Pricing analysis in the Brazilian energy market: A decision tree approach

There is a general consensus that electricity price forecasting is an important task nowadays since power market players are interested in the maximization of profit and the minimization of risk. In this context, this paper proposes the use of data-mining techniques to predict the short-term electricity price in the Brazilian market. In Brazil, the market model adopted has unique characteristics with a centralized form of dispatch due to the predominance of hydro generation. To apply the proposed prediction model, all features of the Brazilian electricity market are considered, such as the transmission restrictions among geo-electrical regions and the price dependency with storage energy in reservoirs. In the proposed prediction model, the electricity price is the dependent variable and the monthly time series data sets from the Brazilian system (such as power load, stored energy and thermal generation) are the independent variables. First, clustering of the data samples is performed to group similar behavior of the attributes. After that, a decision tree algorithm is applied to extract if-then rules from database. The rules obtained allow the identification of attributes that most influence the short-term electricity price. Results show that the proposed model can be an attractive tool to all electricity market players to forecast the short-term electricity price and mitigate the risks in purchasing power.