Short-Term Load Forecasting Using Random Forests

This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and multiple seasonal cycles. The main advantages of the model are its ability to generalization, built-in cross-validation and low sensitivity to parameter values. As an illustration, the proposed forecasting model is applied to historical load data in Poland and its performance is compared with some alternative models such as CART, ARIMA, exponential smoothing and neural networks. Application examples confirm good properties of the model and its high accuracy.