Predict Solar Panel Power Production Using Weather Forecasts

Solar energy is one of the widely used renewable energy in homes and industries and could replace fossil fuels. Frequently changing weather conditions are the main factor that affects solar power generation. Sri Lanka is a tropical country that is affected by unpredictable weather conditions. It is situated very near to the equator and receives more sunlight. Many solar panel users in Sri Lanka generate electricity for ordinary households and industrial purposes. Solar panel users must rely on fossil fuel energy when solar power generation decreases. It increases the load on the main grid.This research focuses on how weather conditions affect solar panel productivity and how solar panel users can use renewable energy in an efficient way using accurate weather prediction systems. Weather data and solar panel energy production data were collected and analyzed for this research. A Google form was distributed to the solar panel users to collect data about the weather prediction systems that are used to estimate solar panel power production. First, the correlation between weather conditions and solar energy production was analyzed. A Neural Network(NN) and the SARIMA algorithm are used to create the weather prediction system and compare the accuracy. Then, the weather prediction system is used to identify the increment and decrement in solar panel power production. This research aims to transform the weather prediction system into a solar power prediction system and use solar power efficiently to manage the load on the main grid.