EPFTOOLBOX: The First Open-Access PYTHON Library for Driving Research
in Electricity Price Forecasting (EPF)
Reference
J. Lago,
G. Marcjasz,
B. De Schutter, and
R. Weron,
"EPFTOOLBOX: The First Open-Access PYTHON Library for Driving Research
in Electricity Price Forecasting (EPF)," WORMS Software (WORking
papers in Management Science Software) WORMS/C/21/01, Department of
Operations Research and Business Intelligence, Wroclaw University of
Science and Technology, Wroclaw, Poland, 2021.
Abstract
The library includes three distinct modules. (1) The DATA MANAGEMENT
module provides functionality to manage, process, and obtain data for
EPF. The module also provides access to data from five different
day-ahead electricity markets: EPEX-BE, EPEX-FR, EPEX-DE, NordPool,
and PJM. (2) The MODELS module grants access to state-of-the-art
forecasting methods for day-ahead electricity prices - the
Lasso-Estimated AutoRegressive (LEAR) model and the Deep Neural
Network (DNN) model - that require no expert knowledge and can be
automatically employed. (3) The EVALUATION module provides with an
easy-to-use interface for evaluating forecasts in EPF. This module
includes both scalar metrics like MAE or MASE as well as statistical
tests to evaluate the statistical significance in forecasting
performance. The EPFTOOLBOX library is thoroughly described in: J.
Lago, G. Marcjasz, B. De Schutter, R. Weron (2021) "Forecasting
day-ahead electricity prices: A review of state-of-the-art algorithms,
best practices and an open-access benchmark", Applied Energy 293,
116983 (https://doi.org/10.1016/j.apenergy.2021.116983;
open access).
Downloads
Related paper
- J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "Forecasting Day-Ahead
Electricity Prices: A Review of State-of-the-Art Algorithms, Best Practices and
an Open-Access Benchmark," Applied Energy, vol. 293,
July 2021. Article 116983. (online
paper
![[open access]](open_access.gif "open access")
, abstract, bibtex, tech. report
(pdf))
Bibtex entry
@techreport{LagMar:21-014,
author={J. Lago and G. Marcjasz and B. {D}e Schutter and R. Weron},
title={{EPFTOOLBOX}: {The} First Open-Access {PYTHON} Library for Driving
Research in Electricity Price Forecasting ({EPF})},
type={WORMS Software (WORking papers in Management Science Software)},
number={WORMS/C/21/01},
institution={Department of Operations Research and Business Intelligence,
Wroclaw University of Science and Technology},
address={Wroclaw, Poland},
year={2021}
}
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