Energy and AI最新文献_第4页

A novel spatiotemporal relation fused network for solar photovoltaic power forecasting 一种新的时空关系融合网络用于太阳能光伏发电功率预测

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-06 DOI: 10.1016/j.egyai.2026.100676

Zuming Liu , Songyi Li , Jiaxin Ding , Yi Zheng , Bo Chen , Yijun He

{"title":"A novel spatiotemporal relation fused network for solar photovoltaic power forecasting","authors":"Zuming Liu , Songyi Li , Jiaxin Ding , Yi Zheng , Bo Chen , Yijun He","doi":"10.1016/j.egyai.2026.100676","DOIUrl":"10.1016/j.egyai.2026.100676","url":null,"abstract":"<div><div>Accurate prediction of solar photovoltaic power is crucial for renewable integration. However, existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies. Here, we propose a novel spatiotemporal relationship fusion network (STRFN) for short-term prediction of photovoltaic power generation. STRFN uses convolutional neural networks to extract spatial features, long short-term memory networks to capture time dependence, and an attention mechanism to enhance its expressiveness. Additionally, the optimal network hyperparameters of STRFN are identified through Bayesian optimization. Moreover, it employs advanced data preprocessing techniques to improve input data quality. These techniques include feature recognition, principal component analysis, location coding, and sliding window segmentation. Our STRFN is applied to two typical PV systems for demonstration and compared with traditional deep learning models. The results show that our model’s accuracy and stability significantly outperform traditional deep learning models, with RMSE of 2.46 and 0.036, and MAPE of 1.51 % and 1.94 %. Furthermore, in predictions for typical days across four seasons, our STRFN still maintained consistent superior performance—evidenced by its normalized RMSE (NRMSE) of 0.90 % and 0.61 % for the two PV systems. Finally, we integrate data processing, model training, and results visualization together into a one-stop platform and make it user-friendly and easily improved for solar power prediction. Our proposed method along with its forecasting platform can offer valuable insights and guidelines for researchers and PV operators.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100676"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977682","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}

引用次数: 0

A World Model framework with digital twin for scheduling demand-side resources under systemic complexity 系统复杂性下需求侧资源调度的数字孪生世界模型框架

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2025-12-23 DOI: 10.1016/j.egyai.2025.100660

Xing He , Yikang Bu , Guoquan Yuan , Junjie Yin , Zhuangyan Zhang , Qian Ai , Caiming Qiu

{"title":"A World Model framework with digital twin for scheduling demand-side resources under systemic complexity","authors":"Xing He , Yikang Bu , Guoquan Yuan , Junjie Yin , Zhuangyan Zhang , Qian Ai , Caiming Qiu","doi":"10.1016/j.egyai.2025.100660","DOIUrl":"10.1016/j.egyai.2025.100660","url":null,"abstract":"<div><div>Modern power grids face profound challenges in scheduling massive, heterogeneous demand-side resources (DSRs), whose collective behaviors often lead to systemic unpredictability and scheduling inaccuracies. Traditional methods, often based on simplified models, struggle to manage this emergent complexity. To address this gap, this paper introduces MetaGrid, a novel digital-twin-enhanced World Model framework designed for proactive and prescient DSR scheduling. The MetaGrid architecture is composed of four integral, closed-loop building blocks: a General Simulator for multi-path deduction, a Situational Perceiver for holistic cognition, an Intelligent Decision-Maker for autonomous optimization, and a Unified Verifier for ensuring trustworthy iteration. By integrating principles from complexity science with data-intensive machine learning, MetaGrid creates a high-fidelity metaverse to model and manage DSR ecosystems. The framework’s core capabilities are demonstrated through an energy storage system scheduling case, where an intelligent agent, guided by the World Model, learns to autonomously balance real-time electricity costs against physical battery degradation constraints. This preliminary validation showcases MetaGrid’s potential as a powerful tool for navigating the complexities of future energy systems, transforming scheduling from a reactive control problem into a process of continuous, adaptive learning.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100660"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926796","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}

引用次数: 0

Cooperative multi-agent reinforcement learning for grid-aware EV charging management with cross-site redirection 基于协同多智能体强化学习的电网感知电动汽车充电管理

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2025-12-13 DOI: 10.1016/j.egyai.2025.100664

