Distinguished Lecture Series 2024/2025

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 four part series I will review recent developments in this fascinating area, including (but not limited to) pre- and postprocessing, calibration window selection, probabilistic forecasting, combining forecasts, deep learning, and economic evaluation.

Date and time

  • 8 (Wed), 10 (Fri), 15 (Wed), and 17 (Fri) January 2025 (*)
  • Calgary 🇨🇦: 🕗 08:00 EST (UTC-7)
  • New York 🇺🇸: 🕙 10:00 EST (UTC-5)
  • Central Europe 🇪🇺: 🕓 16:00 CET (UTC+1)
  • Tokyo 🇯🇵: 🕛 24:00 JST (UTC+9)

(*) Available to IIF members only.

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Lecture slides

I. The art of forecasting electricity prices
  • Markets and products
  • Models and frameworks
  • Feature selection
  • Going deep
II. Tips and tricks
  • Forecast averaging
  • Calibration windows
  • Seasonal decomposition
  • Transformations
III. Beyond point forecasts
  • Postprocessing: Historical simulation and conformal prediction
  • Quantile regression averaging
  • Isotonic distributional regression
  • Distributional forecasts: GAMLSS and DDNN
IV. Trajectories and economic evaluation
  • Path forecasting
  • Trading strategies
  • Battery storage
  • Bonus: The good, the bad, and the ugly of EPF

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Software and data

  • PostForecasts.jl in Julia (maintained by Arkadiusz Lipiecki) – a package for postprocessing point predictions to obtain probabilistic forecasts. See Lipiecki et al. (2024, Energy Economics, doi: 10.1016/j.eneco.2024.107934) for details.
  • 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.

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2023/2024 edition

  • Forecast reconciliation by Rob Hyndman: Over the past 15 years, forecast reconciliation methods have been developed to ensure forecasts are coherent. Forecasts at all levels of aggregation are produced, and the results are "reconciled" so they are consistent with each other. These lectures provide an up-to-date overview of this area, and show how reconciliation methods can lead to better forecasts and better forecasting practices.
  • Videos of all talks are on the IIF YouTube channel.

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