Estimation of Daily Average Global Solar Radiance Using Ensemble Models: A Case Study of Bhopal, Madhya Pradesh Meteorological Dataset

The paper presents an ensemble model to calculate the daily averaged global solar radiation prediction for the City of lakes Bhopal, in the Central region of India. Bhopal has a very diverse climate. During the wet season (commonly called Monsoon), the weather is mostly cloudy with ample rains and thunderstorms, whereas in the dry seasons of winter and summer, the sky is clear, and the sun shines brightly for most part of the day leading to solar radiation in a finite range every season. Thus, the climate of this city depicts the climate of the country and is suitable as solar radiation-based case study for further analysis of renewable energy source. We use meteorological variables like day of the year, sunrise and sunset time, maximum and minimum temperature, solar irradiance, precipitation, humidity, wind direction and wind speed to train the machine learning ensemble model which is built using Python scilearn kit code and trained over the dataset. With good correlation factors of independent variables which have strong influence on local weather, the experimental setup demonstrated good accuracy, and smaller values of root-mean-square error (RMSE), and good relative to mean absolute percentage error (MAPE) of the predictions with normalization of values for the given case study. To the best of our knowledge, this is the first attempt at analyzing the numerical weather prediction data for India where the only factors considered before setting up solar panels include inclination angle. With these set up prerequisite, solar energy estimation is less accurate as compared to that using ML and weather-related information, which can be acquired from reliable sources. The execution of this project will result in a relatively easier and less expensive way to solar radiance estimation and setup of solar panels across the country.