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.
Highlights
The rooftop PV system is usually the first choice investment for the domestic application of customers when they consider following any renewable energy plans [1,2,3]
The climate within a country or a region should vary as little as possible. Those countries and regions are Netherlands, UK, New South Wales, Germany, Belgium, and California; Since we focused on the impacts of PV configuration and components on the lifetime energy, we only surveyed the PV systems which are over two years old to ensure that they suffered the same seasonal change; We classified the PV systems into two groups—non-shading and shading
Machine learning techniques are based on the power of a computer to build and train models according to the input datasets
Summary
The rooftop PV system is usually the first choice investment for the domestic application of customers when they consider following any renewable energy plans [1,2,3]. The meta-analysis in References [4,5,6] surveyed many popular PV financial models, considering the technical characteristic of PV component, PV configuration and type of solar panels These studies helped a customer to choose a reliable tool for PV planning and design from the system point of view, without depending on the equipment supplier. The authors of References [7,8,9] studied the PV cost of the residential application of a PV system in terms of energy payback time (EPBT) and energy return on energy investment (EROI) They found that the small PV modules area helps to increase the energy yield but it increases the model-level and system-level.
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