Energy and AI最新文献

{"title":"Smart energy strategies: Leveraging LSTM and LLMs for advanced energy management","authors":"Fernando Almeida ,&nbsp;Mauro Castelli ,&nbsp;Nadine Côrte-Real ,&nbsp;Camilla Fallarino ,&nbsp;Luca Manzoni","doi":"10.1016/j.egyai.2025.100642","DOIUrl":"10.1016/j.egyai.2025.100642","url":null,"abstract":"<div><div>Accurately predicting and optimizing heating and cooling demands in building energy management is crucial for enhancing efficiency and reducing energy consumption. Traditional methods often struggle with building energy usage patterns' nonlinear and variable nature. With the advent of advanced data collection through smart sensors, there is a growing need for intelligent systems to leverage this data to provide actionable insights. This study addresses the gap by developing a recommendation system using three machine and deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and XGBoost to predict and optimize the efficiency levels of space heating, ceiling cooling, and free cooling systems. Our proposed solution harnesses the power of these models, with LSTM performing best overall, to forecast energy consumption across hourly and daily timescales, enabling precise adjustments and efficient energy management. The methodology involves extensive data preprocessing, including hierarchical imputation of missing values and label encoding of categorical variables, followed by the transformation of raw data into efficiency levels. The deep learning model architecture, consisting of sequential layers, captures long-term dependencies in the data, while grid search-based hyperparameter tuning optimizes model performance. Results indicate high predictive accuracy, with R-squared values demonstrating the model's ability to explain up to 97.2 % of the variance in hourly space heating, 95.2 % in daily ceiling cooling, and 93 % in daily free cooling energy consumption. Additionally, we interpret graphs using OpenAI's GPT-4 model to enhance understanding and facilitate actionable insights. This interpretation enhances the clarity of the predictive results, supporting more informed decision-making in energy management. The significance of this work lies in its potential to transform energy management practices in building environments, providing a robust tool for optimizing heating and cooling operations and contributing to overall energy efficiency.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100642"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416080","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":"ScaleONet: Scalable and control-oriented modeling of building cluster thermal dynamics using deep operator networks — A practical case study for a Belgian district","authors":"Muhammad Hafeez Saeed ,&nbsp;Maomao Hu ,&nbsp;Hussain Kazmi ,&nbsp;Geert Deconinck","doi":"10.1016/j.egyai.2025.100634","DOIUrl":"10.1016/j.egyai.2025.100634","url":null,"abstract":"<div><div>Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand; this coordination depends on training and deploying control policies and optimization routines, which in turn require predictive models that can be queried efficiently over large building clusters. However, conventional physics-based simulators are computationally prohibitive for large-scale control training, and simple data-driven surrogates often lack the generalization needed for heterogeneous clusters. This paper introduces <em>ScaleONet</em>, a deep operator network framework designed for scalable, control-oriented modeling of building-cluster thermal dynamics. ScaleONet leverages the DeepONet paradigm to decouple and share learning across buildings: an LSTM-based branch network encodes outdoor climate and individual HVAC control signals, while a multilayer perceptron (MLP)-based trunk network embeds prediction timestamps, enabling fast predictions for growing clusters with negligible extra cost for each additional building or timestep. To the authors’ knowledge, this is the first operator-learning method tailored to indoor air temperature forecasting in heterogeneous building clusters. Validation on thirty Belgian buildings (GenkNet) simulated in Dymola shows that, although a non-operator-learning LSTM baseline slightly outperforms ScaleONet for single-building cases, its error grows monotonically with cluster size. In contrast, ScaleONet’s median per-building-per-day RMSE decreases from 0.59 °C at three buildings to 0.53 °C at ten and 0.47 °C at thirty, compared to 0.95 °C for the LSTM at thirty buildings — a 51% reduction in prediction error. Error analysis across envelope heat-loss coefficients (<span><math><msub><mrow><mi>UA</mi></mrow><mrow><mi>building</mi></mrow></msub></math></span>) further reveals that while the LSTM’s RMSE increases for high-<span><math><mrow><mi>U</mi><mi>A</mi></mrow></math></span> structures, ScaleONet maintains uniformly low error. With millisecond-scale inference (approximately <span><math><mrow><mn>4</mn><mspace></mspace><mi>ms</mi></mrow></math></span> per sample for thirty buildings), ScaleONet is well suited for large-scale reinforcement learning, receding-horizon optimization, and real-time model predictive control.