Novel solar forecasting scheme modelled by mixer dual path network and based on sky images

Zhu, Tongsen, Jiao, Xuan, Li, Xingshuo, Yin, Xuening, Du, Yang, Ding, Shuye, and Xiao, Weidong (2023) Novel solar forecasting scheme modelled by mixer dual path network and based on sky images. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 6. 100315.

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Abstract

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.

Item ID: 80807
Item Type: Article (Research - C1)
ISSN: 2772-6711
Keywords: Convmixer architecture, Deep learning (DL), Dual path network (DPN), Solar forecasting
Copyright Information: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited: 16 Feb 2024 04:54
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400808 Photovoltaic power systems @ 100%
SEO Codes: 17 ENERGY > 1708 Renewable energy > 170804 Solar-photovoltaic energy @ 100%
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