Hourly Irradiance Forecasting in Malaysia Using Support Vector Machine

被引：0

作者：

Baharin, Kyairul Azmi ^{[1
]}

Abd Rahman, Hasimah ^{[1
]}

Hassan, Mohammad Yusri ^{[1
]}

Gan, Chin Kim ^{[2
]}

机构：

[1] UTM Johor Bahru, Ctr Elect Energy Syst CEES, Johor Baharu, Malaysia

[2] UTeM, Fac Elect Engn, Melaka, Malaysia

来源：

2014 IEEE CONFERENCE ON ENERGY CONVERSION (CENCON) | 2014年

关键词：

solar irradiance forecasting; SVM; MLP; SOLAR; ENERGY;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence forecast. The evaluation metrics used to validate each model's performance are mean bias error, root mean square error, mean absolute error/average, and Kolmogorov-Smirnov integral test. Results show that the SVM performs significantly better than the conventional MLP technique.

引用

页码：185 / 190

页数：6

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