In order to further constrain the electricity consumption behavior of large industrial enterprise user and reduce carbon emission while decreasing total cost,a short-term electricity load and price prediction method for large industrial user was proposed,which uses random forest screening feature and improves the bidirectional gated recurrent unit based on quantum particle swarm optimization algorithm.This prediction method considers external characteristic factors such as temperature,humidity,date type and so on,to maximize the restoration of actual operating scenario.By inputting historical load data and historical electricity price data,the future 24-hour load and electricity price situation can be predicted.The experimental result shows that this prediction method has superiority compared to other mainstream prediction methods.
万方数据
维普
