A novel convolutional neural network framework based solar irradiance prediction method

•A novel convolutional neural networks has been constructed to predict irradiance.•The chaotic hybrid algorithm is applied to optimize hyperparameters of framework.•The chaotic hybrid algorithm alleviates the imperfect performance of framework.•The novel framework can be further applied into other a...

Full description

Saved in:
Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 114; p. 105411
Main Authors: Dong, Na, Chang, Jian-Fang, Wu, Ai-Guo, Gao, Zhong-Ke
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2020
Subjects:
Convolutional neural network
Hybrid optimization algorithm
Hyperparameter
Renewable energy
Solar irradiance prediction
Renewable energy
Convolutional neural network
45E10
Hybrid optimization algorithm
Hyperparameter
90C26
Solar irradiance prediction
ISSN:0142-0615, 1879-3517
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A novel convolutional neural networks has been constructed to predict irradiance.•The chaotic hybrid algorithm is applied to optimize hyperparameters of framework.•The chaotic hybrid algorithm alleviates the imperfect performance of framework.•The novel framework can be further applied into other areas of renewable energy. As an important part of solar power system, photovoltaic grid-connected system and solar thermal system, solar irradiance has the inherent characteristics of variability and uncertainty. Existing data analysis methods are difficult to demonstrate better generalization. Hence, resource planners must be adaptable to accommodate these uncertainties while conducting planning. To improve the accuracy of solar energy prediction and efficiently organize the utilization of solar energy, a novel convolutional neural networks framework has been constructed. Firstly, we have established a convolutional neural network framework for solar prediction based on meteorological data from surrounding sites and different sampling times. Secondly, the chaotic GA/PSO1Genetic Algorithm/Particle Swarm Optimization.1 hybrid algorithm is applied to optimize the hyper parameters of the novel framework, which alleviates the imperfect performance caused by improper hyper parameters. At the meantime, the hybrid algorithm can reduce the manpower and resources of manual parameter adjustment. Excitability of the novel framework has been verified by benchmark tests. In the solar irradiance prediction studies, the annual average Mean Absolute Error of the proposed method is reduced by 0.1463 MJ·m-2 compared with single CNN2Convolutional neural network.2 framework. The annual average Mean Absolute Error of the proposed method is reduced by 49.47%, 47.6%, 20.34%, respectively, compared with ANN3Artificial Neural Network.3, K-means-RBF4kmeans-Radical Basis Function.4 and GBRT.5Gradient Boosted Regression Trees.5 The superiority has been fully illustrated through all the simulation test results. Therefore, the proposed method provides a basis for accurate estimation of solar power, which can promote further development of the whole power system.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2019.105411