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.
KeywordsShort-term load forecasting seasonal time series forecasting random forests
Unable to display preview. Download preview PDF.
- 2.Weron, R.: Modeling and Forecasting Electricity Loads and Prices. Wiley (2006)Google Scholar
- 4.Cheng, Y.-Y., Chan, P.P.K., Qiu, Z.-W.: Random Forest Based Ensemble System for Short Term Load Forecasting. Proc. Machine Learning and Cybernetics (ICMLC) 1, 52–56 (2012)Google Scholar
- 6.Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman and Hall (1984)Google Scholar
- 7.Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer (2009)Google Scholar
- 8.Dudek, G.: Similarity-based Approaches to Short-Term Load Forecasting. In: Zhu, J.J., Fung, G.P.C. (eds.) Forecasting Models: Methods and Applications, pp. 161–178. Concept Press (2010)Google Scholar
- 9.Dudek, G.: Short-Term Load Forecasting Using Fuzzy Regression Trees. Przegląd Elektrotechniczny (Electrical Review) 90(4), 108–111 (2014) (in Polish)Google Scholar
- 10.Dudek, G.: Forecasting Time Series with Multiple Seasonal Cycles using Neural Networks with Local Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 52–63. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 11.Hyndman, R.J., Khandakar, Y.: Automatic Time Series Forecasting: The Forecast Package for R. Journal of Statistical Software 27(3), 1–22 (2008)Google Scholar