Machine Learning-Based Day-Ahead Prediction of Price-Setting Scheduled Energy in the Korean Electricity Trading Mechanism

Abstract

Power generation companies, which participate in the electricity trading mechanism, need to determine optimal capacity bidding strategy to maximize their operational profit in Korea. Since the Price-setting Power Generation Schedule determines the profit of power generators, it is important to predict Price-setting Scheduled Energy during the trading day before the bidding phase. We propose a methodology for predicting the Pricing-setting Scheduled Energy from the power exchange without optimizing it. Instead of simulating the planning process, machine learning algorithms are applied and compared in the process of predicting the Pricing-setting Scheduled Energy. The input variables consist of seasonal and price information including calendar, fuel cost, and system marginal price. Three categories of machine learning (ML) algorithms including single, bagging and boosting approaches are implemented and tested to compare their performances. The computational experiments show that ML algorithms with price variable are shown to be better in terms of the considered measures. In addition, boosting approach is more effective than single and bagging approaches.

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

“…They selected the very short-term forecasting of the energy consumption for the forecasting horizon. Lee et al applied well-known ML algorithms consisting of the decision tree (DT), SVM, Extra trees (ET), RF, Gradient Boosting, CatBoost, and XGB to investigate their performances for predicting the price-setting scheduled energy [37]. Zhang et al proposed a novel hybrid deep-learning framework for day-ahead electricity price forecasting in the concept of feature pre-processing and a deep-learning-based point prediction module [38].…”