Abstract: Disclosed is a prediction method and device for a clearing price of an auxiliary service for peak regulation, including: acquiring continuous N days of historical data of clearing prices of an auxiliary service market for peak regulation and respectively moving data of original clearing prices forwards by 1, 2, . . . k days, so as to obtain D?1, D?2 . . . D?k data columns; performing first-round training and prediction by adopting a BP (Back Propagation) neural network, a BP neural network optimized by a PSO (Particle Swarm Optimization) algorithm and an LSSVM (Least Square Support Vector Machine), and forming a BP-expansion column, a PSO_BP-expansion column and an LSSVM-expansion column; training the BP neural network by taking first N?k days of time points and D?1, D?2 . . . D?k clearing prices, the BP-expansion column, the PSO_BP-expansion column, the LSSVM-expansion column and the original clearing prices as training data; and performing prediction for the clearing price.