CoRE Lectures 2026
Electricity price forecasting
Electricity price forecasting (EPF) is a branch of energy forecasting on the interface between econometrics/statistics, computer science and engineering, which focuses on predicting the spot and forward prices in wholesale electricity markets. Over the last 25 years, a variety of methods and ideas have been tried for EPF, with varying degrees of success. In this short course I will review recent developments in this fascinating area, including (but not limited to) pre- and postprocessing, (semi)automated feature selection, calibration window selection, probabilistic forecasting, combining forecasts, temporal reconciliation, deep learning, foundation models, and economic evaluation.
Date, time and venue
- Part I: 17 (Mon) August 2026, 13:00-15:30
- Part II: 18 (Tue) August 2026, 09:00-11:30
- Venue: CoRE - Center for Research in Energy: Economics and Markets, School of Business and Social Sciences, Aarhus University 🇩🇰
Lecture slides & Julia/Python notebooks
(To be posted before the course)
Electricity price forecasting I
- Intro
- Energy forecasting literature
- Power markets across the globe
- Model taxonomy
- 'Toy' models
- The forecasting setup
- Naive models
- (Auto)regressive models
- Shallow neural networks
- Exponential smoothing models
- Supply stack models
- Beyond point forecasts
- Probabilistic forecasts
- Reliability & sharpness
- Postprocessing point forecasts
- Historical simulation
- Conformal prediction
- Forecast accuracy
- Absolute and square errors
- Percentage errors
- Scaled and relative errors
- Testing for coverage
- CRPS and the pinball score
- DM-type tests
- Tips and tricks
- Transformations
- Seasonal decomposition
- Combining forecasts
- Averaging across calibration windows
- Calibration window selection
Electricity price forecasting II
- Lasso, DNN and beyond
- Stepwise regression
- Shrinkage (regularization)
- LASSO-Estimated AR (LEAR)
- Deeper and deeper
- Interpretable AI
- Foundation models
- Temporal reconciliation
- Predicting block prices
- Predicting price spreads
- Minimum trace reconciliation
- Information combination
- Probabilistic forecasts revisited
- Quantile Regression Averaging
- Isotonic Distributional Regression
- PostForecasts.jl
- Combining probabilistic forecasts
- GAMLSS
- Distributional Deep Neural Nets
- Probabilistic inputs
- Financial evaluation
- Day-ahead bidding with BESS
- Beyond RMSE and MAE
- Which loss function to minimize?
Notebooks by Arkadiusz Lipiecki
- Repository with notebooks that illustrate the concepts covered in Rafał Weron's CoRE Lectures: To be posted before the course
Software and data
- PostForecasts.jl in Julia (maintained by Arkadiusz Lipiecki) – a package for postprocessing point predictions to obtain probabilistic forecasts. See Lipiecki & Weron (2025, SoftwareX, doi: 10.1016/j.softx.2025.102200) for details. See also Lipiecki et al. (2024, Energy Economics, doi: 10.1016/j.eneco.2024.107934).
- DistributionalNN in Python (maintained by Grzegorz Marcjasz) - library for training, calibrating and making predictions with distributional deep neural networks (DDNN). See Marcjasz et al. (2023, Energy Economics, doi: 10.1016/j.eneco.2023.106843) for details.
- NBEATSx in Python (maintained by Christian Challu) - library that extends the NBEATS model to incorporate exogenous factors (includes a notebook with examples: nbeatsx_example.ipynb). See Olivares et al. (2023, International Journal of Forecasting, doi: 10.1016/j.ijforecast.2022.03.001) for details.
- epftoolbox in Python (maintained by Jesus Lago) - library providing codes for training LEAR and DNN models. See Lago et al. (2021, Applied Energy, doi: 10.1016/j.apenergy.2021.116983) for details. The accompanying market data is here.
- tsrobprep in R (maintained by Michal Narajewski) - package for robust preprocessing of time series data
- IDEAS/RePEc repository in Matlab, Python and/or R - codes for LEAR models, Seasonal Component AutoRegressive (SCAR) models, seasonal decomposition, etc.