Title: Hybrid Approach for Energy Price Prediction in Brazilain Market
Authors: Lidio Mauro Lima de Campos, Jherson Haryson Almeida Pereira, Danilo Souza Duarte, Roberto Célio Limão de Oliveira
Abstract: This paper proposes a hybrid approach for short-term energy price prediction. This approach combines a genetic algorithm (GA) that evolves individuals represented by a set of production rules, these rules are extracted from chromosomes and generate the genotypes, which are mapped to phenotypes(Deep Neural Networks). The Artificial Neural Networks (ANNs) are then trained and then validated, and the prediction tests are carried out. The genotypes are classified by the performances of their ANNs in prediction and the GA selects the best individuals for mutation and crossover operations, which provide a new population. The previous steps are repeated through n generations. The result is an optimized neural network architecture for energy price prediction. The results show good ability to predict spikes and satisfactory accuracy according to error measures, delivering an accurate prediction. Finally, the results are compared with traditional techniques.
Key-words: NeuroEvolution; Genetic Algorithm; Neural Networks,Time series forecasting
DOI code: 10.21528/CBIC2019-25
PDF file: CBIC2019-25.pdf
BibTeX file: CBIC2019-25.bib