E3S Web Conf.
Volume 239, 2021International Conference on Renewable Energy (ICREN 2020)
|Number of page(s)||16|
|Published online||10 February 2021|
Electricity price forecasting on electricity spot market: a case study based on the Brazilian Difference Settlement Price
Graduate Program in Industrial Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil.
2 Department of Industrial Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil.
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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