One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms

This paper elaborates on a problem of one-year ahead estimation of energy demand based on macroeconomic variables. To this end, two different Computational Intelligence approaches are herein evaluated: (1) a modified Harmony Search (HS) optimization algorithm with an exponential prediction model and (2) an Extreme Learning Machine (ELM). In the case of the HS, a feature selection of the best set of features for the prediction is carried out jointly with the optimization of the model’s parameters. On the other hand, the ELM will be tested with and without the feature selection carried out by the HS approach. We describe several modifications on the proposed HS, which include a hybrid encoding with a binary part for the feature selection, and a real part to tune the parameters of the prediction model. Other adaptations focused on the HS operators are also introduced. The performance of both approaches has been assessed in a real application scenario, corresponding to the total energy demand estimation in Spain, in which we have 14 macroeconomic variables with history values for the last 30 years, including the recent crisis period starting in 2008. The performance of the proposed HS and ELM models incorporating feature selection is shown to provide an accurate one-year-ahead forecast at a higher prediction’s accuracy when compared to previous proposals in the literature. Specifically, the HS and ELM approaches are able to improve the results of a previous approach (based on a genetic algorithm), obtaining an improvement over 15% in this problem of energy demand estimation. As a final experimental evaluation of the proposed algorithm, a similar problem of one-year ahead CO2 emissions estimation from macroeconomic variables is also tackled, and also in this case the HS and ELM are able to obtain significant improvements over a previous approach based on evolutionary computation, over 10% of improvement in this problem. 2015 Elsevier Ltd. All rights reserved.