Yuchen Yang , Rui Tang

{"title":"Cooperative multi-agent reinforcement learning for grid-aware EV charging management with cross-site redirection","authors":"Yuchen Yang , Rui Tang","doi":"10.1016/j.egyai.2025.100664","DOIUrl":"10.1016/j.egyai.2025.100664","url":null,"abstract":"<div><div>As electric vehicles (EV) become more widespread, managing charging demand is critical for grid stability and efficient resource allocation. However, prior work often optimises single sites or treats demand as exogenous, without explicitly modelling grid capacity constraints or the aggregate effects of redirection, and it rarely offers an integrated capacity-aware view that links domestic and public charging under system-level coordination. This study, therefore, develops a multi-agent reinforcement learning framework with cross-site redirection to optimise EV charging operations. The research area focuses on the Perth and Kinross region of Scotland in the UK, using real public charging records from the council database as well as domestic charging statistics from the UK Department for Transport. Charging sites are modelled as cooperative agents using the Multi-Agent Deep Deterministic Policy Gradient algorithm, trained in a spatiotemporal environment that integrates public charging data and simulated domestic demand. We contribute by unifying public and home charging demand in one learning environment, introducing a cross-site actor trained under centralised training and decentralised execution, and incorporating a delayed replay buffer with short-horizon forecasts so redirection aligns with future congestion. The learned policy reduces peak-hour load standard deviation by up to 40 % and lowers cumulative threshold violations by 37 % compared to the baseline. Distinct weekday and weekend strategies emerge, enabling adaptive coordination under varying demand patterns. The study provides interpretable control for EV networks, balancing peak demand and service quality across sites while addressing the system-level coordination.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100664"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791892","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}

引用次数: 0

Practicality-enhanced behind-the-meter PV power generation disaggregation based on synchronization and transferability fused LSTM framework 实用性增强的基于同步和可转移性融合LSTM框架的光伏发电分解

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1016/j.egyai.2026.100675

Chengye Zhang , Huan Long , Zijun Zhang , Jinde Cao

{"title":"Practicality-enhanced behind-the-meter PV power generation disaggregation based on synchronization and transferability fused LSTM framework","authors":"Chengye Zhang , Huan Long , Zijun Zhang , Jinde Cao","doi":"10.1016/j.egyai.2026.100675","DOIUrl":"10.1016/j.egyai.2026.100675","url":null,"abstract":"<div><div>To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated, the availability of community-level behind-the-meter (BTM) PV power generation is crucial. Yet, due to the scarcity of smart meters installed, it is challenging to obtain such information via directly aggregating measured power outputs of individual PV systems, and an effective estimation method needs to be developed. Considering the similarity between household-level and community-level data within the same geographical area, this paper develops a synchronization and model-transfer fused LSTM framework (SAM-LSTM). The core technical contribution lies in the development of the Synchronized Long Short-Term Memory (Syn-LSTM), which separately models the synchronized factors and disaggregated BTM data to capture more generalized representations. The learned household-level representations are then transferred to the community-level. Finally, by explicitly leveraging the complementarity between PV generation and consumption, a dual time-series modeling architecture is developed to refine the initial community-level PV power generation estimates, thereby alleviating potential biases introduced during the model-transfer process. Extensive computational studies are conducted to demonstrate the effectiveness of SAM-LSTM in community-level BTM PV power generation disaggregation in real data from Hebei, China. Compared with the best-performing benchmarks, SAM-LSTM achieves up to 56% lower MSE, significantly demonstrating its strong generalization and robustness capabilities.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100675"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977684","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}

引用次数: 0

A bi-level advanced control framework for large-scale control of buildings with system-level impact 具有系统级影响的建筑物大规模控制的两级高级控制框架

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-16 DOI: 10.1016/j.egyai.2026.100678

Dylan Wald , Olga Doronina , Kathryn Johnson , Ryan King , Michael Sinner , Kevin Griffin , Rohit Chintala , Deepthi Vaidhynathan , Jibonananda Sanyal , Marc Day

{"title":"A bi-level advanced control framework for large-scale control of buildings with system-level impact","authors":"Dylan Wald , Olga Doronina , Kathryn Johnson , Ryan King , Michael Sinner , Kevin Griffin , Rohit Chintala , Deepthi Vaidhynathan , Jibonananda Sanyal , Marc Day","doi":"10.1016/j.egyai.2026.100678","DOIUrl":"10.1016/j.egyai.2026.100678","url":null,"abstract":"<div><div>Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of <span><math><mo>±</mo></math></span>5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100678"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022844","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}

引用次数: 0

A transformer-LSTM network enhanced by EEMD for ultra-short-term wind power forecasting 基于EEMD的超短期风电预测变压器- lstm网络

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-06 DOI: 10.1016/j.egyai.2026.100682

YongSheng Wang, Fan Yang, YongSheng Qi, GuangChen Liu, JiaJing Gao, XueHui Wang, ZhenChao Wang

