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Advances in Smart Systems Research |
| Publisher |
Future Technology Publications |
| Vol. 6 No. 1 |
Workshop Papers from KES Smart Digital Futures 2017 |
| Journal ISSN |
2050-8662 |
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| Article Title | Bipolar Extreme Learning Machine for solar and wind synergy regression |
| Primary Author | Jose Salmeron, Universidad Pablo de Olavide |
| Other Author(s) |
Antonio Ruiz-celma
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| Pages |
23 - 26
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| Article ID |
idt17-007 |
| Publication Date |
15-Oct-17 |
| Abstract | The authors propose a Bipolar Extreme Learning Machine approach for solar and wind synergy regression. The solar and wind synergy is relevant because it is critical in many renewable energies. It is used in processes such as biofuels production, food industry, detergents and dyes in powder production, reprography applications, textile industries, pharmaceutical industry and others. The results are tested with the state-of-the-art techniques (linear regression, k-Nearest Neighbours regression, Random Forest and Support Vector Regression) and our proposal outperforms them. In addition, the experiments have been benchmarked with two error measures (MAE and MSE).
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