Hourly irradiance forecasting in Malaysia using support vector machine
| Autor: | Mohammad Yusri Hassan, Chin Kim Gan, Kyairul Azmi Baharin, Hasimah Abd Rahman |
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| Rok vydání: | 2014 |
| Předmět: |
Engineering
Index (economics) Mean squared error business.industry Irradiance Solar irradiance Machine learning computer.software_genre Support vector machine Computer Science::Computer Vision and Pattern Recognition Multilayer perceptron Statistics Convergence (routing) Artificial intelligence business Mean bias error computer |
| Zdroj: | 2014 IEEE Conference on Energy Conversion (CENCON). |
| DOI: | 10.1109/cencon.2014.6967499 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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