Short-term PV power forecasting using hybrid GASVM technique
The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochasti...
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| Format: | Article |
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Elsevier
2019
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| _version_ | 1825722017208336384 |
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| author | VanDeventer, William Jamei, Elmira Thirunavukkarasu, Gokul Sidarth Seyedmahmoudian, Mehdi Tey, Kok Soon Horan, Ben Mekhilef, Saad Stojcevski, Alex |
| author_facet | VanDeventer, William Jamei, Elmira Thirunavukkarasu, Gokul Sidarth Seyedmahmoudian, Mehdi Tey, Kok Soon Horan, Ben Mekhilef, Saad Stojcevski, Alex |
| author_sort | VanDeventer, William |
| collection | UM |
| description | The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error. © 2019 |
| first_indexed | 2024-03-06T05:59:13Z |
| format | Article |
| id | um.eprints-23207 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:59:13Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-232072019-12-05T08:06:45Z http://eprints.um.edu.my/23207/ Short-term PV power forecasting using hybrid GASVM technique VanDeventer, William Jamei, Elmira Thirunavukkarasu, Gokul Sidarth Seyedmahmoudian, Mehdi Tey, Kok Soon Horan, Ben Mekhilef, Saad Stojcevski, Alex QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error. © 2019 Elsevier 2019 Article PeerReviewed VanDeventer, William and Jamei, Elmira and Thirunavukkarasu, Gokul Sidarth and Seyedmahmoudian, Mehdi and Tey, Kok Soon and Horan, Ben and Mekhilef, Saad and Stojcevski, Alex (2019) Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 140. pp. 367-379. ISSN 0960-1481, DOI https://doi.org/10.1016/j.renene.2019.02.087 <https://doi.org/10.1016/j.renene.2019.02.087>. https://doi.org/10.1016/j.renene.2019.02.087 doi:10.1016/j.renene.2019.02.087 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering VanDeventer, William Jamei, Elmira Thirunavukkarasu, Gokul Sidarth Seyedmahmoudian, Mehdi Tey, Kok Soon Horan, Ben Mekhilef, Saad Stojcevski, Alex Short-term PV power forecasting using hybrid GASVM technique |
| title | Short-term PV power forecasting using hybrid GASVM technique |
| title_full | Short-term PV power forecasting using hybrid GASVM technique |
| title_fullStr | Short-term PV power forecasting using hybrid GASVM technique |
| title_full_unstemmed | Short-term PV power forecasting using hybrid GASVM technique |
| title_short | Short-term PV power forecasting using hybrid GASVM technique |
| title_sort | short term pv power forecasting using hybrid gasvm technique |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
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