Electricity price forecasting on electricity spot market: a case study based on the Brazilian Difference Settlement Price

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 us to minimize risks and establish more reliable decision support mechanisms. Although the discussion on this question is extensive, there is academic attention being paid to the construction of nonparametric models applied to energy markets, as they have presented promising predictive results, what justifies the present study. This paper applies classical statistical models and Dynamic Time Scan Forecasting (DTSF) to the short-term electricity market prices, in Brazil, from 2006 to 2019. DTSF consists of scanning a time series and then identifying past patterns (so-called “matches”), similar to the last available observations. We predict Brazilian electricity spot prices, according the most similar matches, using aggregation functions, such as median. Recent research on the electricity spot market is increasing, indicating research significance. Our predictive approach exhibited greater accuracy than seminal statistical models. Our approach was designed for a high frequency series. Its predictive performance remained robust when other models presented both high predictive errors (spring), as well as when those models are highly accurate (winter). For future research, we recommend a more finely-tune study on DTSF parameters.

Models of Electricity Price Forecasting: Bibliometric Research

“…Contrary to the other bibliometric analysis publications [9,82], this study does not prove that WoS is a subset of Scopus [9], therefore it is not possible to limit literature review analysis to WoS only [83]. The comparison of citation data in both multidisciplinary databases performed by L. Waltman proved the similarities between both database collections [84].…”

Multifractal Analysis of the Brazilian Electricity Market

“…Gontijo et al [25] used Dynamic Time Scan Forecasting (DTSF) for predicting spot electricity prices in Brazil, a methodology initially formulated for predicting wind and power generation in industrial plants. The method involves scanning a time series and identifying past patterns (called "matches") similar to the latest available observations.…”

“…They considered hourly prices from 2019, taking into account the testing period until the end of 2021. The DTSF methodology had already been applied in the other paper by the same authors for the forecasting of weekly electricity prices in Brazil, [30]. DTSF showed better predictive performance and less variability when compared to statistical models and machine learning.…”

Analyzing Risk Premiums in the Brazilian Power Market: A Quantitative Study

“…Market-specific studies add another layer of complexity. Researchers like Gontijo et al [10] and Santos et al [11] focused on the Brazilian electricity market, using methods ranging from classical statistical models to machine learning for assessing short-term electricity prices. Studies by Luz et al [12] specifically examined the Brazilian forward electricity market, identifying contango behavior and high-risk premiums as key features.…”