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A Day-Ahead Power Prediction Method of Photovoltaic Power Plant Based on Learning Day Fitting Degree Clustering

Active Publication Date: 2021-09-10
北京鑫泰绿能科技有限公司
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  • Abstract
  • Description
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Problems solved by technology

However, due to the relatively severe fluctuations in the actual weather, the functional relationship between the learning set and the forecast daily data has parameter instability

Method used

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  • A Day-Ahead Power Prediction Method of Photovoltaic Power Plant Based on Learning Day Fitting Degree Clustering
  • A Day-Ahead Power Prediction Method of Photovoltaic Power Plant Based on Learning Day Fitting Degree Clustering
  • A Day-Ahead Power Prediction Method of Photovoltaic Power Plant Based on Learning Day Fitting Degree Clustering

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[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0028] It should be noted that when a component is said to be "fixed" to another component, it can be directly on the other component or there can also be an intervening component. When a component is said to be "connected" to another component, it may be directly connected to the other component or there may be intervening components at the same time. When a component is said to be "set on" another component, it may be set directly on the other component or t...

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Abstract

The present invention provides a day-ahead power prediction method for photovoltaic power plants based on learning day fitting degree clustering, aiming at the limitation that the ultra-short-term regression prediction of photovoltaic power plant optical power cannot filter the historical day fitting effect when selecting learning sets, Fully consider the influence of the lower fitting degree caused by historical day weather forecast accuracy and small fluctuations on the selection of learning sets. On the basis of taking into account the fitting degree of historical day power itself, use this parameter to correct the weight of similar day clustering. Then realize the day-ahead regression prediction of optical power. First obtain the weather and operating data of the photovoltaic power station, then calculate the fitting degree of the power data of the historical day photovoltaic power station, secondly use the fitting degree of the power itself as the weight to cluster and filter the learning set for the historical daily photovoltaic power station data, and finally analyze the ultra-short-term The power is used for regression prediction, which improves the prediction accuracy.

Description

technical field [0001] The invention belongs to the field of day-ahead power prediction of a photovoltaic power station, and in particular relates to a day-ahead power prediction method of a photovoltaic power station based on learning day fitting degree clustering. Background technique [0002] With the expansion of the installed capacity of the photovoltaic power generation system, its impact on the power system is becoming more and more obvious. The power forecast of the photovoltaic array plays an important role in alleviating the influence of the uncertainty of the photovoltaic power generation on the grid operation. Load forecasting is the basis for formulating power generation plan and long-term planning, and the accuracy of forecasting results is directly related to the economy and safety of power system operation. Therefore, improving the accuracy of photovoltaic power forecasting and load forecasting has always been one of the research directions of domestic and f...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q50/06
CPCG06Q10/04G06Q50/06G06F18/232
Inventor 翟伟翔
Owner 北京鑫泰绿能科技有限公司