Distributional neural networks for electricity price forecasting

TL;DR

This paper introduces a distributional neural network approach for probabilistic electricity price forecasting, effectively modeling higher moments and outperforming existing benchmarks in the German market.

Contribution

It presents a novel neural network architecture with a probability layer that directly estimates distribution parameters for improved probabilistic forecasting.

Findings

Outperforms state-of-the-art benchmarks in German electricity market

Highlights importance of higher moments in modeling volatile prices

Provides implications for risk management in power sector

Abstract

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahead electricity prices in the German market, our approach significantly outperforms state-of-the-art benchmarks, including LASSO-estimated regressions and deep neural networks combined with Quantile Regression Averaging. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also -- given that probabilistic forecasting is the essence of risk management -- provide important implications for managing portfolios in the power sector.