Título: Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine
Autores: Lucas Barth da Silva, Roberto Zanetti Freire and Osíris Canciglieri Junior.
Resumo:
Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a computational intelligence model using Wavelet Transform on input values and the Extreme Machine Learning algorithm for training and prediction (W-ELM). The macro parameters were optimized using the Particle Swarm Optimization algorithm and for the selection of the input variables, a model based on Mutual Information (MI) was used. In the end, the methodology was compared with the traditional methods: Autoregressive Moving Averages (ARIMA) and General Autoregressive Conditional Heteroskedasticity (GARCH) models. Results showed that the W-ELM had better performance for forecasting 1 to 4 weeks of when compared to ARIMA. When the GARCH model results were considered, the proposed method provided worse performance only for 1 step ahead forecasting.
Palavras-chave:
Price, Electricity, Forecasting, Wavelet Extreme Machine Learning, Neural Networks.
Páginas: 8
Código DOI: 10.21528/CBIC2021-62
Artigo em pdf: CBIC_2021_paper_62.pdf
Arquivo BibTeX: CBIC_2021_62.bib