Energy and AI最新文献

{"title":"Denseception network method for transient stability prediction of power systems","authors":"Dan Liu ,&nbsp;Xia Chen ,&nbsp;Kezheng Jiang ,&nbsp;Wei Ge ,&nbsp;Linfei Yin","doi":"10.1016/j.egyai.2025.100550","DOIUrl":"10.1016/j.egyai.2025.100550","url":null,"abstract":"<div><div>In the context of accelerated global energy transition, the high proportion of renewable energy grid connections and the proliferation of power control devices have significantly increased the tangled and haziness of the electromechanical transients in power grids, and the transient stability prediction has become an international forefront problem in the construction of smart grid security and defense system. However, existing methods face triple limitations: traditional physical models rely on ideal assumptions and are computationally inefficient; shallow data-driven models have insufficient feature extraction capabilities; and existing deep learning methods have poor generalization and lack interpretability. To manage the issues highlighted above, this study proposes a deep learning-based Denseception architecture and its accompanying data modeling method, which achieves a breakthrough in high-precision continuous numerical prediction of transient stability indicator (TSI) with engineering practicality. The heterogeneous multi-scale feature fusion network is constructed by integrating the DenseNet dense cross-layer connectivity, Xception deep separable convolution, and the dynamic weighting mechanism of the fully connected layers, which significantly improves the efficiency of the cross-scale dynamic feature extraction; and the three-channel two-dimensional spatial-temporal feature reconstruction method is innovatively designed, which reconstructs the temporal data of the whole fault process into an image-like structure, and combines with the adversarial training strategy to enhance the cross-topology generalization capability. The experiment reveals that the TSI prediction error of the Denseception model is prominently lower than that of the mainstream deep learning model in the IEEE 39–10 and 145–50 systems, which is the best performance. This study overcomes the contradiction between speed, accuracy, and generalizability of traditional methods, provides a full chain solution for the dynamic security defense of a high percentage new energy power grids, and provides a critical time window for emergency control.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100550"},"PeriodicalIF":9.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548851","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":"Accelerating sodium-ion electrode material development through AI-driven optimization and predictive modeling","authors":"Sara Alzaabi ,&nbsp;Ali Elkamel ,&nbsp;Georgios N. Karanikolos ,&nbsp;Ali Alhammadi","doi":"10.1016/j.egyai.2025.100537","DOIUrl":"10.1016/j.egyai.2025.100537","url":null,"abstract":"<div><div>Sodium-ion batteries (SIBs) are gaining traction as a cost-effective and sustainable alternative to lithium-ion batteries for large-scale energy storage, due to sodium’s abundance, low cost, and safety advantages. However, the discovery of high-performance electrode materials for SIBs remains a significant challenge because of the complex interactions between compositional and structural features that govern key properties such as specific capacity, average voltage, and volume change. In this study, we present an artificial intelligence (AI)-driven framework that integrates machine learning and multi-objective optimization to accelerate the design of sodium-ion battery electrodes. Four predictive models, namely Decision Tree, Random Forest, Support Vector Machine (SVM), and Deep Neural Network (DNN), were trained on a feature-rich dataset derived from high-throughput computational databases. The DNN model achieved the highest predictive accuracy, with R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> values up to 0.97 and mean absolute errors (MAE) below 0.11 for the target properties. To support material selection, the DNN was coupled with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify Pareto-optimal materials that maximize specific capacity while minimizing volume expansion. The resulting candidates exhibit balanced electrochemical performance and potential for practical SIB applications. This study demonstrates the power of combining deep learning and optimization to guide the discovery of next-generation energy storage materials with high efficiency and reduced experimental overhead.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100537"},"PeriodicalIF":9.