Electric power system short period load prediction method based on combined neural network

A short-term load forecasting and power system technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as difficulties in accurate forecasting

Inactive Publication Date: 2018-04-13
ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER +1
View PDF5 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to such characteristics of short-term load,

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electric power system short period load prediction method based on combined neural network
  • Electric power system short period load prediction method based on combined neural network
  • Electric power system short period load prediction method based on combined neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] In conjunction with the accompanying drawings, the technical solution will be further described.

[0053] Step 1: Collect and summarize the data collection and summary of power grid load data and meteorological data in historical areas, and import them into the Excel database.

[0054] Step 2: Data preprocessing. In order to avoid the occurrence of neuron saturation, it is necessary to preprocess the original load data. This will help the convergence of the training process and improve the prediction accuracy. The main preprocessing method is to count the maximum and minimum values ​​of the historical load data in the training sample set, and normalize the load data to the [-1, 1] interval, which can make the data at the same level and speed up the convergence of the neural network .

[0055] Step 3: Determine the model structure.

[0056] The generalization performance of a single NN in different sample input spaces is different, which will affect the prediction accu...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the power system short-term load prediction technology field and discloses an electric power system short period load prediction method based on the combined neural network. The method comprises steps that (1), historical power grid load and meteorological data are collected, and a database is built for backup; (2) abnormal data acquired in the step (1) is removed, and normalization processing of the residual data is carried out; (3), a model structure having feedforward and feedback functions is determined; (4), the historical data is utilized to train a prediction model, and model parameters and weights are determined; and (5), the prediction model is utilized for actual load prediction to acquire a prediction load value. The method is advantaged in that the combined neural network is composed of an AM-NN sub model based on the additional momentum method and a QN-NN sub model based on the Quasi-Newton method, the two models are fused through a time-varying comprehensive weight coefficient, meteorological factor data is introduced to the model, the rolling optimization strategy is employed, the model is made to have relatively good generalization and convergence property, and actual scheduling prediction requirements can be more precisely satisfied.

Description

technical field [0001] The invention relates to the technical field of short-term load forecasting of electric power systems, in particular to a method for short-term load forecasting of electric power systems based on combined neural networks. Background technique [0002] As the scale and complexity of power systems continue to increase, the accuracy of short-term load forecasting in power systems plays a key role in effectively reducing power generation costs and implementing optimal control of power systems in various regions. Compared with long-term load forecasting, short-term load forecasting is mainly used to arrange power generation planning, and has the highest timeliness. Its load changes quickly and is greatly affected by abrupt factors such as temperature difference and humidity, and belongs to a dynamic nonlinear time series. Due to such characteristics of short-term load, it is difficult to achieve accurate forecasting. With the implementation of the new ele...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06N3/08G06Q10/04G06Q50/06G06N3/045
Inventor 薛会胥晓晖张健王群李瑶朱新颖张英彬张智晟
Owner ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap