Data analysis and resource forecasting in the renewable energy sector

Project Summary

This project is mandated by SmartHelio, wherein the intern would be building and testing forecasting models on solar energy resources and plant production capacities.This would include comprehensive research of publicly available weather and meteorological data platforms, in order to shortlist the models that suit the project’s objectives. The intern would also be identifying correlations between plant performance and other primary influencers (weather, climate etc.) through historical data collected from the real project sites.

The key objective is to build daily, bi-monthly, and monthly forecasting models on solar energy resources and plant production capacities, spread out in 5 minute time intervals, with an accuracy of over 99%.


Preferred Bachelors/ Masters students with independent research and modeling/programming abilities in one of the following fields or related:

Mathematical Modelling of Wind & Renewable Energy Systems, Climate Modelling, Computer Science, Machine Learning and Artificial Intelligence, Big Data Analytics, Computational Methods & Optimization, Computer vision and Image processing, Climate science & Environment, Meteorological Sciences, Applied Mathematics or Statistics.

Key skills

– The ability to analyze complex technical problems, apply data analysis and machine learning techniques to develop solutions that meet the clients’ demands

– Knowledge and experience in mathematical modeling, forecasting algorithms, predictive analytics, and advanced machine learning

– Knowledge of climate science and meteorological sciences

– In-depth experience in python programming and related libraries/methods (Pandas, Numpy, Tensorflow, Keras, CNN, RNN, LSTM, Vgg16, Resnet-50, Mobilenet, OpenCV, etc.).