Energy and AI最新文献

{"title":"Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems","authors":"Xiaojie Lin ,&nbsp;Zheng Luo ,&nbsp;Liuliu Du-Ikonen ,&nbsp;Xueru Lin ,&nbsp;Yihui Mao ,&nbsp;Haoyu Jiang ,&nbsp;Shuai Wang ,&nbsp;Chongshuo Yuan ,&nbsp;Wei Zhong ,&nbsp;Zitao Yu","doi":"10.1016/j.egyai.2025.100610","DOIUrl":"10.1016/j.egyai.2025.100610","url":null,"abstract":"<div><div>Promoting low-carbon energy systems as a centerpiece of global sustainable development goals is essential. As part of this low-carbon transition, smart energy systems have been an active area of research and education, where artificial intelligence (AI) intersects with energy science. It is an emerging area where research and education face new challenges as new knowledge keeps coming in. During this process, generative artificial intelligence (GAI) plays a critical role in education and research activities. However, GAI's impact on smart energy systems research and education is less discussed. Especially, its impact on education is rarely discussed when compared to research. GAI reshapes both the research process and the roles of teachers and students in the course. This perspective offers insights into the ongoing research and education paradigm shifts observed in the smart energy system. This perspective synthesizes existing studies on \"GAI for Science\" and \"GAI for Education\" practices in the field of smart energy systems. In research, the impact of GAI is discussed from both macro and micro levels. In education, this perspective examines how a GAI-driven teaching approach addresses the challenges of teaching smart energy systems compared to the traditional approach. This perspective could benefit the discussion of GAI-reshaped research and education in energy science.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100610"},"PeriodicalIF":9.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007832","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":"Whale algorithm optimized anode pressure controller for fuel cell systems in ejector recirculation mode","authors":"Wenjun Guo ,&nbsp;Renkang Wang ,&nbsp;Yu Qiu ,&nbsp;Linhong Wu ,&nbsp;Kai Li ,&nbsp;Hao Tang","doi":"10.1016/j.egyai.2025.100611","DOIUrl":"10.1016/j.egyai.2025.100611","url":null,"abstract":"<div><div>The anode pressure control in proton exchange membrane fuel cells (PEMFCs) significantly influences the stable operation of the hydrogen supply system and the internal gas circulation within the fuel cell. An efficient anode pressure control strategy is imperative for enhancing the overall system efficiency and mitigating lifespan degradation. Effective anode pressure control can prevent hydrogen starvation and instability in output performance under rapid load changes and purge disturbances. Fuzzy control has been extensively employed in anode pressure control studies. However, creating fuzzy rules in the control parameter’s tuning process in existing studies is predominantly dependent on expert knowledge, resulting in concerns about control accuracy. This study investigates the potential of employing the whale optimization algorithm to optimize the selection of fuzzy parameters. We first developed a control-oriented model to address the nonlinearity, coupling, and uncertainty in the hydrogen supply system. Then, based on the model and considering load variations and purge disturbances, we integrated feedforward compensation and fuzzy control into the conventional Proportional-Integral (PI) controller to suppress input disturbances, enhance control accuracy, and reduce the pressure response lag. Finally, an innovative fuzzy PI controller with the whale optimization algorithm is proposed to optimize the fuzzy parameter selection, thereby achieving precise anode pressure control. Simulation tests demonstrate that the whale-optimization-based fuzzy PI control (WFLPIF) reduces a root mean square error by 14.3 % (0.636 vs. 0.742) and a mean absolute percentage error by 28.8 % (0.037 vs. 0.052) compared to conventional PI control, while also outperforming feedforward-compensated fuzzy PI control (FLPIF) by 9.5 % in RMSE and 17.8 % in MAPE. This study substantiates the efficacy of the whale optimization algorithm in addressing the anode pressure stability control challenge of fuel cell hydrogen supply systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100611"},"PeriodicalIF":9.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007631","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":"AI challenge for safe and low carbon power grid operation","authors":"Adrien Pavão ,&nbsp;Antoine Marot ,&nbsp;Jules Sintes ,&nbsp;Viktor Eriksson Möllerstedt ,&nbsp;Laure Crochepierre ,&nbsp;Karim Chaouache ,&nbsp;Benjamin Donnot ,&nbsp;Van Tuan Dang ,&nbsp;Isabelle Guyon","doi":"10.1016/j.egyai.2025.100564","DOIUrl":"10.1016/j.egyai.2025.100564","url":null,"abstract":"<div><div>Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability. To tackle this systems-level bottleneck, Réseau de Transport d’Électricité (RTE) and the research community launched Learn To Run A Power Network (L2RPN), a crowd-sourced competition aiming to accelerate the integration of intermittent renewables into power-grid operations. L2RPN is based on 16 years of weekly scenarios (832 in total) on a 118-node grid under realistic constraints, and casts real-time grid operation as a Markov-Decision-Process. The six participating teams tackled the challenge by developing autonomous agents with various strategies blending heuristics, optimization, data scaling, supervised learning, and reinforcement