Abstract:  ABSTRACT： With the development of smart grid, global energy Internet and related technologies, the structure of power big data is already formed. How to make full use of the high-dimensional massive data through data mining to make full use of data has aroused widespread concern of power workers. Aiming at the high precision and real-time load forecasting with the background of power big data, this paper proposes the short-term power load forecasting based on Spark platform and parallel random forest regression (SP-RFR) algorithm. The parallelization improvement of single machine random forest algorithm is realized by three transforms of resilient distributed datasets(RDD), and can be deployed on a Spark distributed cluster. Experiments are designed by using actual power load data of a transformer substation, and model training and regression prediction are implemented. The conclusions are as follows, for the same testing data set, the short-term power load forecasting method based on SP-RFR model is superior to single machine regression forecasting model； SP-RFR model is less disturbed by outlier data, and SP-RFR model has good robustness with the increase of data set； compared with the single machine model, with the increase of the data set, the SP-RFR, which is based on the distributed cluster, has obvious advantages. The proposed method can effectively forecast power load in distributed background, which can provide a new idea for power load forecasting.

Key words:  ,  power big data, distributed computing, parallel random forest regression algorithm, Spark platform, short-term power load forecasting.