Research on Apache Spark Based Transformer Areas Load Forecasting

Abstract

The massive data accumulated by the power company provides the basic data profile for load forecasting. In this paper, a dynamic Bayesian network is built as a load forecasting model of transformer areas. The parallel computing platform Apache Spark is used to calculate the parameters of the model based on large volume of transformers' historical data in parallel. Meanwhile, the Pregel computing model is used to parallelize the forward backward algorithm to realize the forecasting tasks. The experimental results show that the proposed transformer areas load forecasting technology based on distributed graph computing has high prediction accuracy and fast calculation speed.