learning. We provide a detailed overview of all six participants’ performance under the competition’s demanding design. In addition, we present an in-depth analysis of the winning solution – made publicly available – which achieves consistent decision making across scenarios, executes real-time multimodal actions in under five seconds, and performs efficient topology control via action-space reduction and a neural policy that predicts useful grid actions with over 80% accuracy. In parallel, we trained a neural alert module on 315,000 samples derived from top agents, achieving 93.9% recall in flagging dangerous states and allowing agents to predict future failure. Finally, this work not only demonstrates AI’s promise and current limits in real-time grid management but also lays a transparent foundation for more robust, trustworthy systems in the energy transition.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100564"},"PeriodicalIF":9.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988855","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 framework for vehicle charging pattern recognition and charging duration prediction based on EA-CAE and K-means clustering","authors":"Yuemeng Zhang ,&nbsp;Longqin Guo ,&nbsp;Zeqian Chen ,&nbsp;Hongtao Yan ,&nbsp;Le Liang ,&nbsp;Chunjing Lin","doi":"10.1016/j.egyai.2025.100599","DOIUrl":"10.1016/j.egyai.2025.100599","url":null,"abstract":"<div><div>Accurate prediction of electric vehicle (EV) charging duration is critical for improving user satisfaction and enabling efficient real-time charging management. This paper proposes a dynamic charging duration prediction framework for EVs, composed of four coordinated modules: data preprocessing, charging pattern classification, static prediction, and dynamic bias correction. First, raw charging data collected from the Battery Management System (BMS) is cleaned and normalized to address missing and abnormal values. An enhanced convolutional autoencoder (EV-CAE) is then employed to extract multi-scale temporal features, while K-Means clustering is used to identify representative charging behavior patterns. Based on the classified patterns, the static prediction module estimates the current charging duration by leveraging historical data and pattern labels. To enhance adaptability under dynamic conditions, a bias correction mechanism is designed, integrating linear, logarithmic, proportional, and deep learning-based strategies to adjust the prediction results in real time. Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy. In particular, the dynamic correction module increases the coefficient of determination (R²) from 0.948 to 0.960, while maintaining robust performance under fluctuating charging behavior and low-temperature conditions. These results validate the practical applicability and engineering potential of the proposed method for real-time charging duration estimation in intelligent EV charging systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100599"},"PeriodicalIF":9.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045868","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":"Neural-accelerated numerical model for packed bed latent heat storage system","authors":"Dessie Tadele Embiale ,&nbsp;Shri Balaji Padmanabhan ,&nbsp;Mohamed Tahar Mabrouk ,&nbsp;Stéphane Grieu ,&nbsp;Bruno Lacarrière","doi":"10.1016/j.egyai.2025.100602","DOIUrl":"10.1016/j.egyai.2025.100602","url":null,"abstract":"<div><div>Developing accurate and computationally efficient dynamic models for packed-bed latent-heat storages (PBLHS) is crucial for reliably predicting their performance across different operating scenarios and enabling their use in planning and real-time control. In this study, a novel neural-accelerated numerical model for PBLHS is proposed by coupling a neural network (NN) into a coarsely discretized equations of the Continuous-solid Phase (CP) model. The embedded NN predicts the surface temperature of the phase change material (PCM) given the fluid temperature and enthalpy of the PCM as inputs, which the CP model fails to capture. This allows the neural-accelerated model to replicate the accuracy of a high-fidelity and computationally expensive model namely Concentric Dispersion (CD) model. An innovative data generation process to generate training data for NN involving both CD and CP model is proposed. Two versions of neural-accelerated model are proposed, one with conventional NN and another using NN with a custom activation function. Both versions demonstrate an excellent accuracy, achieving MSE as low as 0.117 °C, <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> values closer to 0.995 and error percentage below 0.394<span><math><mo>%</mo></math></span> compared to the highly accurate CD model. As for computational efficiency, the proposed models achieved 342 times and 764 times acceleration respectively. The gain in more acceleration for the later version of the proposed model is achieved through the use of a compact architecture that benefits from the custom activation function, while also enhancing model explainability. These results highlight the model’s suitability for scenarios demanding both high accuracy and computational efficiency.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100602"},"PeriodicalIF":9.