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Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

1
Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
2
Department of Urban Engineering, University of Architecture Ho Chi Minh City, Ho Chi Minh City 72407, Vietnam
*
Author to whom correspondence should be addressed.
Energies 2019, 12(16), 3158; https://doi.org/10.3390/en12163158
Received: 24 June 2019 / Revised: 8 August 2019 / Accepted: 13 August 2019 / Published: 16 August 2019
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PDF [2299 KB, uploaded 16 August 2019]
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Abstract

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision. View Full-Text
Keywords: lifetime energy yield; bootstrap; confidence interval; multiple linear regression model lifetime energy yield; bootstrap; confidence interval; multiple linear regression model
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MDPI and ACS Style

Le, N.T.; Benjapolakul, W. Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques. Energies 2019, 12, 3158.

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