Short-term load prediction method based on cloud model

A technology of short-term load forecasting and cloud model, applied in the field of electric power system

Inactive Publication Date: 2016-06-15
STATE GRID HUBEI ELECTRIC POWER COMPANY +2
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AI Technical Summary

Problems solved by technology

The existing forecasting technology can achieve a certain forecasting accuracy, but with the rapid development of the power industry, the nature and load of the power load are also changing rapidly, and new forecasting methods are needed to meet the needs of future power load forecasting

Method used

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  • Short-term load prediction method based on cloud model
  • Short-term load prediction method based on cloud model
  • Short-term load prediction method based on cloud model

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Embodiment

[0055] The present invention establishes a three-layer load classification model based on the historical load data of a certain area, conducts a preliminary classification of the load according to the first-level index and the second-level index, and then extracts the corresponding feature quantity of the third-level index and uses the way of scoring to reflect the load. size. The load score is determined by the membership function and the analytic hierarchy process. Finally, the load cloud image is obtained through the weighted deviation degree, and the load is classified based on the cloud image. On the premise of accurate classification, the BP neural network algorithm is used to predict the forecast daily load. The specific implementation example is as follows:

[0056] Step 1: Read the historical load data recorded in a certain place, and build a three-layer load classification model based on seasons, day types and meteorological factors, such as figure 1 shown. Accordi...

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Abstract

The present invention relates to a short-term load forecasting method based on a cloud model. First, a three-layer classification model is established based on seasons, day types and meteorological factors, and the third-level index is extracted through the correlation coefficient method, that is, the characteristic quantity of meteorological factors affecting the load size. According to the different mechanisms of the influence of characteristic quantities on the load, the corresponding scoring standards are formulated, and the scores of each three-level index are obtained by using the membership function. The larger the score, the greater the load of the index. Then according to the importance of each index, the weight value of each index is obtained by using the AHP, and based on the cloud model, the weighted deviation degree is obtained, and the cloud map is drawn, and the load is classified through the cloud map. Finally, the score obtained by the feature quantity of the forecast day is calculated, classified according to the load, and classified into its category. Based on the bp neural network, the load data of the category to which the load belongs is used as a training sample to predict the load of the forecast day. The invention has higher classification recognition accuracy and stronger adaptability.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to a short-term load forecasting method based on a cloud model. Background technique [0002] Short-term load forecasting is an important part of load forecasting. Power system short-term load forecasting mainly refers to forecasting the power load in the next few hours, one day to several days, and is the basis for making dispatching plans, power supply plans, and transaction plans. It is essential for stable operation. At present, there are many methods of short-term load forecasting, but with the increasing marketization of power production and consumption, the requirements for the accuracy, real-time and reliability of load forecasting are getting higher and higher. [0003] The load is changing dynamically with time, and meteorological factors, day types, season types, etc. have a great influence on the load. To improve the accuracy of short-term loa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06Q10/04G06N3/084G06Q50/06
Inventor 夏怀民周想凌杨军王新普邢杰邱丹刘焱郝婉梦陈杰军李扬
Owner STATE GRID HUBEI ELECTRIC POWER COMPANY
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