Analysis of System Marginal Price in the Turkish Electricity Market

Abstract

Due to climate change, growing energy prices, and increasing energy consumption, energy efficiency has become a key topic in recent years. Most energy market traders also want to be able to foresee the energy market in the future so that they can take the appropriate actions to optimize their trading profits. As a result, energy market evaluation models are required. Energy markets, on the other hand, are location-dependent, as each market has its auctions and procedures. As a result, specific models for each energy market should be developed. The primary aim of this study is to provide a comprehensive comparison of various machine learning methods in the Turkish electricity market. A comparative analysis is provided on support vector machines (SVM)-based methods, k-nearest neighbors (KNN)-based methods, and ensemble-based method to analyze system marginal price (SMP). According to the accuracy value, the ensemble-based method gives better results.