The Design And Realization Of Power Grid Load Forecast System Of Classification Of Blind Source Separation

Nearly a dozen years of research on power load forecasting, greater emphasis on fuzzy model for load forecasting research. This is because of the diversity of factors effect on load forecast and load is distributed and complex uncertainties have a better understanding, greater consideration of load due to uncertain factors, interval type 2 or type 2 fuzzy number and fuzzy numbers etc to model the uncertainty. Research of recent years is partly based on the neural network of artificial intelligence technology utilization, to optimize load forecasting processes and results, on the other hand a large number of new forecasting methods have also been used for load forecasting, which brought power load forecasting a new ideas and new directions.The main contents of this project include the following aspects:1.the power grid load forecast system of classification of blind source separation and conduct a detailed analysis of the relationship between the smart grid, determine the current requirements of the smart grid background, grid load prediction system is able to achieve the classification of blind source separation.2.the introduction of big data computing concepts, electric power generation and deployment of cloud patterns, proposed the adoption of virtualization architecture building load forecasting model, describes the power load prediction mechanism and study on resource scheduling in data, a series of resource scheduling algorithm can be applied to the current power load context.3.the traditional power grid load forecast system of classification of blind source separation of shortcomings and flaws, and features a brand new architecture to design the power grid load forecast system of classification of blind source separation, facilitate power grid load forecast system of classification of blind source separation in a future upgrade.Blind Separation grid load classification system used a real grid as the basic data to conduct a calculation, and the results of prediction showed that the system can meet the needs of the prediction of the grid and power load.The prediction results are reasonable, the system interface is user-friendly, the operation is convenient, the graphic display is efficient, thus improve the productivity of people engaged in prediction.