Monitoring Solar Energy Production based on Internet of Things with Artificial Neural Networks Forecasting
Younes Ledmaoui, Asmaa El Fahli, Abdellah Chehri, Adila Elmaghraoui, Mohamed El Aroussi, Rachid Saadane
Abstract
This paper discusses an Internet of Things (IoT)-based energy meter for photovoltaic systems (PV) to forecast energy production at industrial locations. Based on an ESP-32 card, the proposed IoT device collects energy consumption data from the sub-meter and delivers it to the cloud. The data is used to monitor the values of a typical PV system installed in Benguerir (Morocco). The method involves conducting an analysis of the solar resource available at the site in Benguerir, as well as conducting an investigation, evaluation, and selection of the components of the solar station using simulation software such as the PVSYST tool. The proposed solution will improve the management of a PV system. Additionally, the method involves the development of a datalogger that is used for monitoring solar panels’ energy production, storing data in the cloud, and displaying results on a web interface. Lastly, we apply an artificial neural network for solar energy forecasting to future production.