{"title":"A transformer-LSTM network enhanced by EEMD for ultra-short-term wind power forecasting","authors":"YongSheng Wang, Fan Yang, YongSheng Qi, GuangChen Liu, JiaJing Gao, XueHui Wang, ZhenChao Wang","doi":"10.1016/j.egyai.2026.100682","DOIUrl":"10.1016/j.egyai.2026.100682","url":null,"abstract":"<div><div>This study aims to improve the dispatch safety and economic efficiency of grid-connected wind power systems by addressing the limitations of traditional ultra-short-term forecasting methods, particularly their inadequate extraction of multi-scale features and limited forecasting accuracy. A short-term wind power forecasting model that integrates signal decomposition with deep learning is proposed. The model first applies Ensemble Empirical Mode Decomposition (EEMD) to the raw wind power data to reduce non-stationarity and extract multi-scale features. A lightweight Transformer attention mechanism is then employed to model global dependencies, and Long Short-Term Memory (LSTM) networks are incorporated to capture the temporal dynamics of the sequence. The final power forecasting is generated through a fully connected layer. Finally, the Alpha Evolution (AE) algorithm is employed to optimize the model's hyperparameters. Experiments on multiple datasets show that the proposed model outperforms traditional machine learning and deep learning approaches across various evaluation metrics. It achieves higher fitting accuracy, confirming its effectiveness and robustness in multi-scale feature extraction and wind power forecasting.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100682"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926794","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}

引用次数: 0

An intelligent multi-layer framework for attack conduction, detection and reconstruction in the smart grid with renewable energies 一种面向可再生能源智能电网攻击传导、检测与重构的多层智能框架

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.egyai.2025.100636

Mostafa Mohammadpourfard , Yang Weng , Mahdi Eghbali , Manohar Chamana , Suhas Pol

{"title":"An intelligent multi-layer framework for attack conduction, detection and reconstruction in the smart grid with renewable energies","authors":"Mostafa Mohammadpourfard , Yang Weng , Mahdi Eghbali , Manohar Chamana , Suhas Pol","doi":"10.1016/j.egyai.2025.100636","DOIUrl":"10.1016/j.egyai.2025.100636","url":null,"abstract":"<div><div>The rapid proliferation of renewable energy sources (RESs) has enhanced operational flexibility but intensified cybersecurity concerns in modern power systems. In this work, we investigate how attackers can exploit the increased variability introduced by RESs to orchestrate false data injection attacks (FDIAs). First, we propose a targeted attack strategy based on Jensen–Shannon divergence (JSD) and the Kolmogorov–Smirnov (KS) test. This two-stage procedure identifies measurements that exhibit minimal distributional shifts after RES integration. False data are then injected into these stable measurements, blending seamlessly into the expanded measurement space and increasing attack stealth. Second, we develop a customized hybrid deep learning model, combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) units to capture both spatial correlations and temporal dynamics in power system measurements. This design explicitly addresses concept drift arising from fluctuating load and generation profiles, ensuring persistent detection accuracy. Third, we integrate an Autoencoder (AE)–based reconstruction mechanism to repair compromised measurements upon detection, mitigating denial-of-service (DoS) scenarios that could result from discarding suspect data. Our evaluations on the IEEE 14-bus and 118-bus systems, using real-world load profiles, confirm that the JSD–KS approach boosts attack stealth while the CNN–LSTM–AE pipeline achieves robust detection and recovery. Our experiments on the IEEE 14-bus and 118-bus systems demonstrate F1-score gains of up to 3% over the strongest CLSTM baseline under traditional FDIA scenarios, and up to 13% under our intelligent FDIA, while also reducing AE reconstruction RMSE by approximately 6%–7%. This integrated strategy offers a multi-layered defense against evolving cyber threats in renewable-rich smart grids.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100636"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739285","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}

引用次数: 0

Spectrogram-driven unsupervised autoencoder with isolation forest and one-class SVM for lab-scale wind turbine blade fault detection 基于隔离森林和一类支持向量机的谱图驱动无监督自编码器用于风力发电机叶片故障检测

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-05 DOI: 10.1016/j.egyai.2026.100681

