Electricity Load Forecasting

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations
Oct 6, 2026Haoran Li, Zhe Cheng, Yang WengElectricity Load Forecasting Probabilistic Time Series Forecasting
Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty
Oct 5, 2026Tomas Kaljevic, Ivan Arzola, Yu ZhangZero-Shot Time Series Forecasting Time Series Forecasting
Department of Electrical and Computer Engineering University of California, Santa Cruz
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 Forecasting Cross-Attention
Sophia University Tokyo, Japan
Automated feature engineering, AutoML, and decision-focused learning for improved energy consumption forecasting
Sep 28, 2026Nasser AlkhulaifiAutomated Machine Learning Electricity Load Forecasting
School of Computer Science, Faculty of Science University of Nottingham
Probabilistic electrical power demand forecasting with uncertainty quantification
Sep 28, 2026Mahesh Neupane, Pragya Dhungana, Pradip Khatri +2Uncertainty Quantification Probabilistic 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
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
Sep 21, 2026Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1Time Series Forecasting Electricity Load Forecasting
CB · Centre Borelli, ENS Paris-Saclay, Gif-sur-Yvette, France · EDF Lab, Palaiseau, France +4
Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels
Sep 16, 2026Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening +5Time Series Forecasting Electricity 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
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 Forecasting Forecasting Benchmarks
THK-AI Research Cluster, Germany · TH Köln, Germany
SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting
Jul 22, 2026Hang Ye, Xinyan Jiang, Yuedong Shi +5Probabilistic Forecasting Electricity 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
Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting
Jul 17, 2026Ramin Soleimani, Andrea Visentin, Dirk PeschNeural Processes Time Series Forecasting
School of Computer Science and Information Technology, University College Cork, Cork, Ireland
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 Forecasting Forecasting Benchmarks
Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany
Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
Jul 14, 2026Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok JeonQuantile Regression Time 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
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 Forecasting Electricity Load Forecasting
Karlsruhe Institute of Technology (KIT), Germany and Netze BW GmbH, Germany · Netze BW GmbH, Germany · Karlsruhe Institute of Technology (KIT), Germany
Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Jun 22, 2026Jinhao Li, Hao WangMultivariate Time Series Forecasting Time 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
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 Forecasting Concept Drift
Department of Chemical and Materials Engineering, New Mexico State University · Department of Electrical and Communications Engineering, New Jersey Institute of Technology
Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models
Jun 16, 2026Vansh BansalTime Series Forecasting Non-Stationary Time Series Forecasting
Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems
Jun 11, 2026Mansi Od, Param Pathak, Nouhaila Innan +1Time Series Forecasting Reservoir 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
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 Forecasting Synthetic-to-Real Domain Adaptation
Department of Computer Science University of Kentucky Lexington, KY 40506
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

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

Haoran Li, Zhe Cheng, Yang Weng

Electricity Load Forecasting Probabilistic Time Series Forecasting Parameter-Efficient Fine-Tuning

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Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introduces a distinct scalability challenge. At these levels, load uncertainty is strongly affected by customer behavior, weather, and mixed load composition, making it difficult for a single shared model to capture heterogeneous patterns. Using separate probabilistic models can improve local accuracy, but becomes costly to train, store, update, and validate at scale. To address this challenge, we develop a scalable customer-aware forecasting framework that learns common demand behavior through a shared model while adapting only a compact subset of parameters. Rather than using an independent model for each load or assigning each load to a specialized model, the proposed design learns a small bank of low-dimensional adaptation components and allows each load to combine them according to its forecasting characteristics. This preserves shared knowledge across customers while providing sufficient flexibility for heterogeneous and mixed load compositions. Experiments on 590 load profiles from the SMART-DS dataset show consistent improvements in deterministic accuracy and probabilistic quality over statistical, neural-network, Transformer-based, and pretrained time-series baselines, while retaining low storage and inference costs.

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