Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation

The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. Particularly, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating the traceability of the predictor selection procedure, the method of Bootstrap Aggregation (bagging), which is a variant shrinkage estimation approach for the estimation of large scale models, is proposed in this paper. To forecast day-ahead electricity prices in a multivariate context for six major power markets we construct a large scale pure-price model (in addition to some stochastic models that are commonly applied in the literature) and apply the bagging approach in comparison with the popular Least Absolute Shrinkage and Selection Operator (LASSO) estimation method. Our forecasting study reveals that with its superior forecasting performance and its computationally simple algorithm, the bagging emerges as a strong competitor to the commonly applied LASSO approach for the short-term EPF. Further analysis for the variable selection for the bagging and LASSO approaches suggests that the differentiation in the forecast performances of two approaches might be due to, inter alia, their structural differences in the explanatory variables selection process. Moreover, to account for the intraday hourly dependencies of day-ahead electricity prices, all our models are augmented with latent factors, and a substantial improvement is observed only in the forecasts from models covering a relatively limited number of predictors, while almost no improvement is obtained in the forecasts from the large scale model estimated through LASSO and bagging techniques.