As a substitute for conventional energy sources, Solar energy is quickly becoming a popular source of renewable energy. Various entities ranging from small households and businesses to large firms and MNCs are currently making plans on investing resources in the generation of solar energy. Thus, accurate prediction of solar radiation has become a necessity in the present scenario. Due to limitations like the unavailability of proper measuring equipment and a small number of meteorological departments, accurate prediction of solar radiation is not possible in many places around the world. This paper focuses on forecasting solar radiation using machine learning techniques. Solar radiation depends upon various natural factors, which are easier to measure, and these factors can help forecast solar radiation. This paper explores the available data to identify the various factors which affect solar radiation. Based on these factors, the paper investigates the performance of different standard regression models based on solar radiation prediction. Next, multi-level statistical models are proposed, which stack multiple standard models into layers, and the R^2 scores of these custom models is compared with the R^2 scores of the standard models.
--> Upload 'SolarPrediction.csv' and 'SolarRadiationPrediction.ipynb' in Google colab.
--> Click on 'Runtime' -> 'Run All' in the toolbar.
The project 'Multi-Level Statistical Model for Forecasting Solar Radiation' has been created as a mini project for the course IT302- Probability and Statistics.
- Pratham Nayak (191IT241)
- Aprameya Dash (191IT209)
- Suyash Chintawar (191IT109)