Software Design for Short-Term Prediction of Wind Power Generation and Electrical Load in Jilin

T95 paper presents the design of software for the prediction of wind power generation and electrical load in Jilin Province, for the purpose of reducing wind power abandonment. The forecasting of wind power generation is based on a dispersion distance integration and parallelization prediction method, which utilizes the historical data of power generation of fifteen wind farms in Jilin. The prediction of electrical load is based on an improved vague neural network prediction method, which uses the historical data of electrical load of fifteen typical industrial companies in Jilin. The result shows that the software achieves a decent prediction accuracy of 94% in the rough.