Abstract:

Multi-user power load forecasting refers to the power load forecasting of multiple users or regions based on historical loads data, which can make the grid companies understand the power demands of different users or regions, so as to better carry out the planning and scheduling optimization of the power system. However, different users have complex and diverse power consumption behaviors, so it is difficult to use traditional methods to universally model different power users' loads and achieve accurate prediction. Therefore, a new multi-user short-term load prediction model based on DTW K-medoids and VMD-multi-branch neural network is established. Firstly, in order to improve the clustering performance of traditional clustering methods, the DTW K-medoids method is used to cluster users' load data, and the distance between loads data is calculated using the dynamic time warping (DTW) instead of the traditional Euclidean distance measurement method in K-medoids to improve the clustering effects of multiple users' load. Secondly, in order to fully characterize the long short-term time series-dependent characteristics of load history data, a parallel load forecasting method based on VMD-multi-branch neutral network model is established for multi-user short-term load forecasting. Finally, the 365-day load data of 20 users in a region is used for clustering, training and experiment, and the results show that the MAE and RMSE indexes of the proposed model significantly decrease compared with that of the comparative models, indicating that the proposed method can effectively characterize the power consumption behaviors of multiple users and improve the prediction efficiency and accuracy of multi-user loads.

Key words: multi-user, load forecasting, DTW K-medoids clustering, variational mode decomposition, multi-branch neural network