Electricity Load Forecasting

Follow this topic Ask about this topic

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

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

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
May 30, 2026Dahai Yu, Rongchao Xu, Lin Jiang +1Selective SSMs Time Series Forecasting
Florida State University · Tallahassee, Florida, USA
Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
May 27, 2026Ruoyu Hu, Dahai Yu, Feng Bao +2Time Series Forecasting Data Assimilation
Department of Mathematics, Florida State University, Tallahassee, 32306, Florida, USA · Department of Computer Science, Florida State University, Tallahassee, 32306, Florida, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, 37831, Tennessee, USA
Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
May 25, 2026Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Industrial & Systems Engineering Graduate Program (PPGEPS), Federal University of Technology-Parana (UTFPR), Via do Conhecimento, KM 0l—Fraron, Pato Branco 85503-390, PR, Brazil
PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting
May 20, 2026Wangzhi Yu, Peng Zhu, Qing Zhao +2Time Series Forecasting Electricity Load Forecasting
School of Computer Science and Technology, Tongji University, Shanghai, China · Big Data Center, State Grid Corporation of China, Beijing, China
A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation
May 15, 2026Pavan Manjunath, Thomas PrueferElectricity Load Forecasting
Independent Research, India · Independent Research, Germany
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
May 7, 2026Yutong Wang, Yannig Goude, Qiwei YaoEnsemble Learning Time Series Forecasting
Department of Statistics, LSE · Électricité de France R&D
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Apr 30, 2026Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz +3Explainable Time Series Forecasting Time Series Forecasting
Karlsruhe Institute of Technology, Karlsruhe, Germany
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
Apr 26, 2026Md Abubakkar, Sajib Debnath, Md. Uzzal MiaHybrid CNN-Transformer Architectures Time Series Forecasting
Dept. of Computer Science, Midwestern State University, Dallas, TX, USA · O&M Analytics, AES Clean Energy, The AES Corporation, Louisville, CO, USA · Dept. of Information and Communication Engineering, Pabna University of Science and Technology, Bangladesh
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Apr 24, 2026Marco Obermeier, Marco Pruckner, Florian Haselbeck +1Time Series Forecasting Forecasting Benchmarks
Julius-Maximilians-Universität Würzburg, Modeling and Simulation Lab, Am Hubland, Würzburg, 97074, Germany · Weihenstephan-Triesdorf University of Applied Sciences, Smart Farming, Am Staudengarten 1, Freising, 85354, Germany · Weihenstephan-Triesdorf University of Applied Sciences, Digital Energy Transition, Hans-Carl-von-Carlowitz-Platz 3, Freising, 85354, Germany
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
Apr 14, 2026Prasad Nimantha Madusanka Ukwatta Hewage, Hao WuElectricity Load Forecasting Time Series Forecasting
School of Computing and Information Technology Victoria University, Sydney, NSW, Australia
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
Apr 14, 2026Mohammad AlShaikh Saleh, Sanjay Chawla, Sertac Bayhan +2Time Series Forecasting Electricity Load Forecasting
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar · Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, Qatar +1
GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances
Apr 4, 2026Yunhao Yao, Jinwei Fang, Puhan Luo +3Electricity Load Forecasting Time Series Forecasting
University of Science and Technology of China (USTC)
Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand
Mar 13, 2026Danbo Chen, Zijun Zhou, Yongyang Cai +3Power Systems Electricity Load Forecasting
The Ohio State University, Columbus, OH 43210, USA. · Meta Platforms Inc., Menlo Park, CA 94025, USA. · Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.
Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids
Mar 9, 2026Sajib Debnath, Md. Uzzal MiaElectricity Load Forecasting Probabilistic Time Series Forecasting
O&M Analytics, AES Clean Energ; The AES Corporation; Louisville, CO 80027, USA · Department of Information and Communication Engineering; Pabna University of Science and Technology; Rajapur, Pabna, Bangladesh
Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Feb 24, 2026Sunki Hong, Jisoo LeeForecasting Benchmarks Electricity Load Forecasting
Gramm AI
On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Feb 24, 2026Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis +1Electricity Load Forecasting Federated Learning
Department of Informatics, University of Piraeus, Piraeus, Greece · Center for Power and Energy Systems, INESC TEC, Porto, Portugal · Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Feb 21, 2026Suhasnadh Reddy Veluru, Sai Teja Erukude, Viswa Chaitanya MarellaElectricity Load Forecasting Time Series Forecasting
Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India · Department of Electronics and Communication Engineering, Bharat Institute of Engineering and Technology, Hyderabad, Telangana, India

May 30, 2026J/K move · Enter open · S save

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang

Florida State University · Tallahassee, Florida, USA

Selective SSMs Time Series Forecasting Probabilistic Forecasting Conformal Prediction Spatiotemporal Forecasting Electricity Load Forecasting+1

Open paper AskPDF arXiv

Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have been employed for better prediction performance, existing works have two key limitations: (1) they usually formulate this task as a purely time-series prediction problem without explicitly modeling the spatial dependencies among different regions, and (2) they fail to provide reliable predictions with uncertainty estimates under abnormal situations such as extreme weather events. To advance existing research, we propose EnergyMamba, an uncertainty-aware spatiotemporal learning framework for accurate and reliable energy consumption prediction, which comprises two key components: (i) a novel Graph-Enhanced Selective State Space Model (GE-Mamba) that injects spatial context learned from the grid topology into the temporal dynamics, enabling coupled spatiotemporal modeling, and (ii) an Adaptive Sequential Conformalized Quantile Regression (AS-CQR) module, which includes locally adaptive normalization and an online feedback mechanism to dynamically calibrate prediction intervals under potential distribution shifts. We evaluate EnergyMamba on four large-scale real-world datasets from Florida, New York, and California. Results show EnergyMamba achieves around 5% improvement in prediction accuracy and 6% improvement in uncertainty quantification over 15 state-of-the-art baselines.