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556996","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":"Multi-temporal PV power prediction using long short-term memory and wavelet packet decomposition","authors":"Amirhasan Sardarabadi ,&nbsp;Amirhossein Heydarian Ardakani ,&nbsp;Silvana Matrone ,&nbsp;Emanuele Ogliari ,&nbsp;Elham Shirazi","doi":"10.1016/j.egyai.2025.100540","DOIUrl":"10.1016/j.egyai.2025.100540","url":null,"abstract":"<div><div>The integration of photovoltaic (PV) systems into power grids presents operational challenges due to the inherent variability in solar power generation. Accurate PV power forecasting can help address these issues by enhancing grid reliability and energy management. This study introduces a novel hybrid deep learning approach that combines Wavelet Packet Decomposition (WPD) and Long Short-Term Memory (LSTM) networks to improve forecasting accuracy across multiple time horizons. The proposed model incorporates a dynamic weighting mechanism to optimally integrate the forecasts of decomposed subseries, effectively capturing both high- and low-frequency components of the power signal. Using real-world data from a solar parking site at the University of Twente, Netherlands, the proposed models are compared with standard LSTM, Linear Regression, and Persistence baselines across 15 min, 1-hour, and day-ahead horizons. The WPD-LSTM model with weight optimization reduces nRMSE by up to 72.5%, 52.9%, and 34.7% compared to Persistence, and by 68.6%, 36.1%, and 7.5% compared to standalone LSTM, respectively. These results highlight the effectiveness of the hybrid approach in delivering more accurate and robust PV power forecasts.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100540"},"PeriodicalIF":9.6,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535869","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":"Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading","authors":"Ioanna Kalospyrou,&nbsp;Timothy Hutty,&nbsp;Robert Milton,&nbsp;Solomon Brown","doi":"10.1016/j.egyai.2025.100543","DOIUrl":"10.1016/j.egyai.2025.100543","url":null,"abstract":"<div><div>The maximisation of renewable energy generation is critical for net-zero aspiring countries around the globe. Local energy markets facilitate the seamless incorporation of energy from distributed renewable energy resources into the electricity network, serving as platforms for trading locally-generated renewable energy between prosumers in residential communities. However, local energy markets’ essential role in distributed energy resource integration is not enough to encourage participation. Prosumers are more likely to join a local energy market if financial incentives are offered. To address this, we present the Flexible Aggressiveness Probabilistic Optimisation (FAPO) bidding strategy for trading electricity within a local energy market aimed at maximising participation incentives. This is formulated as an optimisation problem targeting the maximisation of prosumers’ individual utilities. The FAPO methodology is applied in a simplified local energy market simulation environment, and its results are compared to two other well-established bidding strategies: Zero Intelligence-Constrained and Adaptive Aggressiveness. The results indicate that FAPO achieved a wider range of clearing prices than both Adaptive Aggressiveness and Zero Intelligence-Constrained, incentivising greater prosumer participation. Specifically, FAPO enabled the trading of 1.48 MWh of electricity, compared to 1.34 MWh with Adaptive Aggressiveness and 1.37 MWh with Zero Intelligence-Constrained. Furthermore, FAPO cleared 100% of all asks and 98% of all bids, while the other two strategies cleared approximately 90% of submitted orders. Consequently, FAPO is proven to be an engaging bidding methodology likely to attract more prosumers to local energy markets. This is critical for the successful acceptance, uptake, and widespread application of this financial market type, which is key for smooth distributed energy resource integration into the network.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100543"},"PeriodicalIF":9.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522281","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":"Transformer-based forecasting for high-frequency natural gas production data","authors":"Saifei Ma ,&nbsp;Tiantian Zhang ,&nbsp;Haibo Wang ,&nbsp;Haoyu Wang ,&nbsp;Nan Li ,&nbsp;Haiwen Zhu ,&nbsp;Jianjun Zhu ,&nbsp;Jianli Wang","doi":"10.1016/j.egyai.2025.100535","DOIUrl":"10.1016/j.egyai.2025.100535","url":null,"abstract":"<div><div>Accurate prediction of natural gas well production data is crucial for effective resource management and innovation, particularly amid the global transition to sustainable energy. Traditional models struggle with high-frequency, high-dimensional datasets generated by digital transformation in the oil and gas industry. This study explores the application of Transformer-based models — Transformer, Informer, Autoformer, and Patch Time Series Transformer (PatchTST) — for forecasting high-frequency natural gas production data. These models utilize self-attention mechanisms to capture long-term dependencies and efficiently process large-scale datasets. Autoformer achieves predictive success through its Seasonal Decomposition Attention mechanism, which effectively extracts trend-seasonality patterns. However, our experiments show that Autoformer exhibits sensitivity to dataset changes, as performance declines when using old parameters compared to retrained models, highlighting