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

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