ReModels: Quantile Regression Averaging models
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Abstract (arXiv)
Electricity price forecasts play a crucial role in making key business decisions within the electricity markets. A focal point in this domain are probabilistic predictions, which delineate future price values in a more comprehensive manner than simple point forecasts. The golden standard in probabilistic approaches to predict energy prices is the Quantile Regression Averaging (QRA) method. In this paper, we present a Python package that encompasses the implementation of QRA, along with modifications of this approach that have appeared in the literature over the past few years. The proposed package also facilitates the acquisition and preparation of data related to electricity markets, as well as the evaluation of model predictions.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 18 May 2024.
Builds on
5 of 30 references.
References · 5
- 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 105
- 2022Distributional neural networks for electricity price forecastingcited by 24
- 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19
- 2019Smoothing quantile regressionscited by 16
- 2023A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Marketcited by 6
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1 paper.