Abstract:After the renewable energy is incorporated into the power grid, the power supply increases, and the short-term power load is difficult to predict, so it is impossible to formulate an accurate power distribution strategy. Therefore, a research on the accurate prediction method of short-term power load based on random forest is proposed. Deeply analyze the influencing factors of short-term electricity load forecasting (weather, time, electricity price and random interference factors), select appropriate model input variables (historical electricity load data, temperature data and daily type), construct the short-term electricity load forecasting model combined with random forest algorithm, repeatedly determine the selection rules of similar days, use particle swarm optimization algorithm to find the best value of forecasting model parameters, and input the sample set into the model, Obtain accurate short-term power load forecasting results. The experimental data show that when the number of input variables reaches a certain value, the short-term power load forecasting delay obtained by the proposed method is stable at about 0.55s, and the short-term power load forecasting error is almost 0, which fully proves the good application performance of the proposed method.