Predicting solar irradiance with all-sky image features via regression

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107141

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107141

Title:	Predicting solar irradiance with all-sky image features via regression
Authors:	鄭旭詠;Fu, Chia-Lin;Cheng, Hsu-Yung
Contributors:	資訊電機學院資訊工程學系
Keywords:	All-sky image;Applied sciences;Energy;Energy efficiency;Exact sciences and technology;Natural energy;Power supply;Regression;Regression analysis;Solar energy;Solar irradiance prediction;Solar radiation
Date:	2013-11-01
Issue Date:	2026-04-23 13:57:50 (UTC+8)
Publisher:	Elsevier Ltd.;Kidlington: Elsevier Ltd
Abstract:	摘要： •Features are extracted from all-sky images for high resolution solar irradiance prediction.•Different features are analyzed and compared for feature selection.•A regression technique with clearness index conversion scheme is designed.•The propose method demonstrates substantial improvement on prediction accuracy.•Prediction at 5min in advance is achieved with MAE of 22% for a highly challenging dataset. To address the problem of forecasting solar irradiance for grid operators, the aim of this work is to automatically predict solar irradiance several minutes in advance. This work presents a solar irradiance prediction scheme that utilizes features extracted from all-sky images. To select a proper feature subset for prediction, various features are analyzed and compared. We propose to utilize the regression technique to predict clearness index and then to calculate the desired solar irradiance from the predicted clearness index. We validate the effectiveness of the proposed scheme using a challenging dataset collected at a coastal site. The experiments have shown that the designed clearness index prediction mechanism yields better prediction results than predicting solar irradiance directly. Also, irradiance prediction at 5min in advance can be achieved with mean absolute error of around 22%. The results of this work could provide very useful information for grid operators to ensure greater efficiency of the renewable energy supply. 出版者： Kidlington: Elsevier Ltd 出版日期： 2013-11-01 出處： Solar energy, 2013-11, Vol.97, p.537-550 版權： 2013 Elsevier Ltd 版權： 2014 INIST-CNRS 版權： Copyright Pergamon Press Inc. Nov 2013 識別號： ISSN: 0038-092X 識別號： EISSN: 1471-1257 識別號： DOI: 10.1016/j.solener.2013.09.016 識別號： CODEN: SRENA4
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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