Prediction of market-clearing price using neural networks based methods and boosting algorithms

The development of Turkey's industry is contributing to a significant rise in electrical energydemand. Also, electricity is one of the critical elements in the household sectors. Therefore, theplanning and managing of electrical energy is of great importance to support economic growth.In addition, effective prediction of market-clearing prices (MCP) is critical topic to meet theincreasing energy demand and provide basis for decision making process. In this paper, MCP ispredicted using artificial neural network (ANN), convolutional neural network (CNN), and alsothree boosting algorithms including extreme gradient boosting (XGBoost), categorical boosting(CatBoost), and adaptive boosting (AdaBoost). Various performance metrics are employed toevaluate the prediction performance of proposed methods. The results showed that proposedmethods provide reasonable prediction results for energy sector. Hence, producers andconsumers can use these methods to determine the bidding strategies and to maximize theirprofits.