Abstract：Aimed at the characteristics of competitive bidding in the early stage of electricity market, which is less data available for research, the environment changes greatly and the user behavior is uncertain, a set of strategies for adaptive bidding supervised learning algorithm based on time series is proposed. The strategy combines the traditional mechanical learning methods, current electricity market rules and user behavior characteristics, the forgetting mechanism is used to simulate the maturing market behavior of users and the inertia mechanism is used to simulate the delayed response of users to the market. The self-verification mechanism is designed to correct the unreasonable forecasting errors. The regularization parameters avoid overfitting. The simulation results of the experimental example predict the supply-side curve and the demand-side curve, and verify the correctness and effectiveness of the proposed study based on the clearing result of the Guangdong electricity market.
初日辉, 胡秦然, 时翔, 李鹏. 基于监督式学习的自适应竞价预测模型研究[J]. 电气技术, 2018, 19(10): 1-5.
Chu Rihui, Hu Qinran, Shi Xiang, Li Peng. Study on adaptive bidding prediction algorithm based on supervised learning. Electrical Engineering, 2018, 19(10): 1-5.
2018, Vol. 19 
