Electricity Load Forecasting

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Overview

The field predicts electricity demand at different time scales, from household and building loads to regional grids, helping operators balance supply, plan capacity, and manage peaks. Methods range from statistical models and gradient-boosted trees to recurrent networks, Transformers, state space models, and graph or physics-informed approaches; weather, calendar, economic, and behavioral data can add useful context. Probabilistic forecasts, online adaptation, and shared or pretrained models address uncertainty, changing demand patterns, and settings with limited or distributed data. Benchmarks show that no architecture wins in every setting: performance depends on forecast horizon, covariate reliability, and distribution shifts, while operational use also requires calibrated uncertainty, robust peak predictions, interpretability, and manageable computational cost.

Latest papers

CardsList

47 papers10 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

  1. A Diagnostic Protocol for GNNs in Electricity Load Forecasting: Separating Parameter Sharing from Neighborhood Access

    Oct 26, 2026Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2Graph Neural NetworksElectricity Load Forecasting

    CB · LMO

  2. A Model-Agnostic Correction Framework for Short-Term Building Load Forecasting

    Oct 22, 2026Shiqiao Zhou, Huanbo Lyu, Holger Schöner +5Electricity Load ForecastingTime Series Forecasting

    School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, West Midlands, United Kingdom · Siemens AG, Munich, 80333, Bavaria, Germany · School of Mathematics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, West Midlands, United Kingdom +1

  3. Extreme Low-State Quantized Transformer for Electricity-Load Forecasting

    Oct 20, 2026Muhammad Sabbir Alam, Harindra Sandun Mavikumbure, Victor Cobilean +2Low-Bit QuantizationQuantization-Aware Training

    Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA · Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA · Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA

  4. Parameter-Efficient Adaptation of Time Series Foundation Models for Electrical Load Forecasting

    Oct 20, 2026Aliullah, Julian Oelhaf, Edouard Fouché +4Electricity Load ForecastingTime-Series Foundation Models

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Siemens AG, Smart Infrastructure · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden

  5. Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

    Oct 8, 2026Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw +1Electricity Load ForecastingForecasting Benchmarks

  6. From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations

    Oct 6, 2026Haoran Li, Zhe Cheng, Yang WengElectricity Load ForecastingProbabilistic Time Series Forecasting

  7. Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty

    Oct 5, 2026Tomas Kaljevic, Ivan Arzola, Yu ZhangZero-Shot Time Series ForecastingTime Series Forecasting

    Department of Electrical and Computer Engineering University of California, Santa Cruz

  8. When Attention Does Not Explain the Peak: Temporal Reference vs. Forecast Output in Attention-Based Time-Series Forecasting

    Oct 5, 2026Yuji Akamatsu, Takao YamanakaExplainable Time Series ForecastingCross-Attention

    Sophia University, Tokyo, Japan

  9. Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result

    Oct 1, 2026Yixuan LiangElectricity Load ForecastingTime Series Imputation

    Independent researcher

  10. Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting

    Sep 28, 2026Nasser AlkhulaifiAutomated Machine LearningElectricity Load Forecasting

    School of Computer Science, Faculty of Science University of Nottingham

  11. Probabilistic electrical power demand forecasting with uncertainty quantification

    Sep 28, 2026Mahesh Neupane, Pragya Dhungana, Pradip Khatri +2Uncertainty QuantificationProbabilistic Forecasting

    Interconnection and Numbering Section Nepal Telecommunications Authority Kathmandu, Nepal · Nepal Electricity Authority Kathmandu, Nepal · Department of Global Public Health and Primary Care University of Bergen Bergen, Norway +1

  12. Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model

    Sep 25, 2026Lindas Eloi, Goude Yannig, Ciais PhilippeElectricity Load ForecastingMultivariate Time Series Forecasting

    Atos Inno’Lab TS Bezons, Bezons, France · Laboratoire des Sciences du Climat & de l’Environnement, CEA, CNRS, UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France · EDF R&D Lab, EDF, Palaiseau, France +1

  13. GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

    Sep 21, 2026Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1Time Series ForecastingElectricity Load Forecasting

    CB · LMO, CELESTE · EDF R\&D +1

  14. Comparative review of hybrid forecasting models for short-term prediction of building thermal load

    Sep 19, 2026Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. ChristoforidisElectricity Load ForecastingTime Series Forecasting

