Abstract
In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.
Highlights
With more than 70 GW of solar photovoltaic power installed all over the world, most of them in large PV plants, and in some cases running for several years, the management of the operation and maintenance (O&M) of these systems is a relevant research field in the solar PV industry [1].Data represents a key asset in this PV management area, since they enable modeling the standard behaviour of the system and to monitor its performance compared against the expected output determined by the model
We model Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) and we compare the accuracy against the standard analytical model which is based on parameters and specifications
It is a good standard for forecasting output power in photovoltaic system, which is based on analytical modeling
Summary
Data represents a key asset in this PV management area, since they enable modeling the standard behaviour of the system and to monitor its performance compared against the expected output determined by the model. This monitoring, when is applied timely and comprehensively including all the factors that may impact the performance, enables early damage and fault detection, which allows operation and maintenance actions to maximize the up-time and efficiency of the PV plants. Approximate analytical expressions based on the electrical parameters of the solar cells that conform the PV system and the specifications provided by the manufacturer were used to build the standard performance model. We model Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) and we compare the accuracy against the standard analytical model which is based on parameters and specifications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
