Short-Term Load Forecasting of Buildings Based on Data of Mobile Base Station

With the establishment of large-scale buildings, the rapid increase in power consumption has had a major impact on the reliability and economy of the grid operation. Therefore, it is very crucial to make effective short-term load forecasting for building electricity. At the same time, as a new type of big data, data of mobile base station has been applied to military, transportation and so on. But in the power system, the data has not been applied. This paper proposes a multi-dimension hierarchical grey relational analysis method, in order to introduce data of mobile base station into building short-term load forecasting. The data is divided into meteorological dimension and mobile base station dimension. The grey relational analysis algorithm based on entropy weight method is used to analyze the horizontal and vertical associations. And the more relevant variables are selected as the input variables of load forecasting. Eventually, the effectiveness of the method is verified by an example.