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921292","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 hybrid monthly electricity demand forecasting model combining an Hodrick-Prescott filter, recurrent neural networks, and autoregressive integrated moving average","authors":"Zhenyu Su,&nbsp;Juan Zhang,&nbsp;Zhehan Yang,&nbsp;Leihao Ma","doi":"10.1016/j.egyai.2025.100600","DOIUrl":"10.1016/j.egyai.2025.100600","url":null,"abstract":"<div><div>The coexistence of growth trends and seasonal fluctuations in monthly electricity demand presents significant forecasting challenges. Therefore, this study proposes a univariate time series forecasting approach that applies the Hodrick-Prescott (HP) filter to decompose the demand series into trend and seasonal components. Autoregressive integrated moving average (ARIMA) is used to forecast the trend, while recurrent neural networks (RNNs) handle the periodic component. The final prediction is obtained by combining the forecasts of both components. The model’s predictive performance is evaluated using Guangzhou’s total electricity consumption data. Compared to traditional methods such as Holt-Winters, Seasonal ARIMA, and error-trend-seasonal (ETS), the proposed HP_RNN_ARIMA hybrid model reduces mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) by approximately 9.70 % to 35.66 %, 14.18 % to 35.06 %, and 20.01 % to 41.92 %, respectively. Compared to standalone neural networks such as backpropagation (BP), RNNs, and long short-term memory (LSTM), the proposed model lowers MAPE, RMSE, and MAE by approximately 9.05 % to 44.02 %, 20.88 % to 51.74 %, and 29.53 % to 56.23 %, respectively. Against other hybrid models, it reduces these metrics by 3.60 % to 33.39 %, 4.27 % to 36.67 %, and 4.43 % to 44.87 %. It also achieves the highest Willmott’s index (WI) and Legates and McCabe’s index (LMI) scores, reflecting superior model fit. Moreover, applying the HP filter for decomposition and modeling each component individually significantly improves forecasting accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100600"},"PeriodicalIF":9.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919974","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":"Intelligent coalbed methane drainage optimization: A deep reinforcement learning-driven life-cycle strategy","authors":"Chen Liu ,&nbsp;Bin Gong ,&nbsp;HaoQiang Wu ,&nbsp;Hu Huang ,&nbsp;Heng Zhao","doi":"10.1016/j.egyai.2025.100598","DOIUrl":"10.1016/j.egyai.2025.100598","url":null,"abstract":"<div><div>Coalbed methane (CBM) production, as a significant portion of unconventional natural gas development, plays a crucial role in enhancing output and economic benefits through the optimization of its life-cycle drainage strategy. Traditional drainage strategies rely on experience and trial-and-error methods, making it difficult to adapt to complex and dynamic production environments. This study proposes a life-cycle production and drainage optimization strategy for CBM based on Deep Reinforcement Learning (DRL). Utilizing the Deep Q-Network (DQN) algorithm, this work learns and optimizes the drainage strategy during the production process, achieving intelligent decision-making for drainage operations. An auto-regressive surrogate model is introduced to build a reinforcement learning environment for gas production and drainage, based on a deep learning model (CNN-LSTM). This model substitutes the full-physics simulation model that requires high computational cost, significantly accelerating the interactive learning process between the agent and the environment in DRL. Whether to set the gas production or Net Present Value (NPV) as reward, two strategies for reinforcement learning were considered accordingly. The results concluded that the DRL drainage strategy with NPV as the reward increased the net gain by 5.83 % compared to historical data. Compared with traditional methods, this approach significantly improves the NPV and optimizes the drainage strategy. The findings demonstrate that the life-cycle drainage optimization method for CBM based on DRL is not only efficient and feasible but also provides an intelligent solution for the development of unconventional natural gas resources. The results highlight the method's strong adaptability and potential for addressing complex optimization problems in dynamic production environments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100598"},"PeriodicalIF":9.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919975","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":"The pyxis project: A geospatial data system for emission estimation monitoring in the oil and gas industry","authors":"Yaqi Fan ,&nbsp;Mohammad S. Masnadi ,&nbsp;Liang Jing ,&nbsp;Bo Ren ,&nbsp;Adam R. Brandt","doi":"10.1016/j.egyai.2025.100601","DOIUrl":"10.1016/j.egyai.2025.100601","url":null,"abstract":"<div><div>Consistent estimation and monitoring of greenhouse gas (GHG) emissions in the Oil and Gas (O&amp;G) industry is challenging due to inaccessible, fragmented, and unstandardized datasets. Earlier efforts in estimating such emissions required extensive manual analysis to harmonize diverse data sources on O&amp;G operations. Also, these analyses depend on flaring and methane leakage datasets, which should ideally be updated in near real-time, challenging to integrate effectively to process models. To tackle these challenges, this study proposes a Geographic Information System (GIS)-based data platform called Pyxis for integrating and managing data input associated with GHG emissions estimates in the O&amp;G sector. The Pyxis architecture includes a scalable geodatabase for source management and an automated data pipeline for data management using spatial indexing. This greatly reduces the manual labor traditionally needed for data matching and merging. In addition, top-down remote sensing data can be seamlessly associated with bottom-up field operations data through Pyxis, which improves data recency and spatiotemporal coverage. Here, we apply Pyxis to the O&amp;G fields of Brazil as a case study to show how it can help generating accurate estimates of Carbon Intensity (CI) with data management among disparate and inconsistent data sources. This work highlights the potential of scaling up Pyxis globally via integrating artificial intelligence models for data extraction and ultimately becoming a valuable tool for GHG emissions monitoring and policymaking in the O&amp;G industry.