Budderfly, Inc.

Albert Subbloie

Kenneth Buda

Jaan Leemet

Christopher J DeBenedictis

Paul Schmidt

Kenneth Scott Fassman

William Butler

Jacob Marchinus Boerma

A system and method of dynamically calculating average monthly energy use through a system of weights attributed to business, environmental, and operational variables. The system applies machine learning to improve upon its estimations by learning the correlations of equipment and operational factors to the overall equipment use.

Dynamic real time average monthly energy management system

A system and method for dynamically and automatically adjusting the load on a power grid through micro adjustments of equipment coupled to the consumer side of the power grid. The system allowing for the automatic adjustment of equipment to either decrease or increase instantaneous power demand on the grid in response to peak demands and demand valleys to smooth the demand curve on the power grid. The system able to balance demand within the grid to adjust demand within various different portions of the power grid and allowing for reducing the power buffer supplied by electric utilities to reduce waste and carbon emissions.

Facilities grid micro demand response system

A system of modules which evaluate, control and measure energy usage and signatures at a building that are in communication with software executing on a computer controlled by a third-party. Software executes on the computer to compare energy usage and signatures of equipment at the facility with information in a database accessible by the computer and is adapted to adjust a Building Management System to modify the operation of at least one piece of equipment at the facility based on the comparison third-party. The database including information and filters for dynamic system adaptation to changing environmental factors.

Facility smart energy management

A system of modules which control and measure energy usage at a building which are in communication with a software program executing on a remote server controlled by a third party. The third party said usage via the software program which communicates with the modules to modify energy usage and demand for energy and is responsible or liable for energy usage charges the building where the third party does not actually use the energy.

Third party energy management

A system for automatically learning and adapting to the energy usage of an equipment installed at a facility or many pieces of equipment at a plurality of facilities, where the system is provided with an initial baseline energy usage signature for the equipment, which is modified by measured energy usage and by at least one peripheral sensor measurement data to create a modified energy usage signature. The system uses artificial intelligence to learn and adapt the baseline energy usage signature to learn the business operation and account for external variables such as temperature variance and increased business flow or an interaction between devices. The smart system can identify when a piece of equipment falls outside of 'normal' operation and determines what automatic action is to be taken for that piece of equipment.

Electrical equipment fault diagnosis and control

A system for automatically learning and adapting to the energy usage of an equipment operating according to a control input including at least one sensor for measuring an energy usage of the equipment an generating a baseline energy usage over time signature that is used to compare active energy usage measurements to so as to determine operational deviations. The system includes software that matches and compares equipment operation to established norms and can modify the functioning of the equipment when threshold deviations are detected. The system includes the ability to learn the functioning of the equipment and can adjust for dynamically changing conditions to avoid generation of false alerts or alarms while at the same time detecting longer term deviations that if left unchecked, could shorten the lifespan of the equipment and increase the costs associated with running the equipment.

Machine learning application to predictive energy management

An electrical connection and control system is provided including a power source configured to provide a direct current output and an electrically powered element configured to receive power from the power source. The power is supplied via strips of nanowire material applied upon a surface. Controllers are provided to control the electrically powered elements via switches and/or via network connections.

Nanowire based electrical contact system

A system of modules which control and measure energy usage at a building that are in communication with a software program executing on a remote server controlled by a third party. The third party said usage via the software program that communicates with the modules to modify energy usage and demand for energy and is responsible or liable for energy usage charges the building where the third party does not actually use the energy.

Facility energy management

Aspects include an adaptor and a method for providing computer based energy management. Commands specifying a control device from energy management host software located on a host system are received at an adaptor via a server network. The commands include control instructions and requests for energy usage data. Energy usage data are received from the control device in response to a command including a request for energy usage data. The energy usage data includes energy usage for one or more energy devices. The energy usage data are transmitted to the energy management host software. Sensor data are received from one or more sensors and transmitted to the energy management host software. Control commands are received from the energy management host software. The control commands are transmitted to the control device to alter a function of at least one of the one or more energy devices.

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Orange, CT, United States of America

Albert Subbloie