Short-term photovoltaic power prediction method based on meteorological factor weight similar day

A meteorological factor and power prediction technology, applied in the direction of prediction, genetic model, genetic law, etc., can solve the same weight error and other problems, and achieve the effect of high model accuracy

Active Publication Date: 2018-09-21
HOHAI UNIV
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Problems solved by technology

In this way, the same weight in the traditional method will cause a large error

Method used

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  • Short-term photovoltaic power prediction method based on meteorological factor weight similar day
  • Short-term photovoltaic power prediction method based on meteorological factor weight similar day
  • Short-term photovoltaic power prediction method based on meteorological factor weight similar day

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[0050] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0051] figure 1 The shown k-means algorithm takes k as a parameter, and divides n objects into k clusters, so that the similarity within the cluster is high, and the similarity between clusters is low. The processing process of the k-means algorithm is as follows: first, k objects are randomly selected, and each object initially represents the mean or center of a cluster; for each remaining object, according to its distance from the center of each cluster, the It assigns to the nearest cluster; then recomputes the mean for each cluster. This process is repeated until the criterion function converges.

[0052] image 3 Shown is the structural diagram of the RBF-BP combined neural network. The ...

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Abstract

The invention discloses a short-term photovoltaic power prediction method based on a meteorological factor weight similar day. The method comprises the following steps that: S01: calculating the Pearson coefficient of photovoltaic power and a meteorological factor to extract a main impact factor; S02: classifying historical days by a k-means clustering algorithm; S03: according to grey relationalanalysis, obtaining the weight of the meteorological factors for generation power in different categories; S04: according to similarity statistic magnitude with weight, calculating a similarity between a new sample and each clustering center, and taking the category with the high similarity as the category of the new sample; S05: selecting seven historical days with the highest similarity as similar days to obtain a similar day sample training set; and S06: establishing an RBF-BF (Radial Basis Function-Back Propagation) combined neural network model based on genetic algorithm optimization forprediction. By use of the short-term photovoltaic power prediction method based on the meteorological factor weight similar day, photovoltaic power prediction accuracy can be effectively improved, andthe method is high in practicality.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic power generation, and in particular relates to a short-term photovoltaic power prediction method based on meteorological factor weight similar days. Background technique [0002] Photovoltaic power generation technology has developed rapidly in recent years and plays an important role in renewable energy power generation. Compared with traditional power generation technology, photovoltaic power generation technology is cleaner, more environmentally friendly, energy-saving and easy to maintain. And its flaws are also obvious, it is volatile and random, and it is greatly affected by the weather. This low controllability will have a negative impact on the dispatch of the power grid, so the power forecasting technology of photovoltaic power generation is particularly important. [0003] With the continuous deepening of research, photovoltaic power forecasting technology has been greatly develop...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06F17/30G06N3/12
CPCG06N3/126G06Q10/04G06Q50/06Y04S10/50
Inventor 王冰李伟
Owner HOHAI UNIV
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