Electricity Price Forecasting

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Electricity price forecasting estimates prices across day-ahead, intraday, and imbalance markets, where weather, demand, fuel costs, grid constraints, and sudden price spikes make predictions difficult. Common approaches range from statistical models and tree ensembles such as LightGBM and XGBoost to neural networks, probabilistic forecasters, and time-series foundation models; recent work also uses market rules, text, and grid topology as inputs. Researchers evaluate models across bidding zones and changing market regimes, using measures of point accuracy and uncertainty calibration, as well as the value of forecasts for decisions such as battery arbitrage. Foundation models and cross-market transfer can help when data are limited, but they do not consistently beat specialized models, and performance remains sensitive to covariates, distribution shifts, and whether forecast accuracy translates into economic value.

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20 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

The Hidden Look-Ahead: Point-in-Time Leakage in Electricity Market Data
Oct 18, 2026Abdelkrim AlfalahTemporal Data Leakage Electricity Price Forecasting
Goldsmiths, University of London
Cross-Market Meta-Learning for Electricity Price Forecasting
Oct 12, 2026Delaram Pirhayatifard, Terrence AlsupElectricity Price Forecasting Time Series Forecasting
Rice University, Houston, TX · Morgan Stanley, New York, NY
TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text
Sep 30, 2026Xinyi Yi, Zhangdie Yuan, Ioannis LestasElectricity Price Forecasting Time Series Forecasting
Department of Engineering, University of Cambridge · Department of Computer Science, University of Cambridge · University of Cambridge
Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets
Sep 19, 2026Arkadiusz Lipiecki, Nikolaos Kourentzes, Rafal WeronElectricity Price Forecasting Time Series Forecasting
Department of Computational Social Science, Wrocław University of Science and Technology, Poland · Indicio Technologies AB, Stockholm, Sweden · Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Poland
CastClaw: A Human-in-the-Loop Autonomous Agent for Interactive Time Series Forecasting
Aug 31, 2026Xiaoyu Tao, Mingyue Cheng, Ze Guo +4Electricity Price Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China
Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?
Aug 31, 2026Arkadiusz Lipiecki, Rafał WeronTabular Foundation Models Electricity Price Forecasting
Department of Computational Social Science, Wrocław University of Science and Technology, Poland · Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Poland · Center for Research in Energy (CoRE), Aarhus University, 8000 Aarhus C, Denmark
A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting
Aug 26, 2026Yufan Ji, Abdollah Shafieezadeh, Noah DormadyElectricity Price Forecasting Explainable Time Series Forecasting
Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA · John Glenn College of Public Affairs, The Ohio State University, Columbus, 43210, OH, USA
Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
Aug 17, 2026Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn +2Electricity Price Forecasting Time Series Forecasting
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany · Department of Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
Aug 11, 2026Hang Fan, Wei Wei, Shengwei MeiElectricity Price Forecasting Time Series Forecasting
Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?
Jul 7, 2026Clemens Kortmann, Eike CramerElectricity Price Forecasting Adversarial Attacks
Process Systems Engineering (AVT.SVT) RWTH Aachen University · Department of Chemical Engineering Sargent Centre for Process Systems Engineering University College London
Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
Jul 2, 2026Zhenghua Pan, Ahmed Aziz EzzatZero-Shot Time Series Forecasting Electricity Price Forecasting
Department of Industrial & Systems Engineering, Rutgers, The State University of New Jersey, USA.
Analysing drivers and interdependencies in European electricity markets using XAI
Jun 17, 2026Antoine Pesenti, Aidan O'SullivanElectricity Price Forecasting
UCL Energy Institute, University College London, UK
Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
Jun 8, 2026Jan Niklas Lettner, Hadeer El Ashhab, Benjamin SchäferProbability Calibration Electricity Price Forecasting
Institute for Automation and Applied Informatics
Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market
May 21, 2026Houxuan Zhou, Sriram Prasad, Chenghao Huang +2Electricity Price Forecasting Time Series Forecasting
Department of Data Science and AI, Faculty of IT, Monash University, Australia · School of Electrical Engineering and Computer Science, University of Queensland, Australia · Monash Energy Institute, Monash University, Australia
Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
May 16, 2026Chi Bui, Maria Margarida Mascarenhas, Arnaud Verstraeten +1Zero-Shot Time Series Forecasting Electricity Price Forecasting
ELECTA-ESAT (KU Leuven) Leuven, Belgium · ELECTA-ESAT(KU Leuven) and Gridual Leuven, Belgium
A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
May 9, 2026Runyao Yu, Julia Lin, Derek W. Bunn +6Electricity Price Forecasting Time Series Forecasting
Delft University of Technology · Austrian Institute of Technology · London Business School +2
Electricity price forecasting across Norway's five bidding zones in the post-crisis era
Apr 29, 2026My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha +1Electricity Price Forecasting Time Series Forecasting
Independent researcher · Department of Mathematics, University of Oslo · Department of Computer Science, The Arctic University of Norway +1
Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
Apr 26, 2026Wei Lu, Jay Wang, Dingli Duan +3Gradient-Boosted Decision Trees Electricity Price Forecasting
School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology (AUT), Auckland 1010, New Zealand · Department of Thermal Energy and Power Engineering, Yantai University, Yantai, China · Department of Building Environment and Equipment, School of Civil Engineering, Hefei University of Technology, Hefei, China +1
Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Apr 16, 2026Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer +1Electricity Price Forecasting Time Series Forecasting
Karlsruhe Institute of Technology · Karlsruhe, Germany
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
Mar 6, 2026Yunzhong Qiu, Binzhu Li, Hao Wei +5Electricity Price Forecasting Time-Series Foundation Models
School of Software, BNRist, Tsinghua University, Beijing, China · Suzhou Industrial Park Xingzhixun CS Co., Ltd., Jiangsu, China

Oct 18, 2026J/K move · Enter open · S save

The Hidden Look-Ahead: Point-in-Time Leakage in Electricity Market Data

Abdelkrim Alfalah

Goldsmiths, University of London

Temporal Data Leakage Electricity Price Forecasting

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Empirical studies of electricity prices routinely state that their models use only information available at the time of the trading decision. For weather-driven forecasts drawn from commercial archives, this condition is harder to meet than it appears, and whether it holds can change over time. We identify three timing pitfalls. A forecast run is labelled by the issue time of its weather model, but becomes available hours later and keeps changing after publication. Auction gates follow local clock time, while archives use UTC. Outturn series are revised after publication without earlier values being kept. Using the timestamps of 128,656 forecast runs from ten weather-model configurations, matched to the day-ahead and first intraday auction gates in Great Britain over 2022-2026, we find that selecting "the latest run issued before the gate" picks a run not yet published at the gate on more than 99% of delivery days for six of the ten configurations at one of the two gates. For GFS at the day-ahead gate, the share is 0.6% in winter and 99.8% in summer. The exposure also shifts as publication delays drift, and an action margin of a few minutes can cost a full six-hour forecast cycle. The wrongly selected inputs differ materially from those available at the gate, but their apparent accuracy gain ranges from -9% to +24% across variables, models, gates and seasons, so its effect cannot be assumed and must be measured. We argue that point-in-time correctness is a dynamic property of the data pipeline, and propose a protocol that audits it from timestamps before any model is fitted.