Photovoltaic power analysis and prediction using machine learning methods
| dc.contributor.author | Shehada, Halah | |
| dc.date.accessioned | 2021-07-29T16:04:34Z | |
| dc.date.available | 2021-07-29T16:04:34Z | |
| dc.date.graduationmonth | August | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The stochastic nature of Photovoltaic power directly affects the stability of the grid. PV power forecasting allows power stations to know beforehand how much PV power will be available, which ensures that the grid remains in stabilized condition. PV power from India is analyzed and predicted using machine learning methods | |
| dc.description.advisor | Mohammad B. Shadmand | |
| dc.description.degree | Master of Science | |
| dc.description.department | Department of Electrical and Computer Engineering | |
| dc.description.level | Masters | |
| dc.identifier.uri | https://hdl.handle.net/2097/41580 | |
| dc.language.iso | en_US | |
| dc.publisher | Kansas State University | |
| dc.rights | © the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject | Photovoltaic | |
| dc.subject | Random Forest | |
| dc.subject | Confusion Matrix | |
| dc.title | Photovoltaic power analysis and prediction using machine learning methods | |
| dc.type | Report |
