California Patent of the Month – March 2023
Heating, ventilation and air conditioning (HVAC) systems are crucial in ensuring that indoor environments are comfortable, safe, and healthy. However, HVAC systems are often costly to operate and can have a significant environmental impact. In an effort to improve the efficiency and effectiveness of HVAC systems, EnerAllies, Inc has developed a revolutionary HVAC control system that utilizes a computer-implemented method to optimize heating and cooling in buildings.
The EnerAllies HVAC control system works by storing multiple different policy datasets, each representing HVAC control policies for different structure locations and extreme weather conditions. When weather condition data is received, the system identifies the structure location and selects the corresponding policy dataset. The policy dataset is then transformed into specific HVAC equipment instructions, based on stored HVAC configuration data, and transmitted to the HVAC equipment at the specific structure location.
What sets the EnerAllies HVAC control system apart is its use of machine learning to determine the likelihood of extreme weather conditions occurring. By training the system using previous forecast data, the likelihood of extreme weather conditions can be quite accurately predicted. If the likelihood of an extreme weather condition is greater than a threshold value, the system will execute the specific HVAC equipment instructions to prepare the HVAC system for the extreme weather condition.
In addition to optimizing HVAC control during extreme weather conditions, the system also offers customization options for building owners. Building owners can select a specific policy dataset for their building, allowing for a personalized approach to HVAC control.
EnerAllies is re-imagining energy management solutions by providing HVAC energy and maintenance savings through WiFi enabled thermostats and smart building technology. By utilizing machine learning and personalized policy datasets, the system offers improved efficiency and effectiveness while also reducing environmental impact.
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