One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms
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Energy demand estimation
Extreme Learning Machines
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 ...