Electrical power system load prediction method based on back propagation (BP) neural network

A BP neural network and load forecasting technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as differences in load changes, achieve high accuracy, ensure safe and stable operation, and be economical

Inactive Publication Date: 2013-09-11
SHANGHAI DIANJI UNIV
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  • Electrical power system load prediction method based on back propagation (BP) neural network
  • Electrical power system load prediction method based on back propagation (BP) neural network

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[0011] The BP neural network-based power system load forecasting method of the present invention will be described in detail below in conjunction with the accompanying drawings, but it should be pointed out that the embodiments and examples of the present invention are preferred solutions for the purpose of explanation, and are not intended to limit the scope of the present invention.

[0012] see figure 1 , the flow chart of the BP neural network-based power system load forecasting method of the present invention, and the steps of the method will be described in detail next.

[0013] S11: Determine the input and output vectors according to the specified electric load forecast.

[0014] Treat each day of the 7 days of the week as a type, let the neural network learn the potential relationship of these seven types, and inform the prediction of the load type of the power system on that day.

[0015] Step S11 can be realized by following steps:

[0016] (11) Measure the power l...

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Abstract

An electric power system load prediction method based on a back propagation (BP) neural network comprises the following steps of (1) confirming input-output vectors according to specified electric power load prediction; (2) establishing a BP neural network model according to the input-output vectors; (3) training the BP neural network; (4) inputting a test sample to test the trained BP neural network, judging whether an error of an output predicted value and an actual value is smaller than a set threshold value or not and if so, carrying out a step (5); and (5) obtaining actually required load prediction according to the predicted value. The implicit presentation of internal relation of predictive factors is achieved through automatic learning of the BP neural network model and weight distribution of the BP neural network, the accuracy of the electric power system load prediction is high, and safe and stable operation and economical efficiency of an electric power system can be guaranteed effectively.

Description

technical field [0001] The invention relates to the technical field of power system load forecasting, in particular to a power system load forecasting method based on a BP neural network. Background technique [0002] Power system load forecasting is an important part of power system planning and the basis of power system economic operation, which is extremely important to power system planning and operation. Through accurate load forecasting, it is possible to economically and rationally arrange the start and stop of units, reduce spinning reserve capacity, arrange maintenance plans reasonably, reduce power generation costs, and improve economic benefits. [0003] According to the forecast time, load forecasting can be divided into long-term, medium-term and short-term load forecasting. Among them, in the short-term load forecasting, weekly load forecasting (for the next 7 days), daily load forecasting (for the next 24 hours) and several hours in advance forecasting are cr...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
Inventor 杨明莉刘三明王致杰张卫丁国栋李义新高叶军
Owner SHANGHAI DIANJI UNIV
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