Connecticut Patent of the Month – May 2023

Budderfly Inc. is putting machine learning capabilities to the test in their predictive energy management solution. Budderfly is a revolutionary energy management solution that is working to change the way businesses, schools, and other institutions use and save energy.

Electric-powered equipment is designed with expected electrical usage parameters. These parameters are based on the motor size, power draw and more. The parameters are then used by contractors to size the electrical wiring used to provide power to the equipment – a crucial component to ensure safe and proper operation. Tracking energy consumption patterns over time can provide insight into how efficiently a facility is operating and can inform when maintenance is required.

Unfortunately, establishing a reliable power consumption pattern is not always straightforward. External variables like temperature, humidity, and airflow can all impact power consumption. In some cases, even details like the number of people in a room, or the presence of direct sunlight through windows, can impact energy usage patterns.

Budderfly’s solution uses machine learning to manage these extensive variables, adjusting for changing variables to produce a single, reliable pattern. This can then be used as a baseline to compare the energy usage against. Budderfly’s system can create an expected energy utilization for a particular piece of equipment based on a measured energy usage signature of the equipment. The historical accuracy of these predictions can improve the classification system over time. The system can diagnose aberrations to the expected patterns and classify these into potential equipment problems, triggering automated actions if there is a noticeable change or deviation from the expected pattern. The alerts may result in a closer look at the system, such as taking more frequent measurements or the correlation of other data or potentially related measurements, such as diagnostic information or data from other sensors.

One example of how the cyclic nature of energy consumption patterns for installed systems can be monitored is a deep freezer used in a restaurant environment. The system can monitor each cycle of cooling and warming, measuring the electrical power or current drawn by the compressor of the unit. Each of the cycles exhibits a consistent pattern, which can be monitored and acted upon to trigger automated actions if there is a noticeable change or deviation from the expected pattern.

The system and method provide for automatic monitoring and self-adjustment of the monitoring system. It can limit or eliminate the generation of any false alarms for non-typical but normal operation of the equipment. The system can provide insights into energy consumption and savings opportunities, increasing visibility and control of energy usage. It can lead to cost savings and a reduction in greenhouse gas emissions. Overall, the system and method developed by Budderfly could benefit facility managers, contractors, and equipment manufacturers by improving energy efficiency, reducing costs, and minimizing environmental impact.

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