Paper Contents
Abstract
In this paper, we examine how different machine learning (ML) models can predict daily global solar radiation (DGSR) based on historical weather data. The major focus? To examine how well such ML models can predict solar radiation that is super crucial for most Solar energy applications. Our dataset will include important bits of information like UNIX time, date, levels of solar radiation, temperature, pressure, humidity along with times of sunrise and sunset. We will use those features for the purpose of training and testing some of the ML models. Until now, preliminary results indicate that our strategies may have a good chance of making accurate forecasts of DGSR. Therefore, it gives a sense of how real ML's potential could be for improving the accuracy of the solar radiation forecast. This study truly shows how vital it is to incorporate more advanced data-driven meteorology techniques. Why? It could lead towards the better forecasting of solar energy, which would help in energy management & planning.
Copyright
Copyright © 2024 Gautam Nimase. This is an open access article distributed under the Creative Commons Attribution License.
