Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures

TL;DR

This paper presents a novel hybrid approach combining econometric autoregressive models with market expectations from futures to improve short- and mid-term day-ahead electricity price forecasts, achieving higher accuracy.

Contribution

It introduces an AR24-X model incorporating futures data for each hour, demonstrating that futures contain valuable information for electricity price prediction and outperforming existing models.

Findings

Futures data significantly improve forecast accuracy.

The model maintains hourly precision over multiple weeks.

Lasso estimation enhances model efficiency and performance.

Abstract

Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the time series of electricity prices. This paper tackles this issue in a promising and novel approach. By combining the precision of econometric autoregressive models in the short-run with the expectations of market participants reflected in future prices for the short- and mid-run we show that the forecasting performance can be vastly increased while maintaining hourly precision. We investigate the day-ahead electricity price of the EPEX Spot for Germany and Austria and setup a model which incorporates the Phelix future of the EEX for Germany and Austria. The model can be considered as an AR24-X model with one distinct model for each hour of the day. We are able to show that future data contains relevant price information for future time periods of the day-ahead electricity price. We show that relying only on deterministic external regressors can provide stability for forecast horizons of multiple weeks. By implementing a fast and efficient lasso estimation approach we demonstrate that our model can outperform several other models in the literature.