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AI + IIoT reduces 1.3 tons of CO2 emitted by HVACs


Global warming and sustainability are trend topics that threaten human life and the planet in the future. Sustainability is an umbrella of all actions that conserve resources for the next generations. Green House Gas (GHG) emission, caused mainly by fossil fuel consumption, is a global concern that disturbs sustainability and directly affects agriculture, the atmosphere, etc. Fossil fuels are still consumed in some power plants to generate electricity. According to U.S. Environmental Protection Agency (EPA), 0.85 pounds of CO2 is emitted into the atmosphere for generating 1kW of electricity; although, this ratio is different in Canada.

How much CO2 do HVACs release?

The energy consumption of HVACs depends on various parameters, such as the size and efficiency of HVACs. The popular size of HVACs in a residential or commercial building is 100 tons (1 ton ~ 12,000 BTU). A one-ton HVAC consumes 1kWh power on average; however, the coefficient factor is not linear. That means a 100-ton HVAC uses 350 kWh rather than 100 kWh. Assuming the HVAC works for 8 hours a day, the HVAC consumes 2,800 kWh. According to EPA, power plants release 1.3 tons of CO2 in the U.S. to generate 2,800 kWh of electricity. This amount of CO2 is equivalent to GHG emission from 3,000 miles driven by a gasoline-powered vehicle and CO2 emission from 2.8 barrels of oil consumed. Also, 20 tree seedlings grown for ten years are required to resolve the CO2.

How do Artificial intelligence (AI) and Industrial IoT (IIoT) reduce GHG?

AI and IIoT technologies can optimize HVACs and provide the same comfort with less energy consumption. How? 

  • We know that Independent System Operators (ISOs) supply electricity from renewal resources, BUT they have to supply additional demands on peak times from other resources such as gas generators. IIoT integrates all HVACs and cloud services into a central platform, allowing energy managers to control HVACs from anywhere and shift HVACs operations to off-peaks. For example, HVACs may cool a building a few minutes before peak times and then switch to fan mood within the peak interval. 
  • The faulty HVACs consume 30% more energy than healthy HVACs. Predictive maintenance is an AI solution that forecasts and detects faults, saving 30% electricity.

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