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⚡ "Solving Heat Loss on a City Scale"

CleanTechies

Photo by Dennis Kummer / Unsplash

Table of Contents

Host: Silas Mähner
Guest: James Henry | Director of Growth & Sustainability | MyHEAT
Category: ⚡ Energy | Heat Maps

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Podcast’s Essential Bites:

[5:21] “MyHEAT is an energy software and SaaS startup. […] Our purpose is to reduce the amount of energy required to heat and cool buildings around the world. […] The way that we do this is with remote sensing, specifically with airborne thermal imagery that identifies heat loss for individual buildings at a city scale or larger. And we use this imagery that we collect to provide a unique visual heat loss map for each individual building, and a heat rating for each home to allow homeowners to compare their relative heat loss to their neighbors or to homes of similar sizes.”

[6:17] “We're all about energy made visible. The world wastes more energy per year than it actually uses. There's a lot of inefficiencies in that chain from energy production to consumption. And a lot of energy ends up getting wasted, but it's kind of an invisible problem. You don't see it and if you don't see it, you don't think about it. And it makes it hard to tackle.”

[10:25] “We're operating a turboprop plane, flying it somewhere between 2,000 to 4,000 feet in the air. And attached to that plane is a very high resolution thermal infrared sensor. […] [It] flys an entire city in a couple of nights and collects information about the relative difference in temperatures per pixel that it sees down below, and maps that to rooftops to detect heat loss.”

[13:23] “The very first step is to deploy some of our proprietary machine learning on this imagery for image detection and recognition to be able to say, this is building or this is not, this is water, this is pavement, etc. We're also at that point, pulling in some third party data sources to help with the identification of buildings as well and start to effectively draw the building outlines so that we can isolate buildings from everything else. […] Step two would be also then using more of our in-house machine learning skills […] to be able to automatically classify the buildings, so residential, commercial, industrial, and then within residential, what's single, family, multifamily [etc.]. […] We currently predominantly work with our product in the residential sector.

[16:50] “We partner predominantly with gas and electric utilities, across North America at the moment. And we use these maps to essentially promote energy literacy, to educate their customers on energy efficiency, to really engage them in the conversation on energy efficiency, and help them reduce energy loss by pinpointing inefficient areas of their home.”

Rating: ⚡⚡⚡

🎙️ Full Episode: Apple | Spotify | Google
🕰️ 56 min | 🗓️ 07/02/2022
✅ Time saved: 54 min

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