Load Forecasting Based on Smart Meter Data and Gradient Boosting Decision Tree

Abstract

Load forecasting can be used to optimize the operation of the energy management system and reduce the cost of energy consumption. In this paper, we implement an energy management system in the office building of Fujian Huatuo Automation Technology Company. The smart meters monitor the energy consumption of the building, and the smart meter data are transmitted to the cloud server for load forecasting. To improve the precision of load forecasting, we adopt the gradient boosting decision tree (GBDT) to process the data, and study the best combination of features. The smart meter data are used to test the performances of the proposed load forecasting approach, and the results show that the proposed approach has better performance than traditional methods.