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Load prediction method and memory and system comprising same

A load forecasting and historical load technology, applied in the fields of memory and system, can solve the problems of subjectivity of intelligent forecasting algorithm, system complexity and efficiency, and achieve the effect of simple and intuitive clustering and accurate results.

Active Publication Date: 2019-08-27
广州水沐青华科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the blindness of empirical methods in the prior art, the subjectivity, system complexity and efficiency of intelligent forecasting algorithms, the present invention provides a load forecasting method to solve the above problems

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  • Load prediction method and memory and system comprising same
  • Load prediction method and memory and system comprising same
  • Load prediction method and memory and system comprising same

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Embodiment Construction

[0042] The invention provides a load forecasting method. Several major factors affecting power load are extracted and used as the basis for clustering, and then polynomial fitting is performed on each clustering result to obtain multiple functions, and finally the final prediction function is integrated according to the contribution rate of the cluster. The present invention effectively improves the polynomial fitting algorithm, and improves the accuracy of the algorithm while ensuring the simple and operable prediction algorithm. Hereinafter, the present invention will be further described with reference to the drawings and specific implementations:

[0043] The present invention analyzes the data through the principal component analysis method, and obtains that temperature, humidity, and wind speed are important factors that affect the electric load, and in the same climate, the electric load on working days and non-working days is significantly different. Therefore, historica...

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Abstract

The invention discloses a power load prediction method, which comprises the following steps of carrying out preliminary division on the data according to whether the holidays and festivals exist, andclustering the result of the preliminary division to carry out the secondary division according to the temperature, humidity and wind speed to obtain a plurality of small categories; secondly, performing polynomial fitting on each small classification to obtain a plurality of fitting functions; and finally, integrating into a final prediction function according to each classification. According tothe method, a polynomial fitting algorithm is effectively improved, and the accuracy of the algorithm can be improved under the condition that the prediction algorithm is simple and operable.

Description

Technical field [0001] The present invention relates to the field of power load forecasting in smart grids, and in particular to a load forecasting method and a memory and system containing the method. Background technique [0002] As the basis of power system operation, power load forecasting can guide the configuration of power equipment and the supply of power. The accuracy of the forecast is directly related to the effect of the entire power system. If the forecast result is lower than the actual value, the power demand cannot be met, and there will be a large-scale blackout, which may even cause the system to be paralyzed; if the forecast result is higher than the actual value, configure The power equipment will be left unused, causing a waste of resources. [0003] Traditionally, empirical methods are used to predict mid-to-long-term power load. “Pork divided from top to bottom” or “platters from bottom to top” are weaker in theory and the results of distribution are determi...

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62G06Q50/06
CPCG06Q10/04G06Q50/06G06F18/23211G06F18/2415
Inventor 孙立明
Owner 广州水沐青华科技有限公司