Research On Electricity Inspection And Management Of Qingdao Power Grid Based On Load Forecasting

In recent years,with the continuous adjustment of social energy structure changes and the continuous development of regional economy,the whole society’s dependence on electricity continues to deepen,customer electricity consumption continues to grow,power equipment manufacturing industry upgrading and upgrading,the number of types of electrical equipment used by customers operating in the grid continues to grow,its uneven level of equipment health.At the same time,with the continuous improvement of the level of social information,electricity inspection management has become increasingly grim situation facing.Therefore,this paper focuses on the problems faced by Qingdao Electric Power Company in the electricity inspection management,through the short-term load forecast of electricity customers for the electricity inspection management to improve efficiency and relevance of great significance.Firstly,this paper briefly introduces the significance and background of the topic selection,the basic overview of the system used in electricity inspection management,the basic concepts,characteristics and influencing factors of short-term load forecasting.Then through the comparison of several models,this paper selects the widely used BP neural network to build the forecasting model.Through in-depth study of BP algorithm modeling theory and principles,combined with historical data for a load electricity customers,the paper established a BP neural network prediction model by the use of MATLAB toolbox.And then use the model to predict the new day’s electric vehicle daily load,comparing with the actual load,the forecast result has reached the requirement.Finally,the effectiveness of the proposed method is validated by the application of this method in electricity inspection management.