Application of a data-driven DTSF and benchmark models for the prediction of electricity prices in Brazil: A time-series case.

The global energy market has significantly developed in recent years; proof of this is the creation and promotion of smart grids and technical advances in energy commercialization and transmission. Specifically in the Brazilian context, with the recent modernization of the electricity sector, energy trading prices, previously published on a weekly frequency, are now available on an hourly domain. In this context, the definition and forecasting of prices become increasingly important factors for the economic and financial viability of energy projects. In this scenario of changes in the local regulatory framework, there is a lack of publications based on the new hourly prices in Brazil. This paper presents, in a pioneering way, the Dynamic Time Scan Forecasting (DTSF) method for forecasting hourly energy prices in Brazil. This method searches for similarity patterns in time series and, in previous investigations, showed competitive advantages concerning established forecasting methods. This research aims to test the accuracy of the DTSF method against classical statistical models and machine learning. We used the short-term prices of electricity in Brazil, made available by the Electric Energy Commercialization Chamber. The new DTSF model showed the best predictive performance compared to both the statistical and machine learning models. The DTSF performance was superior considering the evaluation metrics utilized in this paper. We verified that the predictions made by the DTSF showed less variability compared to the other models. Finally, we noticed that there is not an ideal model for all predictive 24 steps ahead forecasts, but there are better models at certain times of the day. [ABSTRACT FROM AUTHOR]