By Topic

Characterization and modeling of a grid-connected photovoltaic system using a Recurrent Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Riley, D.M. ; Sandia Nat. Labs., Albuquerque, NM, USA ; Venayagamoorthy, G.K.

Photovoltaic (PV) system modeling is used throughout the photovoltaic industry for the prediction of PV system output under a given set of weather conditions. PV system modeling has a wide range of uses including: prepurchase comparisons of PV system components, system health monitoring, and payback (return on investment) times. In order to adequately model a PV system, the system must be characterized to establish the relationship between given weather inputs (e.g., irradiance, spectrum, temperature) and desired system outputs (e.g., AC power, module temperature). Traditional approaches to system characterization involve characterizing and modeling each component in a PV system and forming a system model by successively using component models. This paper lays the groundwork for using a Recurrent Neural Network (RNN) to characterize and model an entire PV system without the need to characterize or model the individual system components. Input/output relationships are “learned” by the RNN using measured system performance data and correlated weather data. Thus, this method for characterizing and modeling PV systems is useful for existing PV system installations with several weeks of correlated system performance and weather data.

Published in:

Neural Networks (IJCNN), The 2011 International Joint Conference on

Date of Conference:

July 31 2011-Aug. 5 2011