A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources. (15th May 2019)

• Record Type: Journal Article
• Authors: Luo, Shuman; Weng, Yang
• Is Part Of: Applied energy. Volume 242(2019)
• Page Start: 1497
• Page End: 1512
• Publication Date: 2019-05-15
• Subjects: Electricity price forecast -- Machine learning methods -- Renewables -- Wind power generation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.03.129
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A two-stage method is proposed for electricity price forecasting. The method diversifies the data resources improve the forecasting accuracy. The method is tested using the real-world data of Texas electricity market. Causal relationship combined with physical models guarantee better results. Deep neural networks show a great performance in electricity price forecasting. Abstract: Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.