In mid-March I attended a talk organized by the ClimateLink SF Meetup, titled “Rise of the Machines: AI for Energy Management.” The three presenters were Daniel Lim, Co-Founder at Crocus Energy, Pratik Parekh, Sr. Data Scientist and PM at Bidgely, and Andrew Byrnes, Investment Director with Comet Labs. In this post, I’ll summarize the main points of the talks and their connections to data science.
Tag: energy
In my last two posts I discussed classification of land use and weather in Amazon images. This week, I’m taking a break in that series to cover a recent event I attended at Stanford University. I’m lucky to live near Stanford, which hosts a diverse array of free or inexpensive talks and events. I frequently attend the Energy Seminar, sponsored by the Precourt Institute for Energy. Last week I attended a panel called “Devices and Decisions for Smart Buildings,” moderated by Strategic Energy Alliance Executive Director Richard Sassoon. The three panelists were Anthony Diamond, CTO and Co-founder of Axiom Exergy; Matt Duesterberg, CEO of OhmConnect; and Mark Chung, CEO and Co-founder of Verdigris. I was excited to learn more about this industry and am eager to share some insights with my readers. I’ll cover each company’s offering, use of data science, and future directions.
After reading my first post, you might be wondering, what do artificial intelligence (AI) and data science (DS) have to do with reversing global warming? I’ll start to answer that question in this post with some examples. And of course future posts will include many more examples, and go into more depth.
Sectors
Many emissions analyses divide the world into economic sectors. The EPA publishes data on global emissions by sector based on analysis by the IPCC, and summarized in the figure below. Let’s examine some Drawdown Data Science examples for two large sectors, noting that the pie chart below is based on 2010 emissions.