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100601"},"PeriodicalIF":9.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921144","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":"Intelli-Dispatch-SQL: An LLM-based agent for reliable Text-to-SQL in power dispatching","authors":"Binye Ni ,&nbsp;Xinlei Cai ,&nbsp;Zhijun Shen ,&nbsp;Zijie Meng ,&nbsp;Junhua Zhao ,&nbsp;Yuheng Cheng ,&nbsp;Xuanang Gui","doi":"10.1016/j.egyai.2025.100591","DOIUrl":"10.1016/j.egyai.2025.100591","url":null,"abstract":"<div><div>The increasing complexity of modern power systems, driven by factors such as the large-scale integration of renewable energy and the proliferation of distributed generation, has placed unprecedented demands on power dispatching operations. Ensuring grid stability and safety in this new environment requires real-time monitoring and swift, data-driven decision-making. Consequently, efficient and accurate data querying capabilities have become paramount. This study introduces Intelli-Dispatch-SQL, a novel agent-based Text-to-SQL framework that leverages the Large Language Model (LLM) to enhance the accuracy and reliability of generated SQL queries in the context of power dispatching. By integrating intent recognition and SQL validation modules, Intelli-Dispatch-SQL ensures that generated queries are not only syntactically correct but also semantically aligned with user intent and executable within the operational context. Through comprehensive experiments, including ablation studies and cross-model evaluations, we demonstrate that Intelli-Dispatch-SQL significantly outperforms existing Text-to-SQL models, achieving substantial improvements in both Exact Match (EM) and Execution Accuracy (EX). Notably, the incorporation of intent recognition and SQL validation modules is shown to be critical for performance enhancement. The framework’s effectiveness was further validated across various LLMs, confirming its robustness and applicability across diverse scenarios. Intelli-Dispatch-SQL offers a high-performance and generalizable solution for Text-to-SQL in power dispatching, paving the way for more efficient and intelligent power system management.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100591"},"PeriodicalIF":9.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908072","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 wind speed prediction in wind farms through ordinal classification","authors":"A.M. Gómez-Orellana ,&nbsp;M. Vega-Bayo ,&nbsp;D. Guijo-Rubio ,&nbsp;J. Pérez-Aracil ,&nbsp;V.M. Vargas ,&nbsp;P.A. Gutiérrez ,&nbsp;L. Prieto-Godino ,&nbsp;S. Salcedo-Sanz ,&nbsp;C. Hervás-Martínez","doi":"10.1016/j.egyai.2025.100596","DOIUrl":"10.1016/j.egyai.2025.100596","url":null,"abstract":"<div><div>This paper presents and evaluates two novel ordinal classification methods for wind speed prediction, considering three prediction time-horizons: 1h, 4h, and 8h. To address the problem, wind speed values are discretised into four classes, critical for wind farm management. Each class represents essential information for wind farm production, ranging from very low wind speeds to extreme wind speed events and the corresponding production conditions, facilitating operational decisions for wind farm operators. Ordinal classifiers are more suitable than nominal methods to tackle this problem. The study’s primary objective is to compare recently proposed ordinal classifiers for addressing the challenges of wind speed prediction with a focus on extreme wind conditions, which are responsible for many turbine shutdowns. Hourly wind speed measurements from a Spanish wind farm and predictor variables from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5 Reanalysis) model are used. The proposed methods include an Artificial Neural Network (ANN) model implementing the Cumulative Link Model as an ordinal output function (<span><math><msup><mrow><mtext>MLP-CLM</mtext></mrow><mrow><mtext>O</mtext></mrow></msup></math></span>), which emphasises overall performance, and an ANN model optimised using a soft labelling technique based on triangular distributions (<span><math><msup><mrow><mtext>MLP-T</mtext></mrow><mrow><mtext>O</mtext></mrow></msup></math></span>), which excels at handling extreme class performance. The results demonstrate the superiority of both approaches over other nominal and ordinal methods across performance metrics that account for the unbalanced nature and ordinality of the data. <span><math><msup><mrow><mtext>MLP-CLM</mtext></mrow><mrow><mtext>O</mtext></mrow></msup></math></span> excels in overall and ordinal performance, while <span><math><msup><mrow><mtext>MLP-T</mtext></mrow><mrow><mtext>O</mtext></mrow></msup></math></span> demonstrates superior handling of the extreme class predictions.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100596"},"PeriodicalIF":9.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908071","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}