For our Challenge Climate Change contest (done in collaboration with and hackster.io) we had two primary categories: battery powered and line powered . Previously we discussed the battery powered winners, now it’s time to take a look at the clever line powered winners.
Our first place finisher in the line powered category was Ralph Yamamoto’s HVAC Performance Optimization Project. His project was focused on tackling climate change by improving home energy efficiency. As we all know, heating and cooling a home consumes more energy than just about any other residential use. Any that can save even a bit of energy in a single home could have a huge impact if deployed on a large scale.
Ralph’s approach tackled two different HVAC-related issues. The first was comparing temperature and humidity measurements for both floors of his home and then adjusting the airflow to get each floor more evenly matched. That way neither floor would be too hot nor too cold and the amount of heating or cooling coming from the HVAC system would be “just right” for the whole house. The second used -based analysis of vibration and sound data to ensure that his HVAC equipment was running at optimal efficiency and didn’t have any potential operational problems that might require maintenance work.
Since we’re particularly interested in the climate change, let’s zoom in a bit on that second issue. Ralph focused initially on vibration using the available as part of the QuickFeather open source development kit from . That same kit features the EOS S3 with an embedded Arm Cortex MF4 processor, which he used to implement -based analysis of the real-time vibration activity exhibited by his HVAC system. He then added a Wi-Fi module that enabled the board to communicate wirelessly with his computer.of for helping to solve the problem of
Next, he used the SensiML dataset showing both “no operation” and “normal operation” for his furnace. That dataset was then used to train the software to recognize the state of the furnace and ultimately to create an ultra-low-power model capable of running on processors as small as 8-bits. He tested his system and found it to have a very high degree of accuracy, though additional work was required to add sound-based problem detection and the identification of nuanced conditions such as dirty furnace filters.in our free Community Edition of the Analytics Toolkit to train his system. He mounted his box with the QuickFeather board (including the ) to the side of his furnace and began streaming data as he turned his furnace from off to on. That gave him a
Although this system demonstrated basic functionality, we were impressed by the creativity of Ralph’s ideas and the fact that his design created a nice foundation for adding new and more sophisticated features. We thought this was a good use of HVAC systems around the world.technology with the potential for significant impact due to the broad use of
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