Power consumption prediction method for system

Abstract

For accurately measuring the power consumption, the invention provides a power consumption prediction method for a system. The method comprises S1 data acquisition: acquiring the power utilization information of residents, industrial users and business users through an intelligent ammeter, acquiring the influence factor information through an external system, and generating a history table for the users at the same time; S2 analysis of power utilization rules for the users: performing fitting analysis of the acquired power utilization information of the users by means of combination with the history table, and obtaining the power utilization rules for the users through analysis; S3 analysis of influence factors: setting a load fluctuation range according to the user type, and performing analysis of influence factors for the time point surpassing the fluctuation range to obtain the influence value of each influence factor for the power consumption of the users; and S4 power consumption prediction for a system: predicting the future short-term power consumption for the system by means of combination of analysis of power utilization rules and analysis of influence factors. The prediction method shows a brand new development direction for short-term power consumption prediction in future, and can be widely applied to the prediction field with great significance.

Description

technical field [0001] The invention belongs to the field of electricity consumption analysis of electric power systems, and relates to a system electricity consumption prediction method. Background technique [0002] The research on short-term power consumption prediction of distribution network system has a long history. For a long time, the object of short-term power consumption prediction is usually limited to the whole network system. Scholars at home and abroad have done a lot of theoretical and methodological research work on this. , put forward a variety of forecasting methods with their own characteristics, such as time series method, expert system method, artificial neural network method and so on. [0003] Short-term power consumption has obvious periodicity, such as the similarity of daily power consumption curves, the similarity of days of the same week, the similarity of holidays, etc. In addition, short-term power consumption is easily affected by various envi...

AI Technical Summary

Problems solved by technology

Therefore, the existing forecasting methods, such as time series method, expert system method, artificial neural network method, etc., are not ideal in practical application, and it is difficult to establish a relationship model between system power consumption and many influencing factors.

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