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100634"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416162","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":"An integrated agent-based modelling and artificial intelligence framework for enhancing the experience of minority ethnic communities in digital energy services","authors":"Mennan Guder,&nbsp;Nazmiye Balta-Ozkan","doi":"10.1016/j.egyai.2025.100624","DOIUrl":"10.1016/j.egyai.2025.100624","url":null,"abstract":"<div><div>Digitalisation plays a pivotal role in enhancing energy efficiency; however, it also highlights significant governance challenges and exacerbates various forms of energy injustice. This study explores how technological injustice exacerbates energy poverty, particularly via disparities in digital service access. The focus is on understanding and addressing challenges faced by minority ethnic (ME) communities, who often encounter heightened barriers to essential online energy services. While previous research has noted barriers ME communities face in energy markets, this study broadens this literature to analyse these issues for access to digital energy services.</div><div>The study integrates modelling, simulation, and AI to address these inequalities. The framework comprises three core modules: AI, Environment Configuration, and Agent-Based Modelling (ABM) and Simulation. Its primary aim is to identify effective strategies, policy changes, and adjustments that enhance online service experiences while addressing the unique challenges faced by these communities.</div><div>The AI Module uses ensemble-based ML pipelines to develop region-specific models. It addresses issues such as high dimensionality and overfitting by incorporating methods like Principal Component Analysis, Recursive Feature Elimination, and hyperparameter optimization.</div><div>The Environment Configuration Module supports tailored simulations by adapting datasets and regional characteristics, ensuring the accuracy and relevance of the simulations to the target communities.</div><div>The ABM and Simulation Module facilitates in-depth analysis of policy impacts and service provider attributes.</div><div>This framework offers valuable insights into improving online service delivery, promoting fairness, and addressing disparities in digital experiences. This work advances energy justice research by quantifying how socio-technical barriers disproportionately affect ME communities.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100624"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220301","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":"Data-driven machine learning model estimates efficiency gains from passive filters under variable loads","authors":"Uchenna Johnpaul Aniekwensi ,&nbsp;Dipyaman Basu ,&nbsp;Jörg Bausch","doi":"10.1016/j.egyai.2025.100631","DOIUrl":"10.1016/j.egyai.2025.100631","url":null,"abstract":"<div><div>Accurately estimating power loss reduction from passive filters before installation is challenging due to variable loads and power quality conditions across grid points. Existing studies rely on simulation or analytical models. These approaches often fail to capture real-world variability through data-driven methods. This gap limits effective, site-specific filter deployment decisions. We present a two-step machine learning approach to estimate energy efficiency gains from passive filters under variable conditions using high-resolution power analyzer data. Ridge Regression identifies key predictive variables, achieving baseline R² = 0.591. XGBoost then captures nonlinear interactions between load variability, power quality disturbances, and filter performance, improving accuracy to R² = 0.755. The methodology was validated through deployment at three industrial facilities in collaboration with Livarsa GmbH. Results demonstrate 9.9% average relative error across measured efficiency gains, confirming reliability under real-world conditions. Comprehensive validation through k-fold cross-validation, ensemble methods, and external testing quantified prediction uncertainty inherent in small industrial datasets (25 training samples). The approach offers a scalable, data-driven decision-support tool overcoming simulation-based limitations. Computational efficiency enables real-time assessment during client consultations without specialized software. Economic value derives from reduced performance guarantee margins, accelerated assessment timelines, and minimized warranty exposure. Limitations include statistical constraints from limited training data, reflected in cross-validation overfitting and wide confidence intervals. External validity requires site-specific validation for facilities with substantially different electrical characteristics. Despite these constraints, the findings provide practical value for energy professionals seeking efficient power quality solutions, enabling confident passive filter deployment decisions based on quantified performance predictions.