Evolving energy demand estimation models over macroeconomic indicators


Energy is essential for all countries, since it is in the core of social and economic development. Since the industrial revolution, the demand for energy has increased exponentially. It is expected that the energy consumption in the world increases by 50% by 2030 [17]. As such, managing the demand of energy is of the uttermost importance. The development of tools to model and accurately predict the demand of energy is very important to policy makers. In this paper we propose the use of the Structured Grammatical Evolution (SGE) algorithm to evolve models of energy demand, over macro-economic indicators. The proposed SGE is hybridised with a Differential Evolution approach in order to obtain the parameters of the models evolved which better fit the real energy demand. We have tested the performance of the proposed approach in a problem of total energy demand estimation in Spain, where we show that the SGE is able to generate extremely accurate and robust models for the energy prediction within one year time-horizon.

Proceedings of the 2020 Genetic and Evolutionary Computation Conference
J. Manuel Colmenar
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.