Abstract: Aiming at the problems of poor prediction effect in the existing power system short-term load forecasting, a power system short-term load forecasting algorithm based on deep data mining is proposed. Taking the normalized historical power system load data, fuzzy temperature data, weather conditions, precipitation probability and other data as the input of the prediction model, a power system short-term load prediction model based on fuzzy gbdt is constructed, and the boosting algorithm is introduced to solve the problems of slow training speed and large memory occupation in the prediction model. The experimental results show that the short-term load forecasting results of the proposed method are close to the actual load at different times on weekdays and weekends. The MAPE and rmspe values of power system short-term load forecasting in the next week are lower than 0. 2% .

Key words: deep data mining, power system, short term load forecasting, normalization, fuzzy processing, fuzzy boosting-gradient boosting decision tree(GBDT)