Prediction of Day-ahead Clearing Price in Electricity Market Based on Hybrid Algorithm

Aiming at the problem of poor prediction accuracy of day-ahead clearing price in power market, a day-ahead clearing price prediction algorithm based on principal component analysis, particle swarm optimization and lightweight gradient hoist was proposed. The mathematical model of day-ahead electricity market clearing was analyzed, and the market factors affecting electricity price were obtained. The feature correlation heat map and principal component analysis method were used to reduce the dimension. The light gradient boosting machine was used to construct the electricity price prediction model, and the particle swarm optimization algorithm was used to optimize its hyper parameters. The experimental results show that the mean absolute error of the proposed algorithm is reduced by 23.75% compared with the benchmark model, so it can effectively improve the prediction accuracy in the actual power trading business.