Abstract:
In Rwanda and other developing nations, solar energy is acknowledged as a sustainable energy source since it provides an endless supply of electricity. The energy from the sun can be transformed into electricity via solar photovoltaic (PV) modules (photo = light, voltaic = electricity). The amount of energy lost during generation depends on a solar power plant's efficiency. Solar power plant failures have a significant influence on the energy balance and system dependability. As a result, there are two main elements that are directly proportional to the energy losses produced by this plant. Photovoltaic (PV) panel production inefficiencies and plant failures are some of these concerns. Solar power plants also tend to be more expensive, and the placement of these infrastructures makes security a major concern. The goal of this research is to increase solar energy's effectiveness and the infrastructure security of solar systems. Energy loss is reduced and consumers are given the ability to use more energy thanks to monitoring systems and predictions of photovoltaic energy generation. Forecasting solar energy is insightful since it depends on varying solar radiation and weather patterns. Monitoring solar panel parameters and estimating energy production for energy management methods are the stated problems. The development of real-time data collecting for solar systems makes use of the Internet of Things and machine learning algorithms as a potent tool. The microcontroller (ATmega328) collects metrological data such as humidity, temperature, solar light density, current, voltage produced, and photovoltaic panel data from various sensors mounted on the solar system, including temperature sensor, LDR, current sensor, voltage sensor, and GPS module. The data has been transmitted by the GSM/GPRS module to cloud storage, which then sends the data to a web application for analysis. The system analyzes the sun's rays and then transmits feedback to the microcontroller, which chooses the solar panel that will provide the most power. The system has the ability to automatically switch loads to priority mode in the event of an emergency fault. The efficiency, dependability, accessibility, safety, and security of solar systems will all be enhanced by this IoT system. To determine the characteristics of the effective solar system, data from various sensors is evaluated by the web application and saved to the cloud. This information will help determine whether all loads must be linked or whether some loads can be prioritized based on the measurements gathered. The operator can get the alert on their desktop or mobile device.
