Version 1
: Received: 16 September 2016 / Approved: 18 September 2016 / Online: 18 September 2016 (06:16:19 CEST)
How to cite:
González, C.; Mira, J.M.; Ojeda, J.A. Applying Multi-Output Random Forest Models to Electricity Price Forecast. Preprints2016, 2016090053. https://doi.org/10.20944/preprints201609.0053.v1
González, C.; Mira, J.M.; Ojeda, J.A. Applying Multi-Output Random Forest Models to Electricity Price Forecast. Preprints 2016, 2016090053. https://doi.org/10.20944/preprints201609.0053.v1
González, C.; Mira, J.M.; Ojeda, J.A. Applying Multi-Output Random Forest Models to Electricity Price Forecast. Preprints2016, 2016090053. https://doi.org/10.20944/preprints201609.0053.v1
APA Style
González, C., Mira, J.M., & Ojeda, J.A. (2016). Applying Multi-Output Random Forest Models to Electricity Price Forecast. Preprints. https://doi.org/10.20944/preprints201609.0053.v1
Chicago/Turabian Style
González, C., José M. Mira and José A. Ojeda. 2016 "Applying Multi-Output Random Forest Models to Electricity Price Forecast" Preprints. https://doi.org/10.20944/preprints201609.0053.v1
Abstract
Predicting electricity prices is a very important issue in modern society, because the associated decision process under uncertainty requires accurate forecasts for the economic agents involved. In this paper, we apply the decision tree extension of Random Forests to the prediction of electricity prices in Spain, but with the novelty of modeling prices jointly with demand, with the purpose of achieving greater accuracy than with univariate response Random Forests, particularly in price prediction, as well as understanding the effect of the input variables (lagged values of price and demand, current production levels of available energy sources) on the joint of the two outputs. The results are very encouraging, providing significant increase in price prediction accuracy. Also, interesting methodological challenges appear as far as the appropriate choice of the relative weights of price and demand in the joint modeling is concerned and a new procedure to provide the importance variable ranking is proposed. The partykit (package of R software) library allowing for multivariate Random Forests has been used.
Keywords
electricity markets; price forecasting; multi-output models; random forests; conditional inference trees
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
