The Design And Implementation Of Bus Load Forecasting System

Electric power load forecasting is related to the safe and economic operation of electricity grid,among which busbar electric power load forecasting is an important content of electric power load forecasting and a key project of current power dispatch work.Accurate and effective busbar load forecasting can provide decision basis for reasonable arrangement of power supply and electricity grid construction schedule,and provide strong guarantee for state estimation,operation stability,dispatch analysis and energy efficiency of electricity grid equipment.Then novel materials and process are explored and investigated for non-reducible dielectrics.This thesis focuses on the busbar load forecasting in electric power load forecasting.Based on the basic concepts and advantages and disadvantages of several basic algorithms for load forecasting,the J2EE-based web programming system development tool,Visual Studio,is combined with the optimal algorithm of busbar load forecasting to design a set of short-term electric power load forecasting system software.the research significance of the busbar load forecasting system,and analyzes the shortcomings of the current short-term load forecasting system research.The busbar electric power load prediction for the specified date is realized by writing the data of previous years.Based on the comprehensive description,prediction algorithm and detailed description of bus load prediction system,this thesis constructs a detailed short-term load prediction system based on J2 EE architecture web programming.Using the current advanced neural network algorithm and cloud model data correction,the system focuses on short-term load prediction,and solves the problem of low accuracy and low usability of the existing bus prediction application software by predicting the short-term curve direction through a large amount of data.The development of this system is of great significance to the research of bus load forecasting model selection,information preprocessing and combination forecasting model.