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Power Prediction Based on Drosophila Optimized Similar Day Method and Random Forest Method

Short-term forecast of power load is an important work in power system. Improving the accuracy of load prediction is of great significance to the safe and reliable operation of power systems. Based on the traditional similar day method, this paper proposes to improve the Drosophila similar day method to improve the similarity calculation, and combines the improved similar day method and the random forest method to construct a combined prediction method. Combined with a load case in a certain area, through comparison with other combined prediction methods, it is found that the improved similar-day random forest method proposed in this paper has the highest prediction accuracy and the shortest time consumption, which shows the applicability and effectiveness of the combined prediction model proposed in this paper.