The prediction of monthly average solar radiation with TDNN and ARIMA
In this paper, two well-known algorithms: ARIMA and TDNN (Time Delay Neural Network) are applied to conduct the short term prediction of solar radiation. For the daily solar radiation series is non-stable due to the fast weather changing, monthly average solar radiation is adopted as the data source...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/102836 http://hdl.handle.net/10220/16877 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In this paper, two well-known algorithms: ARIMA and TDNN (Time Delay Neural Network) are applied to conduct the short term prediction of solar radiation. For the daily solar radiation series is non-stable due to the fast weather changing, monthly average solar radiation is adopted as the data source. As ARIMA model requires the time series to be stationary, first order difference is performed on the monthly solar radiation to obtain a stationary series. AIC (Akaike's Information Criterion) is used to identify the optimal prediction model. TDNN is also used to do prediction of the monthly average solar radiation and LM (Levenberg -- Marquard) is chosen as the training algorithm. The performance of these two prediction models are compared with each other. |
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