Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network

Liu, Yongqi; Hui, Qing; Zhang, Zhendong; Pei, Shaoqian; Wang, Chao; Yu, Xiang; Jiang, Zhiqiang; Zhou, Jing

doi:10.1016/j.apenergy.2019.113596

Cited by 107 publications

(25 citation statements)

References 41 publications

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“…For example, Zhou et al [45] proposed a Bayesian framework of the variational graph cyclic attention neural network for robust traffic prediction. Similarly, a variational Bayesian network predicts solar radiation [46] and energy price [47]. These papers show that the variational Bayesian method can overcome the influence of uncertain data with noise, improving the prediction accuracy.…”

Section: Related Workmentioning

confidence: 94%

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

Jin

Gong

Kong

et al. 2022

Entropy

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Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing’s air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.

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Section: Related Workmentioning

confidence: 94%

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

Jin

Gong

Kong

et al. 2022

Entropy

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“…In [13], an uncertainty metric, the dropout technique, and the deep learning structure are combined to construct PIs. Bayesian deep learning structure is used to predict point values, and the variational interface is used to construct PIs of large-scale solar generations in [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Afrasiabi

Mohammadi

Rastegar

et al. 2020

IET Generation, Transmission & Distribution

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“…Probabilistic forecasting is a more comprehensive method than interval method, such as Bayesian theory and Gaussian process regression (GPR) [25]. Liu et al obtained the spatiotemporal probabilistic forecasting results of solar radiation intensity based on deep learning method and Bayesian inference [26]. Yang et al used GPR to obtain the probability density function (PDF) of solar power output, which can provide a reference for decision-makers to avoid future risks [27].…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression

et al. 2021

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Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time series clustering (K-MTSC) algorithm is first proposed to cluster solar radiation intensity and compared with astronomy method and K-means. Then, different feature inputs for different categories of solar radiation intensity are screened. Afterwards, the different kernel functions of Gaussian process regression (GPR) are compared and optimal kernel function is selected in terms of deterministic forecasting and probabilistic forecasting for different categories. Finally, the case study in Tibet province, China are performed to verify the validity and practicability of this research model and method. In this experiment, the average accuracy of GPR is 44% higher than that of Artificial Neural Network ANN, and 17% higher than that of Support Vector Regression. The experiments show that (1) the clustering results obtained by the K-MTSC algorithm have a larger inter-group distance and a smaller intra-group distance, and at the same time, it will not destroy the continuity of the time series. (2) The probability forecast results obtained by GPR are reliable and high-accuracy.INDEX TERMS solar radiation intensity; K-means Time Series Clustering; probabilistic forecasting; Gaussian process regression.

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Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network

Cited by 107 publications

References 41 publications

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression

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