Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure

TL;DR

This paper introduces a lasso-based regression model for day-ahead electricity prices that effectively captures intraday dependencies and improves forecasting accuracy across European markets.

Contribution

It demonstrates the application of lasso regression to model autoregressive intraday structures in electricity prices, offering a novel approach for improved forecasting.

Findings

Lasso captures intraday price dependencies effectively.

The model outperforms traditional methods in out-of-sample forecasts.

It provides insights into intraday price behavior.

Abstract

In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitting. Thus, it is able to capture the autoregressive intraday dependency structure of the electricity price well. We discuss in detail the estimation results which provide insights to the intraday behavior of electricity prices. We perform an out-of-sample forecasting study for several European electricity markets. The results illustrate well that the efficient lasso based estimation technique can exhibit advantages from two popular model approaches.