    VIVAVIS Schweiz AG - 5405 Baden, Switzerland · Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece

  15. Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

    Sep 16, 2026Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening +5Time Series ForecastingElectricity Load Forecasting

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Siemens AG, Smart Infrastructure · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden

  16. Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

    Sep 6, 2026Varsha Pendyala, Yiwei Fu, Weizhong Yan +1Exogenous Time Series ForecastingElectricity Load Forecasting

    Artificial Intelligence Team GE Vernova Advanced Research Center, Niskayuna, NY, USA

  17. Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

    Aug 28, 2026Jianing Chen, Vajiheh Farhadi, Yan Li +1Electricity Load ForecastingFederated Learning

    School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA · Department of Electrical and Computer Engineering, Bucknell University, Lewisburg, PA 17837, USA

  18. A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads

    Aug 25, 2026Chenghan Li, Qingxiang Liu, Yinliang Xu +1Probabilistic ForecastingElectricity Load Forecasting

    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China

  19. An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting

    Aug 19, 2026Behnaz Kavoosighafi, Maria Eidenskog, Wiktoria Glad +1Time Series ForecastingForecasting Benchmarks

    Linköping University, Department of Science and Technology, Norrköping, 60174, Östergötland, Sweden · Linköping University, Department of Thematic Studies, Linköping, 58183, Östergötland, Sweden

  20. Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load

    Aug 5, 2026Thomas Bartz-Beielstein, Inalbek Akiev, Lalo MohamadTime Series ForecastingForecasting Benchmarks

    THK-AI Research Cluster, Germany · TH Köln, Germany

  21. SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

    Jul 22, 2026Hang Ye, Xinyan Jiang, Yuedong Shi +5Probabilistic ForecastingElectricity Load Forecasting

    Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China · University of Chinese Academy of Sciences, Beijing 101408, China · School of Physical Science and Technology, Shanghaitech University, Shanghai 201210, China +2

  22. Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting

    Jul 17, 2026Ramin Soleimani, Andrea Visentin, Dirk PeschNeural ProcessesTime Series Forecasting

    School of Computer Science and Information Technology, University College Cork, Cork, Ireland

  23. A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

    Jul 17, 2026Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3Time Series ForecastingForecasting Benchmarks

    Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany

  24. Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

    Jul 14, 2026Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok JeonQuantile RegressionTime Series Forecasting

    aComputer System and Technology Department, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia · bInformatics Institute, Istanbul Technical University, Turkey · cComputer Engineering Department Konkuk University South Korea

  25. Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

    Jul 2, 2026Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber +5Time Series ForecastingElectricity Load Forecasting

    Karlsruhe Institute of Technology (KIT), Germany and Netze BW GmbH, Germany · Netze BW GmbH, Germany · Karlsruhe Institute of Technology (KIT), Germany

  26. Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting

    Jun 22, 2026Jinhao Li, Hao WangMultivariate Time Series ForecastingTime Series Forecasting

    Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia · Monash Energy Institute, Monash University, Melbourne, Victoria, Australia

  27. Short-Term Electricity Demand Forecasting for New England Using a Hybrid Transformer-XGBoost Framework with Weather, Calendar, and COVID-19 Indicators

    Jun 18, 2026Reza Ghanavati, Behrooz MosallaeiMultivariate Time Series ForecastingConcept Drift

    Department of Chemical and Materials Engineering, New Mexico State University · Department of Electrical and Communications Engineering, New Jersey Institute of Technology

  28. Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

    Jun 16, 2026Vansh BansalTime Series ForecastingNon-Stationary Time Series Forecasting

  29. Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems

    Jun 11, 2026Mansi Od, Param Pathak, Nouhaila Innan +1Time Series ForecastingReservoir Computing

    University of Greenwich, London, United Kingdom · Fractal Analytics, QuantumAI Lab, Mumbai, Maharashtra, India · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE +1

  30. RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

    Jun 1, 2026Jainam Dhruva, Yousaf Raza, A. B. Siddique +1Multivariate Time Series ForecastingSynthetic-to-Real Domain Adaptation

    Department of Computer Science University of Kentucky Lexington, KY 40506