Paper Info
Title
A Scalable, Data-driven Approach for Power Estimation of Photovoltaic Devices under Indoor Conditions
Abstract
For the output power estimation of photovoltaic devices in indoor applications, models are needed that perform accurately at the low illumination levels encountered. As a robust and scalable solution, we propose a data-driven modeling method, spanning an interpolated surface between two reference I-V curves. The proposed approach is evaluated based on experimental data of two exemplar PV panels at indoor illumination levels. The results are compared to two common parameter extraction methods for the one-diode circuit model. This investigation demonstrates that the proposed surface model has a high performance under all test conditions, whereas the reference models show a performance dependency on the PV panel type. It can be concluded that the surface model is a competitive alternative for output power estimations at indoor illumination levels, removing many of the uncertainties of traditionally used physical parameter extraction and scaling methods.
Year
DOI
Venue
2019
10.1145/3362053.3363494
Proceedings of the 7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems
Keywords
DocType
ISBN
i-v characteristics, indoor energy harvesting, parameter scaling, photovoltaic panel, pv model
Conference
978-1-4503-7010-3
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Xinyu Ma121.03
Sebastian Bader245.73
B Oelmann37721.78