Multivariate Probabilistic Forecasting of Electricity Prices With Trading Applications

A recently introduced approach is extended to probabilistic electricity price forecasting (EPF) utilizing distributional artificial neural networks, based on a regularized distributional multilayer perceptron (DMLP). We develop this technique for a multivariate case EPF with incorporated dependence. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. The empirical data application analyzes two day-ahead electricity auctions for the United Kingdom market. This creates the opportunity to buy in the first auction for lower price and sell in the second for higher price (or vice versa). Utilizing forecasting results, we develop trading strategies with various investors’ objectives. We find that, while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly