Özet:

Electricity known as static electricity until nineteenth century took on a new meaning with it was generated in New York in 1880. Electricity have been an indispensable instrument for human life. Initially, governments undertook the electricity process from generating to distribution. However, in 1980’s, also in Turkey at the beginning of the 2000’s, countries such as Chile, England, Australia started to liberalize their electricity markets for competition. This paper aims to predict hourly Market Clearing Price (MCP) announcing in Day Ahead Market being operated by Turkish Energy Exchange (EXIST) with market operating licence dated from 2015. It is researched that lagged values of MCP and trade value of day ahead market how prediction success on MCP. As prediction method, random forest and support vector machine of machine learning methods was used. Analysis period involve 1 Jan 2019 00:00 and 10 Mar 2020 23:00 and consist of 10440 data divided into two subset as training set (%84) and test set (%16). K-fold Cross-validation method is used for describing best parameter. Analysis was implemented in R program with caret and e1071 package. As a result of the analysis, according to the RMSE, MAPE, MAE using frequently in literature for comparing forecast performance, the best method and the best variable group which predict MCP is respectively random forest regression and the group including trade value. Therefore, this paper demonstrated that trade value is important variable for MCP and random forest is important method just as other methods used prediction of MCP.

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