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BP neural network-based ultra-short load prediction method

A load forecasting, neural network technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as the reduction of load forecasting accuracy

Inactive Publication Date: 2017-04-26
JINZHOU ELECTRIC POWER SUPPLY COMPANY OF STATE GRID LIAONING ELECTRIC POWER SUPPLY +1
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AI Technical Summary

Problems solved by technology

Due to the volatility and hysteresis of user-side response, cooling and heating loads, and electric vehicle charging, the accuracy of load forecasting is reduced

Method used

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  • BP neural network-based ultra-short load prediction method
  • BP neural network-based ultra-short load prediction method
  • BP neural network-based ultra-short load prediction method

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Experimental program
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Embodiment Construction

[0054] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0055] This method is based on taking the load data of 96 points on a certain day in a certain city as a sample, the daily information d is an integer from 1 to 7, and the cycle is weekly. Specific steps such as figure 1 Shown:

[0056] Step 1 Perform microgrid data preprocessing

[0057] (1) Mutation data identification, according to the following formula

[0058]

[0059] L d,t is the load value at time t of day d; u=0.01 is the judgment threshold; M is the last moment of day d-1. When the load change rate at adjacent moments is greater than the judgment threshold, the load data L d,t It is the mutation data and is eliminated, and then the data at this moment is completed as the missing data;

[0060] (2) Incomplete data use the following formula

[0061]

[0062] L d-m,t is the load data at time t of day d-m; λ is the weight coefficient, which means L ...

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Abstract

The invention discloses a BP neural network-based ultra-short load prediction method. The method includes the following steps of: step 1, microgrid load data identification and completion; step 2, microgrid load normalization processing; step 3, microgrid data de-noising processing; step 4, load sample difference calculation; step 5, RBF neural network load prediction mathematical model establishment and on-line prediction. According to the BP neural network-based ultra-short load prediction method, based on a traditional RBF neural network load prediction mathematical model, load sample difference is defined through a regression analysis method; and the prediction model is adjusted according to the load sample difference, so that the load prediction model can automatically adapt to load change.

Description

technical field [0001] The invention belongs to the technical field of smart grid control, in particular to a short-term load forecasting method for a microgrid based on an RBF neural network. Background technique [0002] Ultra-short-term load forecasting, also known as time-division forecasting, refers to predicting the load changes within 1 hour in the future with a cycle of 5 to 30 minutes. Due to the short interval between forecasting times and the influence of weather and other factors on load changes, external conditions such as weather are usually ignored in the forecasting; the forecasting system runs online and quickly and accurately predicts the load at the next moment based on historical data in real time Changes, forecast results usually do not require human processing. Ultra-short-term load forecasting plays an extremely important role in power system state estimation, real-time dispatch, automatic generation control, and real-time electricity price formulatio...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06Q10/04G06N3/084G06Q50/06
Inventor 崔吉生王涛刚宏邱鹏
Owner JINZHOU ELECTRIC POWER SUPPLY COMPANY OF STATE GRID LIAONING ELECTRIC POWER SUPPLY
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