Empowering Plants with Python
Chaitra Vishweshwaraiah (~chaitra23) |
In India, it’s a Government mandate, for the wind and solar farm owners to provide prediction of energy produced by the farm ahead in time. Hence, Forecasting plays an important role in reducing the uncertainty in demand and generation of electricity. Accurate forecast helps manage grid stability and also to reduce the penalties which the farm owners are liable to pay, in-case of Inaccurate prediction. The aim is to provide forecast as per government regulations, every 15 minutes using machine learning, statistics and physics-based models.
In our talk we would like to present, how python is used to
Handle data from solar and wind farms to forecast energy generation, day ahead and intraday
Forecast energy generation at different levels based on the granularity of data available
Power Plant Level
Turbine level or PV module level
Systematize handling multiple farms which
provide different frequency of data
have data stored in different data sources
have different sensors' data associated with different assets
Devise mechanisms for data retrieval from different weather forecast providers in handling data which
Have different units, tags
Different forecast providers give better forecast in different locations
Implement various models and their performance comparisons
Physics based model
Machine Learning models
For a farm in India, the revenue rose by about Rs 12 lakhs, in a month, after adoption of forecasting and scheduling solution. Annual cost savings and increased revenue generation brought about by the solution are significant.
The penalties payed to the government is drastically reduced.
Forecasts facilitate efficient planning and execution of maintenance activities at the farm.
- Python basics
- ML basics
- Cron jobs basics
- Curiosity to Learn
Ripunjoy is Analyst at BLP Clean Energy. He is a curious and driven data science professional with more than three year of experience in the IT industry. He has the ability to constantly mine for hidden gems located within large sets of structured and unstructured data. Master degree holder from Indian Statistical Institute with skills in Mathematics, Statistics and Quality Management. Background in computer programming language and databases learnt on the current job.
Malavika is the Assistant Manager, Data Analytics at BLP Clean Energy. She leads the data architecture validation team and the development activities in Analytics and Visualization. She has prior experience in handling enterprise data warehouses and crisp visualizations built using them. She has co-authored a White paper titled "Overcoming the challenges faced in a Data warehousing : A project perspective" which won a Platinum category award in an internal contest at Infosys. She has studied Data Analytics at IIIT Bangalore. She is a data enthusiast, with working knowledge of data storage, data analytics and data visualization.
Chaitra B V
Chaitra is Analyst at BLP Clean Energy. She works on Data Visualization and Analytics to create Intuitive dashboards upon analysis of solar, wind energy asset monitoring and management in the analytics practice. Previously, she worked in Risk Consulting for Financial Crime domain with PricewaterhouseCooper LLP. She Holds a Bachelor of Engineering in Information Technology from CMR Institute of Technology. She is been felicitated by the Ex-Governor of Karnataka H.R.Bharadwaj for Academic Performance.