Distributional neural networks for electricity price forecasting

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.

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Cited by in corpus (10)

• Operational Research: Methods and Applications
• Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices
• Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
• A feature selection method based on Shapley values robust to concept shift in regression
• Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market
• Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading
• Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
• ReModels: Quantile Regression Averaging models
• Statistical and economic evaluation of forecasts in electricity markets: beyond RMSE and MAE
• Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting