Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain
Author to whom correspondence should be addressed.
Received: 9 February 2019 / Revised: 15 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019
PDF [1374 KB, uploaded 21 March 2019]
Predicting electricity prices and demand is a very important issue for the energy market industry. In order to improve the accuracy of any predictive model, a previous variable importance analysis is highly advised. In this paper, we propose an alternative framework to assess the variable importance in multivariate response scenarios based on the permutation importance technique, applying the Conditional inference trees algorithm and a
-divergence measure. Our solution was tested in simulated examples as well as a real case, where we assessed and ranked the most relevant predictors for price and demand of electricity jointly in the Spanish market. The new method outperforms, in most cases, the outcomes achieved by the recently proposed techniques, Intervention prediction measure (IPM) and Sequential multi-response feature selection (SMuRFS). For the electricity market case, we identified the most relevant predictors among pollutant, renewable, calendar and lagged prices variables for the joint response of demand and price, showing also the effectiveness of the proposed multivariate response method when compared with the univariate response analysis.
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 (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Ahrazem Dfuf, I.; Mira McWilliams, J.M.; González Fernández, M.C. Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis. Energies 2019, 12, 1097.
Ahrazem Dfuf I, Mira McWilliams JM, González Fernández MC. Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis. Energies. 2019; 12(6):1097.
Ahrazem Dfuf, Ismael; Mira McWilliams, José M.; González Fernández, María C. 2019. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis." Energies 12, no. 6: 1097.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.