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Article

3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting

by 1,2,3,4, 1,3,4, 1,2,3,4,* and 1,3,4
1
Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399, China
2
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China
3
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
4
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Armando Carravetta
Water 2021, 13(13), 1773; https://doi.org/10.3390/w13131773
Received: 31 May 2021 / Revised: 16 June 2021 / Accepted: 24 June 2021 / Published: 27 June 2021
(This article belongs to the Special Issue New Perspectives in Agricultural Water Management)
The instability and variability of solar irradiance induces great challenges for the management of photovoltaic water pumping systems. Accurate global horizontal irradiance (GHI) forecasting is a promising technique to solve this problem. To improve short-term GHI forecasting accuracy, ground-based sky image is valuable due to its correlation with solar generation. In previous studies, great efforts have been made to extract numerical features from sky image for data-driven solar irradiance forecasting methods, e.g., based on pixel-value color information, and based on the cloud motion detection method. In this work, we propose a novel feature extracting method for GHI forecasting that a three-dimensional (3D) convolutional neural network (CNN) is developed to extract features from sky images with efficient training strategies. Popular machine learning algorithms are introduced as GHI forecasting models and corresponding forecasting accuracy is fully explored with different input features on a large dataset. The numerical experiment illustrates that the minimum average root mean square error (RMSE) of 62 W/m2 is achieved by the proposed method with 15.2% improvement in Skill score against baseline forecasting method. View Full-Text
Keywords: 3D CNN; feature engineering; global horizontal irradiance; machine learning algorithm; sky image 3D CNN; feature engineering; global horizontal irradiance; machine learning algorithm; sky image
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MDPI and ACS Style

Yang, H.; Wang, L.; Huang, C.; Luo, X. 3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting. Water 2021, 13, 1773. https://doi.org/10.3390/w13131773

AMA Style

Yang H, Wang L, Huang C, Luo X. 3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting. Water. 2021; 13(13):1773. https://doi.org/10.3390/w13131773

Chicago/Turabian Style

Yang, Hao, Long Wang, Chao Huang, and Xiong Luo. 2021. "3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting" Water 13, no. 13: 1773. https://doi.org/10.3390/w13131773

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