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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kmetova, Maria Kmet, Tibor |
| Abstract | Artificial neural networks have demonstrated to be good at timeseries forecasting problems, being widely studied in literature. In this study an artificial neural network model is introduced for modelling the solar irradiance. Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24h or even more) can become fundamental in making power dispatching plans. In this paper, a practical method for solar irradiance forecast using artificial neural network is presented. The proposed echo state neural networks model makes it possible to forecast the solar irradiance on the base of 24h using the present values of the mean hours solar irradiance, air temperature, humidity, and the wind speed. An experimental database of solar irradiance, air temperature, humidity, wind speed data (from October 17th 2013 to May 11th 2015) has been used. The database has been collected in Lucenec (48.33N 19.67E), Slovakia. The results indicate that the proposed model performs well, while the correlation coefficient between measured and forecasted solar irradiance is in the range 0.94 -- 0.97. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450335805 |
| DOI | 10.1145/2797143.2797166 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-09-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Solar irradiance 24h forecasting Echo state neural networks |
| Content Type | Text |
| Resource Type | Article |
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