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Photovoltaic output probability distribution prediction method based on Bayesian-long short-term memory neural network

A long-short-term memory and neural network technology, applied in the field of photovoltaic output probability distribution prediction based on Bayesian-long-short-term memory neural network, can solve the problems of difficult to eliminate errors and uncertainty of prediction results, and achieve strong characterization ability, The effect of accurate uncertainty information

Pending Publication Date: 2022-03-22
GUIZHOU POWER GRID CO LTD
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Problems solved by technology

Due to the large number of high-uncertainty power sources such as photovoltaics, the emergence of active loads such as new energy vehicles, and the randomness of weather conditions, it is difficult to eliminate prediction errors, and there is a high degree of uncertainty in the prediction results

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  • Photovoltaic output probability distribution prediction method based on Bayesian-long short-term memory neural network
  • Photovoltaic output probability distribution prediction method based on Bayesian-long short-term memory neural network
  • Photovoltaic output probability distribution prediction method based on Bayesian-long short-term memory neural network

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specific Embodiment

[0154] 1. Correlation analysis of meteorological factors

[0155] Using the photovoltaic output and numerical meteorological data of the park in a certain area, the path analysis method is used to calculate the correlation coefficient. The results are shown in Table 1 below.

[0156] Table 1

[0157] Meteorological factors correlation coefficient Relative humidity -0.215 total cloud cover 0.059 total precipitation -0.047 Variation rate of solar radiation at the top -0.316 temperature 0.239 surface irradiance change rate -0.287 Surface thermal radiation change rate -0.259 surface atmospheric pressure 0.028

[0158] Select the features with the absolute value of the correlation coefficient greater than 0.2 in the selection table, and select relative humidity, top solar radiation change rate, temperature, surface light radiation change rate, and surface thermal radiation change rate as meteorological factor features....

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Abstract

The invention provides a photovoltaic output probability distribution prediction method based on a Bayesian-long short-term memory neural network, so as to improve the performance of predicting photovoltaic output probability distribution by using the neural network. According to the method, the probability prediction technology is utilized, the probability distribution of the prediction uncertainty is estimated, quantitative analysis of the prediction uncertainty is realized, richer and more accurate uncertainty information is provided for the new energy power system, and the method for comprehensively considering meteorological factors and historical data is provided. According to the method and the device for predicting the photovoltaic output uncertainty by using the neural network, the uncertainty can be depicted more strongly.

Description

technical field [0001] The invention relates to the field of new energy technology, in particular to a method for predicting the probability distribution of photovoltaic output based on Bayesian-long short-term memory neural network. Background technique [0002] The power of new energy power generation has strong randomness, which brings severe challenges to the economic operation of the power system. Its accurate prediction is of great significance to improve the overall energy efficiency of the system and promote the consumption of new energy. High-precision new energy forecasting provides an important guarantee for power system planning and safe and economical operation. Due to the large number of high-uncertainty power sources such as photovoltaics, the emergence of active loads such as new energy vehicles, and the randomness of weather conditions, it is difficult to eliminate prediction errors, and there is a high degree of uncertainty in the prediction results. . C...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/044Y04S10/50
Inventor 马覃峰陈锐刘明顺安甦曹杰贺先强朱灵子张丹吴应双王国松
Owner GUIZHOU POWER GRID CO LTD
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