| Project ID |
BITS-SRIP/FE33C2/2026 |
| Project Title |
Application of Statistics and Machine Learning in Renewable Energy Prediction |
| Project Description |
To combat greenhouse gas emissions and climate change, a transition from fossil fuesls to renewable energy is inevitable. In this regard, this project aims to develop basic and advanced models (e.g., ARIMA, SARIMA, LSTM, and others) to predict renewable energy (solar irradiance and wind speed) in a short to intermediate term (15-minutes, one hour, to one week). We shall analyze vast historical data (GHI, wind speed, meteorological data) to find inherent patterns, enabling reliable generation forecasts for grid stability and better energy management. We expect enthusiastic students with sound computational knowledge to contribute in this research work. |
| Project Discipline |
B4AD, A7, B4A7 (Mathematics and Computing, Computer Sciences, Mathematics and Computer Sciences) |
| Faculty Name |
Sumanta Pasari |
| Department |
Department of Mathematics |
