hysteresis. We identify certain cross correlations, which would lead to wrong conclusions e.g. claiming an open-circuit voltage not correlated with the hysteresis or some apparent correlations with material parameters, which originate from the historical development. Regarding stability, we are not able to obtain good performance from the machine learning model. Reasons are non-standardized measurements and lack of sufficient data.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100503"},"PeriodicalIF":9.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724741","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 Reinforcement Learning for decentralized peer-to-peer energy trading","authors":"Zhian Ye ,&nbsp;Dawei Qiu ,&nbsp;Shuangqi Li ,&nbsp;Zhong Fan ,&nbsp;Goran Strbac","doi":"10.1016/j.egyai.2025.100500","DOIUrl":"10.1016/j.egyai.2025.100500","url":null,"abstract":"<div><div>The rapid development of distributed energy resources has led to an increasing number of prosumers enhancing their energy utilization, thereby raising the demands on energy management technologies. As a result, the development of future smart grids is becoming increasingly important, with a particular emphasis on integrating demand-side flexibility into electricity market. To facilitate distributed interaction among prosumers, the double-side auction market enables peer-to-peer (P2P) energy trading, maximizing the social welfare within the dynamic local electricity market. In this setup, prosumers can set their own bidding prices and optimize their operations and trading strategies. However, trading in double-side auction market faces limitations due to the complexity of the market clearing algorithm and the difficulty of predicting other participants’ bidding behaviors. To address these challenges, this paper models the P2P energy trading problem in the double-side auction market as a multi-agent reinforcement learning (MARL) task. The concept of federated learning is introduced to enhance scalability among market participants while protecting the private information of individual prosumers. Additionally, the parameter-sharing framework is proposed to accelerate the learning process. To further improve the stability of MARL training, the global information of P2P energy trading price is integrated into the critic network. The proposed federated MARL algorithm is evaluated using a real-world open-source dataset from an European residential community of 250 households with a 15-minute resolution. The evaluation assesses both the training performance of the algorithm as well as the economic and operational benefits of the P2P energy trading market compared to a traditional electricity retail market.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100500"},"PeriodicalIF":9.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687633","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}