Daytime solar radiation estimation model based on sparrow search algorithm-deep belief network

A technology of deep belief network and search algorithm, which is applied in the field of new energy power generation, can solve problems such as multiple time and energy, achieve the effect of improving accuracy and ensuring reliable operation

Pending Publication Date: 2021-10-15
HOHAI UNIV
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Problems solved by technology

Deep learning methods are usually affected by many parameters, but as we all know, setting deep learning parameters mainly relies on empirical methods, but this requires too much time and effort

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  • Daytime solar radiation estimation model based on sparrow search algorithm-deep belief network
  • Daytime solar radiation estimation model based on sparrow search algorithm-deep belief network
  • Daytime solar radiation estimation model based on sparrow search algorithm-deep belief network

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Embodiment Construction

[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0055] The invention discloses a daily solar radiation estimation model based on the sparrow search algorithm-deep belief network, which specifically includes the following steps:

[0056] 1. Obtain meteorological parameter data, air pollution data and daily solar radiation data, and use mutual information theory to select the best input variables. Specifically include the following steps:

[0057] (1) Calculate the mutual information value:

[0058] The mutual information I(X,Y) between random variables X and Y can be defined as:

[0059]

[0060] where x∈X, y∈Y, p(x,y) is the joint probability distribution function of X and Y, p(x) and p(y) are the marginal probability distribution functions of X and Y respectively;

[0061] (2) Use the maximum correlation minimum redundancy algorithm to select highly correlated input variables:

[0062] The maximum ...

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Abstract

The invention discloses a daytime solar radiation estimation model based on a sparrow search algorithm-deep belief network, and belongs to the technical field of new energy power generation. The method includes obtaining Meteorological parameter data, air pollution data and daytime total solar radiation data, and using a mutual information theory to select an optimal input variable; establishing an estimation model for the input variables, comparing estimation errors, and determining an optimal input variable; on the basis of selecting the optimal input variable set, establishing a solar radiation estimation model based on a deep belief network (DBN); and further analyzing the deep belief network model, optimizing deep belief network parameters by adopting a sparrow search algorithm, and establishing a deep belief network model based on the sparrow search algorithm. According to the invention, the deep belief network is improved by using the sparrow search algorithm, so that the estimation accuracy is improved; reliable operation of a photovoltaic system is guaranteed, and engineering practice application is better.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation, and in particular relates to a daily solar radiation estimation model based on a sparrow search algorithm-deep belief network. Background technique [0002] With the gradual reduction of fossil fuels, solar energy, as a representative of renewable energy, is now becoming an important part of human energy use, and its application technology is constantly improving. Solar radiation is an important factor affecting photovoltaic power generation technology. Therefore, in order to reduce the harm caused by the uncertainty of photovoltaic power generation system and improve the conversion efficiency between solar energy and light energy, we need to establish a model to estimate solar radiation. The amount of solar radiation received by a certain level of the earth's surface is affected by many factors, the most important of which is the atmospheric factor. Therefore, to estimate so...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06N3/00
CPCG06Q10/067G06Q50/06G06N3/006
Inventor 臧海祥夏倩倩许瑞琦蒋欣卫志农孙国强
Owner HOHAI UNIV
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