Machine Learning and Dash for the US Electric Grid
with Data Scientist, Kyle Baranko
Recorded on Wednesday, August 4 2021 @ 2 pm EDT
The need to bring vast amounts of new renewable energy capacity onto the grid, coupled with extreme weather events (eg Texas and California), has underscored the need for a massive upgrade to the US electricity grid.
Using open-source models and Dash, we can simplify the exploration and communication of complex problems like rebuilding and optimizing the US energy infrastructure.
In this Webinar, Kyle shares his open-source Dash app and methodology for predicting electric grid loads using ML and XGBoost. Additional topics covered:
- Overview of Independent Service Providers (ISOs) and the grid
- Constructing applied Machine Learning models
- Analysing the prediction results visualized with Dash
- Demand Forecasting in the real world
About Kyle Baranko
Experienced in data acquisition and data modeling, statistical analysis, machine learning, and time series analysis, Kyle is passionate about using data science to address climate change. With a background in energy marketing and communications, his articles have appeared in Towards Data Science, Cleantechnica, and New America Weekly.