Research on Short-term Load Forecasting Based Graph Computation in Power Supply Areas

Large amounts of data collected by power company has laid the foundation for load forecasting at different time scales. In this paper, a Dynamic Bayesian Network is established as a short-term load prediction model for distribution transformer supply area. The parallel computational operators of Apache Spark are used to calculate the model parameters based on Bayesian algorithm and a large number of history data of the transformer. At the same time, the forward backward algorithm is parallelized by using the Pregel calculation model to obtain the predicted results. The experimental results show that the proposed method has better performance and higher efficiency than traditional methods.