Transfer Learning for Electricity Price Forecasting

TL;DR

This paper explores transfer learning to enhance day-ahead electricity price forecasting by leveraging data from multiple markets, demonstrating significant improvements over traditional methods.

Contribution

It introduces a transfer learning framework for electricity price forecasting, utilizing pre-training on source markets and fine-tuning on target markets, which is a novel approach in this domain.

Findings

Transfer learning significantly improves forecasting accuracy.

Pre-training on multiple markets enhances model robustness.

The approach outperforms state-of-the-art methods in experiments.

Abstract

Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a neural network model on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the rich input data from various electricity price markets. Our experiments on four different day-ahead markets indicate that transfer learning improves the electricity price forecasting performance in a statistically significant manner. Furthermore, we compare our results with stateof-the-art methods in a rolling window scheme to demonstrate the performance of the transfer learning approach.