Essays on electricity price forecasting

Abstract

Developing predictive models is a complex task since it deals with the uncertainty and the stochastic behavior of variables. Specifically concerning commodities, accurately predicting their future prices allows for risk minimization and establishment of more reliable decision support mechanisms. Discussion of this issue is extensive, and academic attention is being paid to the construction of nonparametric models to be applied to energy markets. They have presented promising predictive results, which justifies this research. Given the above, the following question is formulated: How is it possible to predict energy prices accurately in the Brazilian spot market? The present thesis provides a systematic literature review of the main forecasting methods applied to the energy sector. In the present study, it was possible to identify research gaps and, thus, propose new predictive models. The present thesis presents predictive models based on the idea of analogs. Analogs consist of scanning a time series and identifying patterns (so-called "matches") that are similar to the last available observations. Additionally, the recent hierarchical time series prediction theory has been incorporated, since many energy databases have well-defined dependency patterns.