Intelligent Probabilistic Forecasts of Day-Ahead Electricity Prices in a Highly Volatile Power Market

Electricity price forecasting plays an important role in decision making on bidding strategies of selling and buying electricity. This paper computes one day-ahead (DA) quantile forecasts of electricity prices in a highly volatile market by applying regression models to a pool of point forecasts. Three data-driven forecasting methods are implemented to generate DA point forecasts of the Ontario market’s electricity prices. In order to generate the three sets of point forecasts, we use: i) the triple exponential smoothing (TES) method, ii) a neural network (NN) that combines layers of Convolutional neurons and gradient recurrent units (GRU), iii) an extreme gradient boosted (XGB) non-linear regression approach. Performance of the three models compared against a benchmark which considers the forecast of electricity prices as the average price of the same hour and day during the last four weeks. The TES method decreases the mean absolute error (MAE) of the benchmark model from 10.29 to 9.42. The Convolutional GRU (ConvGRU) model and XGB regression also reduce the MAE to 8.20 and 7.06, respectively. Finally, quantile regression averaging (QRA) is applied to the pool of point forecasts obtained by TES, ConvGRU, and XGB methods to compute DA quantile forecasts of electricity prices. Moreover, the QRA method is further developed in this work by employing gradient boosting non-linear regression (GBR). Our analysis using real data reveals that the GBR method provides more reliable quantiles as well as tighter prediction intervals with smaller forecasting errors than QRA.