its reliance on dataset-specific retraining. Experimental results demonstrate that increasing sampling frequency significantly enhances prediction accuracy, reducing MAPE from 0.556 to 0.239. Additionally, these models consistently track actual production trends across extended forecast horizons. Notably, PatchTST maintains stable performance using either pretrained or retrained parameters, showcasing superior adaptability and generalization. This makes it particularly suitable for real-world applications where frequent retraining may not be feasible. Overall, the findings validate the applicability of Transformer-based models, particularly PatchTST, in dynamic and precise natural gas production forecasting. This study provides valuable insights for advancing adaptive, data-driven resource management strategies.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100535"},"PeriodicalIF":9.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480982","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":"Enhancing electric vehicle charging load prediction in data-scarce scenarios: A hybrid deep learning-based approach integrating clustering analysis and transfer learning","authors":"Rehman Zafar ,&nbsp;Pei Huang ,&nbsp;Yongjun Sun","doi":"10.1016/j.egyai.2025.100545","DOIUrl":"10.1016/j.egyai.2025.100545","url":null,"abstract":"<div><div>Accurate electric vehicle (EV) load forecasting is crucial for efficient grid operations and demand-side management, yet it is challenging in data-scarce scenarios. Transfer learning (TL) offers a solution by transferring knowledge from data-rich to data-limited scenarios. However, when the knowledge domain exhibits highly diverse behaviors, applying TL alone could introduce large biases, reducing accuracy and limiting its effectiveness. To address this problem, this study proposes a hybrid deep learning-based framework that integrates TL and K-means clustering. The proposed approach consists of two phases. In the source domain phase, a deep-learning-based model is trained using the full dataset and then fine-tuned using clustered user behaviors. In the target domain phase with limited data, TL is applied to transfer knowledge from the source-domain fine-tuned cluster models. For validation, the developed prediction method has been tested using real-world datasets and compared with two other cases: one with applying TL from the source-domain base model trained from full dataset, and one without applying TL. Results show the hybrid method improves forecasting accuracy, reducing the normalized root mean squared error by 3.99 % and 8.22 %, respectively. This study establishes a structured approach for targeted knowledge transfer, enhancing prediction accuracy in data-scarce settings. The framework is scalable and adaptable to other energy forecasting applications, supporting sustainable and resilient energy management.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100545"},"PeriodicalIF":9.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501782","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":"Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition","authors":"Zhiyu Zhao ,&nbsp;Kangning Li ,&nbsp;Yunhao Chen ,&nbsp;Jinyang Wang","doi":"10.1016/j.egyai.2025.100544","DOIUrl":"10.1016/j.egyai.2025.100544","url":null,"abstract":"<div><div>The integration of remote sensing and artificial intelligence technologies into photovoltaic (PV) power generation has significantly enhanced the efficiency and precision of monitoring and evaluating PV station construction. However, most semantic segmentation models are primarily developed for natural scenes, often neglecting the distinctive visual attributes of PV panels. We introduce a visual feature constraint method designed to tailor the segmentation network to the unique aspects of PV panels, including their texture, color, and shape. The method incorporates a constraint module, comprised of three adversarial autoencoders, into a conventional segmentation model. This technique represents a versatile training framework that can be seamlessly integrated with state-of-the-art models, providing clear insights into the learning process. Experimental results with UperNet, SegFormer, DeepLabV3+, TransUNet, CorrMatch, SCSM and UKAN as baseline models show a maximum IoU improvement of 2.16 %. Notably, UperNet attains the superior segmentation outcomes, whereas DeepLabV3+ exhibits the greatest benefit from the imposed constraints. Furthermore, our findings reveal that various models exhibit distinct sensitivities to different visual features, and employing multiple constraints typically yields better results than relying on single-feature constraints. Collectively, our proposed method showcases its potential to advance PV panel segmentation in remote sensing applications, presenting a scalable and effective solution.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100544"},"PeriodicalIF":9.6,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522282","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":"Distributed nonlinear model predictive control for building energy systems: An ALADIN implementation study","authors":"Steffen Eser,&nbsp;Ben Spoek,&nbsp;Augustinus Schütz,&nbsp;Phillip Stoffel,&nbsp;Dirk Müller","doi":"10.1016/j.egyai.2025.100536","DOIUrl":"10.1016/j.egyai.2025.100536","url":null,"abstract":"<div><div>The implementation of sophisticated control strategies for building energy systems is crucial for improving energy efficiency and occupant comfort. While nonlinear model predictive control offers promising benefits, its application to large-scale building systems remains challenging due to computational complexity and system coupling. This work presents a comprehensive study of Nonlinear Distributed Model Predictive Control (NDMPC) implementation for building energy systems, comparing Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithms alongside different modeling approaches. We examine a multi-zone heating system with thermal storage and multiple producers, investigating both Ordinary Differential Equation (ODE)-based and Artificial Neural Network (ANN) based modeling strategies. Through systematic parameter tuning using Bayesian optimization and closed-loop scaling analysis with up to 40 thermal zones, we demonstrate that ALADIN-based NDMPC can achieve performance comparable to centralized model predictive control, showing greater robustness to parameter variations than ADMM. Our results reveal that ANN-based models effectively mitigate distributed integration errors and significantly reduce computation time compared to ODE-based approaches. Detailed computational profiling identifies specific bottlenecks in different NDMPC components. These findings advance the practical implementation of NDMPC in building energy systems, offering concrete strategies for modeling choices, parameter tuning, and system architecture design.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100536"},"PeriodicalIF":9.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338859","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":"Fast data augmentation for battery degradation prediction","authors":"Weihan Li ,&nbsp;Harshvardhan Samsukha ,&nbsp;Bruis van Vlijmen ,&nbsp;Lisen Yan ,&nbsp;Samuel Greenbank ,&nbsp;Simona Onori ,&nbsp;Venkat Viswanathan","doi":"10.1016/j.egyai.2025.100542","DOIUrl":"10.1016/j.egyai.2025.100542","url":null,"abstract":"<div><div>Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field, is time- and resource-intensive. Here, we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications. These datasets encompass various chemistries and realistic conditions, including cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery. Our results show that it is possible to reduce cell-testing efforts by at least 50 % by substituting synthetic data into an existing dataset. This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100542"},"PeriodicalIF":9.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364418","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":"Continuous variable quantum reinforcement learning for HVAC control and power management in residential building","authors":"Sarvar Hussain Nengroo ,&nbsp;Dongsoo Har ,&nbsp;Hoon Jeong ,&nbsp;Taewook Heo ,&nbsp;Sangkeum Lee","doi":"10.1016/j.egyai.2025.100541","DOIUrl":"10.1016/j.egyai.2025.100541","url":null,"abstract":"<div><div>The use of occupancy information for heating, ventilation, and air conditioning (HVAC) control in smart buildings has become increasingly important for enhancing energy efficiency and occupant comfort. However, residential HVAC control presents significant challenges due to the complex dynamic nature of buildings and the uncertainties associated with heat loads and weather conditions. This study addresses this gap in adaptive and energy efficient HVAC control by introducing a quantum reinforcement learning (QRL) based approach. Unlike conventional reinforcement learning techniques, the QRL leverages quantum computing principles to efficiently handle high dimensional state and action spaces, enabling more precise HVAC control in multi-zone residential buildings. The proposed framework integrates real-time occupancy detection using deep learning with operational data, including power consumption patterns, air conditioner control data, and external temperature variations. To evaluate the effectiveness of the proposed approach, simulations were conducted using real world data from 26 residential households over a three month period. The results demonstrate that the QRL based HVAC control significantly reduces energy consumption and electricity costs while maintaining thermal comfort. Compared to the deep deterministic policy gradient method, the QRL approach achieved a 63% reduction in power consumption and a 64.4% decrease in electricity costs. Similarly, it outperformed the proximal policy optimization algorithm, leading to an average reduction of 62.5% in electricity costs and 62.4% in power consumption.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100541"},"PeriodicalIF":9.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480981","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}