Abstract
The estimation of total energy demand in a country from macro-economic variables is an important problem useful to evaluate the robustness of the country’s economy. Since the first years of this century, meta-heuristics approaches have been successfully applied to this problem, for different countries and problem’s parameterizations. Many of these works optimize prediction models which are based on classical polynomial or simple exponential relationships, which may not be the best option for an accurate energy demand estimation prediction. In this paper the use of Grammatical Evolution is proposed to generate new models for total energy demand estimation at country level. Grammatical Evolution is a class of Genetic Programming algorithm, which is able to automatically generate new models from input variables. In this case, Grammatical Evolution considers macro-economic variables from which it is able to generate new models for total energy demand estimation of a country, with a temporal prediction horizon of one year. The models generated by the Grammatical Evolution are further optimized in order to adjust their parameters to the energy demand estimation. This process is carried out by means of a Differential Evolution approach, which is run for every model generated by the Grammatical Evolution. Thus, the algorithmic proposal consists of a hybrid method, involving Grammatical Evolution for model generation and a Differential Evolution meta-heuristic for the models’ parameter tuning. The performance of the proposed approach has been evaluated in two different problems of total energy demand estimation in Spain and France, with excellent results in terms of prediction accuracy.
J. Manuel Colmenar
Associate Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.