Energy Forecasting Pipelines
Erin Boyle, the Head of Data Science at Myst AI, joins us today to talk about her work with Myst AI’s time series forecasting platform and service with the objective for positively impacting sustainability.
Despite advancements in battery technology and other approaches to power storage, most power plants must produce their output on demand with any overage going to waste. Thus, the ability to forecast future demands is key to this industry.
Myst’s platform is designed to help organizations rapidly leverage forecasting approaches, accelerating development and deployment of expert forecasting systems.
Erin Boyle
Erin Boyle is the Head of Data Science at Myst AI, a Series A company providing a forecasting platform and service to organizations in clean power. She manages a team of data scientists to increase the breadth and accuracy of forecasting at Myst. Prior to Myst, she worked at Stitch Fix for five years, where she owned the company’s core style recommendation system and managed the Shop Recommendations team. Her academic career included a PhD in Physical Chemistry, where she developed new spectroscopy techniques relevant to clean energy and atmospheric science.
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