Forecasting individual bids in real electricity markets through machine learning framework

• Published in: Applied energy Vol. 363; p. 123053
• Main Authors: Tang, Qinghu, Guo, Hongye, Zheng, Kedi, Chen, Qixin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Data-driven analysis; Electricity market; Individual bids forecasting; Machine learning; Data-driven analysis; Individual bids forecasting; Electricity market; Machine learning
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2024.123053

With the increasing uncertainty caused by the complexity of the world’s energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated. •A scalable framework for forecasting individual bids in real electricity markets.•A bidding encoding method processes the bidding curves in a machine learning way.•A transformer-based multi-dimensional forecasting model for time-series forecasting.•Performance evaluation based on real data from the Australian electricity market.