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

作     者：Ngoc Thien Le Benjapolakul, Watit 

作者机构：Chulalongkorn Univ Fac Engn Dept Elect Engn Bangkok 10330 Thailand Univ Architecture Ho Chi Minh City Dept Urban Engn Ho Chi Minh City 72407 Vietnam 

出 版 物：《ENERGIES》 (能源)

年 卷 期：2019年第12卷第16期

页      面：3158页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Rachadapisek Sompote Fund for Artificial Intelligence  Machine Learning  and Smart Grid Technology (Year 1) Research Unit (RU)  Chulalongkorn University 

主　　题：lifetime energy yield bootstrap confidence interval multiple linear regression model 

摘      要：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 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.