Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

<p>The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation foreca...

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Bibliographic Details
Main Author:	Mohamed Massaoudi (16888710) (author)
Other Authors:	Haitham Abu-Rub (16855500) (author), Shady S. Refaat (16864269) (author), Mohamed Trabelsi (16869891) (author), Ines Chihi (16888713) (author), Fakhreddine S. Oueslati (16888716) (author)
Published:	2021
Subjects:	Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Support vector machines Temperature distribution Predictive models Photovoltaic systems Feature extraction Wind forecasting Deep belief network PV power forecasting Stacking ensemble Smart grid Power generation planning
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Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

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