Multi-model solar irradiance prediction based on automatic cloud classification

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107065

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107065

Title:	Multi-model solar irradiance prediction based on automatic cloud classification
Authors:	鄭旭詠;Cheng, Hsu-Yung;Yu, Chih-Chang
Contributors:	資訊電機學院資訊工程學系
Keywords:	Classification;Cloud classification;Clouds;Errors;Image classification;Irradiance;light intensity;Mathematical models;opacity;Prediction;regression analysis;Solar irradiance;solar radiation;Surface layer;Texture
Date:	2015-11-01
Issue Date:	2026-04-23 13:55:02 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： This paper proposes a framework to automatically conduct cloud classification on all-sky images and perform short-term solar irradiance prediction according to the classification results. The all-sky images are divided into blocks to deal with the mixed cloud type conditions. Local texture patterns and statistical texture features are extracted from the image blocks for cloud classification. Different cloud types with various heights, thickness, and opacity have different impact on the variation of solar irradiance. Therefore, several regression models are trained to capture the characteristics of irradiance changes under different cloud types. The current classified cloud type is used to select a corresponding prediction model. Such design substantially increases the prediction accuracy. The experimental results verify the effectiveness of the proposed framework. Both the proposed cloud classification method and irradiance prediction mechanism outperform existing works. Adding local texture patterns in the feature vector enhance the classification performance. Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%–10%. Utilizing multiple prediction models according cloud types could lower both the mean absolute error and the root mean squared error on short-term irradiance prediction. •Cloud types are classified automatically based on all-sky images.•Block-based classification deals with mixed cloud type conditions.•Incorporating local pattern descriptors improve classification accuracy.•Multiple regression models for various cloud types are trained for irradiance prediction.•Separating prediction models captures different characteristics of irradiance variations well. 出版者： Elsevier Ltd 出版日期： 2015-11 出處： Energy (Oxford), 2015-11, Vol.91, p.579-587 版權： 2015 Elsevier Ltd 識別號： ISSN: 0360-5442 識別號： DOI: 10.1016/j.energy.2015.08.075
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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