A short-term power load forecasting method based on k-means and SVM

With the continuous development of smart grids, short-term power load forecasting has become increasingly important in the operation of power markets and demand-side management. In order to explore the influence of temperature and holidays on seasonal loads, this paper proposes a short-term SVM power load forecasting method based on K-Means clustering. The method includes the steps of selecting similar days, data preprocessing, SVM prediction model training and parameter adjustment. Among them, the selection of similar days uses K-Means to group seasonal load data into two categories according to temperature characteristics, as the input data to explore the effect of temperature on seasonal load. And divide the data into holidays and working days as the model input data to discover the impact of holidays on seasonal loads by using calendar rules. In order to verify the load forecasting effect of the proposed method, several experiments were carried out on two actual residential load data and two data online, and compared with the LSTM and decision tree load forecasting models in terms of prediction accuracy evaluation index and running time. The results show that the model constructed in this paper has 39.75% improved to the conventional methods for the accuracy and 128.89% improved for the running time.