Short-Term Power Load Forecasting for Larger Consumer Based on TensorFlow Deep Learning Framework and Clustering-Regression Model

This paper tackles a new challenge in high precision load forecasting of larger consumer with the background of electric power big data. The proposed short-term power load forecasting method is based on TensorFlow deep learning framwork and clustering-regression model. Proposed scheme firstly clustering the users with different electrical attributes and then obtains the “load curve of each cluster”, which is considered as the properties of a regional total load, and represents the features of various types of consumers. Furthermore, the “clustering-regression” model is used to forecast the power load of the certain region, which is implemented by TensorFlow deep learning framework. Extensive experiments show that the proposed scheme can predict reasonably the short-term power load and has excellent robustness. Comparing with the traditional model, the proposed method has a higher efficiency in dealing with large-scale data set and can be effectively applied to the power load forecasting.