Waqar Ali , Idriss El-Thalji , Knut Erik Teigen Giljarhus , Andreas Delimitis

{"title":"Spectrogram-driven unsupervised autoencoder with isolation forest and one-class SVM for lab-scale wind turbine blade fault detection","authors":"Waqar Ali , Idriss El-Thalji , Knut Erik Teigen Giljarhus , Andreas Delimitis","doi":"10.1016/j.egyai.2026.100681","DOIUrl":"10.1016/j.egyai.2026.100681","url":null,"abstract":"<div><div>Wind turbine blades are critical components, and their structural integrity is essential for uninterrupted operation and minimizing downtime. Although various methods are used to monitor the health of wind turbine blades, several research challenges persist, such as the reliance on manual feature engineering and the limited availability of large amounts of labeled data. In this study, a novel approach is proposed that will overcome the limitations of manual feature extraction and label data challenges. In the proposed work, time series vibration signals from the blade are first converted into spectrograms and passed through a CNN-based autoencoder that is trained solely on healthy data to learn a compact latent representation. Anomalies are then flagged in three complementary ways: (i) by thresholding the autoencoder’s reconstruction error, (ii) by applying an Isolation Forest to the latent features, and (iii) by evaluating the same features with a One-Class SVM. The outputs of these detectors are subsequently benchmarked, providing a systematic comparison of their ability to discriminate between vibration-induced faults, such as cracks and erosion, and normal operation on a controlled test-rig dataset, the autoencoder achieves 97.2 % accuracy, outperforming the Isolation Forest and One-Class SVM by 8%–27%. These results demonstrate that zero-label, deep-feature pipelines can deliver reliable and scalable blade-fault detection, paving the way for more cost-effective predictive maintenance in wind farms.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100681"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926793","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}

引用次数: 0

Photovoltaic Knowledge-Informed Neural Network (PKINN): Interpretable power prediction model under Fluctuating Environmental Conditions 光伏知识知情神经网络（PKINN）：波动环境下的可解释功率预测模型

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2026-01-13 DOI: 10.1016/j.egyai.2026.100683

Jialong Pei , Jieming Ma , Ka Lok Man , Martin Gairing

引用次数: 0

MI-VMD-BSCNet: A lightweight spatiotemporal modeling framework for tube temperature prediction in coal-fired boiler water-walls MI-VMD-BSCNet：燃煤锅炉水冷壁管温预测的轻量级时空建模框架

IF 9.6

Energy and AI Pub Date : 2026-01-01 Epub Date: 2025-12-02 DOI: 10.1016/j.egyai.2025.100656

Shiming Xu , Zhiqian He , Xianyong Peng , Zhi Wang , Yuhan Wang , Youxiang Zhang , Guangmin Yang , Mingcheng Zhang , Jinsha Luo , Yunxi Guo , Huan Liu , Meixi Zhao , Junqin Yan , Fan Geng , Huaichun Zhou

{"title":"MI-VMD-BSCNet: A lightweight spatiotemporal modeling framework for tube temperature prediction in coal-fired boiler water-walls","authors":"Shiming Xu , Zhiqian He , Xianyong Peng , Zhi Wang , Yuhan Wang , Youxiang Zhang , Guangmin Yang , Mingcheng Zhang , Jinsha Luo , Yunxi Guo , Huan Liu , Meixi Zhao , Junqin Yan , Fan Geng , Huaichun Zhou","doi":"10.1016/j.egyai.2025.100656","DOIUrl":"10.1016/j.egyai.2025.100656","url":null,"abstract":"<div><div>Over-temperature of boiler water-walls causes tube leakage in ultra-supercritical coal-fired power units. This is a critical issue intensified by frequent load fluctuations from flexible peak shaving, essential for carbon peaking and neutrality goals. Existing computational fluid dynamics methods have high computational load, limiting their suitability for real-time monitoring, while data-driven approaches cannot accurately capture dynamic temperature changes under rapid load ramp. This study proposes a lightweight spatiotemporal modeling framework, referred to as mutual information-variational mode decomposition-broad skip connection network (MI-VMD-BSCNet), for high-accuracy and low-cost water-wall temperature prediction, advancing artificial intelligence applications in energy systems. A feature selection method reduces the input complexity, advanced signal processing enhances the temporal feature representation, and a sliding window approach captures the underlying local and global patterns. BSC<img>Net leverages a parallel feature extraction architecture and skip connections to optimize feature fusion and gradient flow, allowing to improve the modeling of dynamic temperature variations. The model is trained and evaluated using historical data from a 1000 MW ultra-supercritical coal-fired boiler. The obtained results demonstrate that it outperforms baseline convolutional neural network and broad learning system models, achieving mean absolute error, mean absolute percentage error, and root mean square error of 1.493 °C, 0.395%, and 1.964 °C, respectively. This framework enables early warning of over-temperature failures, which supports sustainable boiler operation and provides a high potential for theoretical and engineering advancements.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"23 ","pages":"Article 100656"},"PeriodicalIF":9.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718919","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}

引用次数: 0