Modelling energy consumption in Spain with metaheuristic methods

Resumen

Forecasting country-wide energy demand is an important activity for determining and progressing towards an energy secure future. In this paper, one-year-ahead energy use for the country of Spain has been modeled using metaheuristics with socio-economic indicators as the input variables. A total of 14 input variables were selected (including gross domestic product, the population of the country, import and export quantities, etc.). The data for energy use as well as the 14 input variables have been collected from the public websites for 45 years (for the period of 1970–2014). The approach here is to bifurcate the data into training and testing datasets with equal number of data points. The training dataset is used to train the algorithm while the testing dataset is used to evaluate the performance of the algorithm on the independent data. The metaheuristics applied has two components: A highly recursive Grammatical Evolution (GE) and Differential Evolution (DE). GE identifies the model structure while the DE optimizes the coefficients of the models. The performance of the algorithm was evaluated in terms of various statistical errors (RMSE 0.0393, Avg. Error −0.0091, R2 0.9503, Absolute Error 0.0312, and Relative Error 0.0427). The results so obtained confirm that the proposed GE+DE method is highly accurate and can be successfully used to model the country’s future energy demand precisely.

Publicación
2021 6textsuperscriptth International Conference on Smart and Sustainable Technologies (SpliTech)
Lucía Serrano Luján
Lucía Serrano Luján
Phd in Artificial Intelligence
J. Manuel Colmenar
J. Manuel Colmenar
Artificial Intelligence Professor

Mis intereses de investigación se centran en las metaheurísticas aplicadas a problemas de optimización. He trabajado en diferentes problemas de optimización combinatoria aplicando algoritmos trajectoriales como GRASP o VNS. Además, estoy muy interesado en las aplicaciones de la Evolución Gramatical, específicamente en el dominio de los modelos y la predicción, como alternativa a los enfoques de aprendizaje automático.