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Solar Radiation Prediction Based on TCN-N-BEATS Sequence Modeling

Authors :
Ruiyu He
Xin Tang
Li Fang
Ying Zhou
Yuanjun Chen
Junjie Xiong
Xiangru Chen
Source :
Advances in Meteorology, Vol 2024 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Solar radiation prediction research is a key area of interest in the realm of solar energy utilization and has garnered significant attention in recent times. In order to realize accurate prediction of solar radiation and make solar radiation prediction better serve photovoltaic (PV) power generation, this study proposes a solar radiation prediction method based on sequence model, which integrates two kinds of neural networks, namely, temporal convolutional network (TCN) and neural basis expansion analysis (N-BEATS). First, the dataset is preprocessed using Pearson’s correlation coefficient, outlier detection, and normalized to obtain valid and relevant data; second, the features of TCN feature extraction and N-BEATS flexible extension are integrated to predict the solar radiation; then, the model’s hyperparameters are fine-tuned using the grid search algorithm to ensure precise predictions; and last, the correctness of the method is verified by comparing the error metrics and the running time. Empirical findings indicate that the TCN-N-BEATS sequence model has high prediction accuracy and short time overhead, and it has certain application value in solar radiation prediction, and the model could offer valuable insights for predicting solar radiation.

Subjects

Subjects :
Meteorology. Climatology
QC851-999

Details

Language :
English
ISSN :
16879317
Volume :
2024
Database :
Directory of Open Access Journals
Journal :
Advances in Meteorology
Publication Type :
Academic Journal
Accession number :
edsdoj.27200681fc164f40b7184ad270e6646e
Document Type :
article
Full Text :
https://doi.org/10.1155/2024/1446369