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100631"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362268","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":"Fault detection in new wind turbines with limited data by generative transfer learning","authors":"Stefan Jonas ,&nbsp;Angela Meyer","doi":"10.1016/j.egyai.2025.100626","DOIUrl":"10.1016/j.egyai.2025.100626","url":null,"abstract":"<div><div>Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect anomalies and, eventually, operation faults. However, data-driven normal behavior models (NBMs) require a substantial amount of training data, as NBMs trained with scarce data may result in unreliable fault detection. To overcome this limitation, we present a novel generative deep transfer learning approach to make SCADA samples from one wind turbine lacking training data resemble SCADA data from wind turbines with representative training data. Through CycleGAN-based domain mapping, our method enables the application of an NBM trained on an existing wind turbine to a new one with severely limited data. We demonstrate our approach on field data mapping SCADA samples across 7 substantially different WTs. Our findings show significantly improved fault detection in wind turbines with scarce data. Our method achieves the most similar anomaly scores to an NBM trained with abundant data, outperforming NBMs trained on scarce training data with improvements of +10.3% in F1-score when 1 month of training data is available and +16.8% when 2 weeks are available. The domain mapping approach outperforms conventional fine-tuning at all considered degrees of data scarcity, ranging from 1 to 8 weeks of training data. The proposed technique enables earlier and more reliable fault detection in newly installed wind farms, demonstrating a novel and promising research direction to improve anomaly detection when faced with training data scarcity.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100626"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362270","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 new approach to fundamental electricity market modelling: Exploring market dynamics and speculator influence","authors":"Joseph Collins,&nbsp;Andreas Amann,&nbsp;Kieran Mulchrone","doi":"10.1016/j.egyai.2025.100639","DOIUrl":"10.1016/j.egyai.2025.100639","url":null,"abstract":"<div><div>Fundamental short-term electricity market models typically rely on expert-driven assumptions and manual, iterative calibration, and often neglect strategic bidding behaviours. To address these issues, we develop a bottom-up (fundamental) model that forecasts day-ahead outcomes from publicly available participant order data using regularised regression, machine learning, and neural network techniques. We introduce an explainability framework that decomposes forecast errors at participant, cohort, and aggregate levels, linking forecast performance to forecast trading behaviours. Compared with a benchmark top-down model, the bottom-up approach yields lower price forecast accuracy but demonstrates an ability to capture market dynamics. Where forecast dynamics diverge from observed outcomes, many misaligned cases are attributable to specific cohorts, particularly financial traders (speculators). Beyond forecasting, the framework offers complementary applications: the modelling approach can support calibration of traditional fundamental models and serve as a stand-alone forecaster in markets beyond day-ahead where order data are available, while the explainability component can apply to both bottom-up and hybrid modelling approaches. The study highlights the challenges inherent in bottom-up fundamental models, while showing how our approach provides new insights and practical tools to support their calibration and application.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100639"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465941","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":"MVLFLM: A parameter-efficient large language model framework for cross-domain multi-voltage load forecasting in smart grids","authors":"Guolong Liu ,&nbsp;Yan Bai ,&nbsp;Huan Zhao ,&nbsp;Keen Wen ,&nbsp;Xinlei Wang ,&nbsp;Jinjin Gu ,&nbsp;Yanli Liu ,&nbsp;Gaoqi Liang ,&nbsp;Junhua Zhao ,&nbsp;Zhao Yang Dong","doi":"10.1016/j.egyai.2025.100651","DOIUrl":"10.1016/j.egyai.2025.100651","url":null,"abstract":"<div><div>Modern smart grids face significant challenges in short-term load forecasting due to increasing complexity across transmission, distribution, and consumer levels. While recent studies have explored large language models for load forecasting, existing methods are limited by computational overhead, voltage-level specificity, and inadequate cross-domain generalization. This paper introduces Multi-Voltage Load Forecasting Large Model (MVLFLM), a unified Transformer-based framework that addresses multi-voltage STLF through parameter-efficient fine-tuning of a Llama 2-7B foundation model. Unlike previous LLM-based forecasting methods that focus on single voltage levels or require extensive retraining, MVLFLM employs selective layer freezing to preserve pre-trained knowledge while adapting only essential parameters for load pattern recognition. Comprehensive evaluation across four real-world datasets spanning high (transmission), medium (distribution), and low (consumer) voltage levels demonstrates MVLFLM’s superior performance, achieving higher performance than benchmarks. Most significantly, MVLFLM exhibits exceptional zero-shot generalization with only 9.07% average performance degradation when applied to unseen grid entities, substantially outperforming existing methods. These results establish MVLFLM as the unified, computationally efficient solution for multi-voltage load forecasting that maintains forecasting accuracy while enabling seamless deployment across heterogeneous smart grid infrastructures.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100651"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614664","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":"Autonomous wind turbine performance curve modeling based on SCADA data: A vision intelligence powered method","authors":"Chufan Wu ,&nbsp;Luoxiao Yang ,&nbsp;Zijun Zhang","doi":"10.1016/j.egyai.2025.100645","DOIUrl":"10.1016/j.egyai.2025.100645","url":null,"abstract":"<div><div>A central question in efficient wind farm big-data analytics is how to design an algorithm for autonomously extracting performance curves of wind turbines based on data collected via wind farm supervisory control and data acquisition (SCADA) systems. This paper investigates this question systematically, focusing on a challenging setting: the end-to-end autonomous analytics for directly generating mathematical functions of wind turbine performance curves from raw SCADA data. We propose a vision generative modeling (VGM) paradigm for autonomous development of wind turbine performance curve models. We discover that, compared with prevalently discussed numerical fitting-based performance curve modeling (NFM) methods, VGM directly working on raw data without any data preprocessing and model parameter tuning offers more generalizable and accurate results in deriving performance curves as well as their mathematical forms. The success of VGM is achieved by three computational steps developed in this study. By comparing with a set of state-of-the-art NFM benchmarks in multiple performance curve modeling tasks, we observe that VGM consistently performs more advantageously by achieving a 75.1% accuracy improvement in wind power curve modeling with insufficient SCADA data and an 84.3% improvement in modeling the rotor speed curve based on faulty field data. This work presents a milestone in autonomous wind turbine SCADA data analytics, which possesses a great potential of spanning to autonomous analytics of measured data of other industrial systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100645"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519753","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 charging demand prediction method for individual electric vehicle users based on dual-layer multisource data clustering and a LightGBM","authors":"Yunfei Mu ,&nbsp;Ruichao Zhou ,&nbsp;Kangning Zhao ,&nbsp;Hongjie Jia ,&nbsp;Guoqiang Zu ,&nbsp;Ye Yang","doi":"10.1016/j.egyai.2025.100644","DOIUrl":"10.1016/j.egyai.2025.100644","url":null,"abstract":"<div><div>The charging behaviors of electric vehicle (EV) users exhibit high randomness and individual heterogeneity, with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations. Compared with EV cluster-layer prediction, predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables (e.g., weather and holidays) and prediction accuracy, thereby imposing higher robustness requirements on prediction algorithms. An individual-user EV charging demand prediction method that integrates multisource data with a dual-layer clustering approach and a light gradient boosting machine (LightGBM) is proposed in this study to address these technical challenges. First, a multisource dataset that incorporates user charging behavior data and exogenous variables (meteorological factors and date types) is constructed. A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed, thereby establishing a classification feature space that characterizes different charging types and user groups. A predictive model is subsequently developed using the LightGBM algorithm, which directly incorporates classification features as its inputs, effectively mitigating the information loss associated with the traditional categorical variable encoding process. Finally, employing EV users from a typical residential community in northern China as an empirical case, comparative experiments are performed to validate the proposed method, demonstrating its effectiveness at improving prediction accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100644"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465937","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":"An enhanced sorting framework for retired batteries based on multi-dimensional features and an integrated clustering approach","authors":"Zhuo Liu ,&nbsp;Bumin Meng ,&nbsp;Rui Pan ,&nbsp;Juan Zhou","doi":"10.1016/j.egyai.2025.100612","DOIUrl":"10.1016/j.egyai.2025.100612","url":null,"abstract":"<div><div>Retired batteries for secondary use offer significant economic benefits and environmental value. Accurate sorting of retired batteries with diverse characteristics can further enhance their application efficiency. However, in practical sorting processes, the presence of redundant features, noise interference, and distribution discrepancies in the data severely limits the accuracy of sorting outcomes. To address these challenges, this paper proposes an enhanced retired battery sorting strategy that incorporates feature selection and a clustering algorithm, aiming to optimize the sorting process from the perspective of feature data. To address feature redundancy and high dimensionality issues, this paper proposes an entropy screening method. The Local Outlier Factor algorithm is used to remove anomalous samples. Subsequently, an ensemble clustering approach is developed based on K-means, Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Model, and Spectral clustering, to handle diverse data distributions. The proposed method is validated on 100 retired batteries as well as the large-scale dataset. Additionally, its strong sorting capability and engineering applicability are further demonstrated through carefully designed aging-controlled experiments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100612"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010678","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}