Green Power Labs Inc.

Alexandre Pavlovski

Dmitriy Anichkov

Zheng Qin

Brent Strader

Kenneth J Caird

Ilia Pavlovski

Chris Carter

Liqi Ge

Trent Hilliard

James Fletcher

Dimitriy Anichkov

Vladimir Kostylev

Pawan Lingras

Aaron Caldwell

Igor Yashayaev

A method for generating a solar power output forecast for a solar power plant, comprising: using a processor, in a training mode, generating a trained artificial intelligence model using historical output data and historical input data including historical physical subsystem input data and historical physical subsystem forecasts for the solar power plant; in a runtime mode, for a predetermined forecast horizon, applying the trained artificial intelligence model to current input data including current physical subsystem input data and current physical subsystem forecasts for the solar power plant to produce the solar power output forecast; and, presenting the solar power output forecast on a display.

Method and system for solar power forecasting

A method for generating an AI-based building energy model for a client building, comprising: generating an energy profile database by: defining a set of building parameters; generating energy profiles by simulating a set of physical building models; and, populating the energy profile database with the energy profiles; determining an energy profile for the client building by: splitting the energy profile database into groups and clustering each group into a set of clusters; selecting a cluster; and, selecting the energy profile in the cluster that is a closest match to that of the client building; selecting a physical building model from a building model database that corresponds to the energy profile; calibrating the physical building model to generate an adjusted building model; and, generating a set of training datasets from the adjusted building model and inputting the set of training datasets into an AI module to generate the AI-based model.

Method and system for generating a building energy model for a client building

A method for generating a net load forecast for a utility grid, the grid including intermittent distributed energy resources and loads, comprising: defining two or more load forecast zones, each zone being associated with a load profile type and a climate zone type; assigning each of the loads to one of the zones based on the load profile and climate zone types associated with the load; assigning each of the energy resources to at least one of the zones based on the climate zone type associated with the energy resource; for each zone, generating an electrical energy consumption forecast for loads, an electric power generation forecast for energy resources, and a net load forecast from the electrical energy consumption and electric power generation forecasts; combining the net load forecast for each zone to generate the net load forecast for the grid; and, presenting the net load forecast on a display. The utility grid may be or may include a microgrid.

Forecasting net load in a distributed utility grid

A method for controlling an operating condition of an electric power grid having an intermittent power supply coupled thereto, comprising: using an energy variability controller, controlling variability of a delivered power output of the intermittent power supply to the grid by: monitoring an actual environmental value for a location proximate the intermittent power supply, an available power output of the intermittent power supply being dependent on the actual environmental value; when the actual environmental value is increasing and hence the available power output is increasing, increasing the delivered power output according to a predetermined rate of increase; monitoring a forecast environmental value for the location; when the forecast environmental value is decreasing, decreasing the delivered power output according to a predetermined rate of decrease; and, limiting the delivered power output to below a predetermined threshold. The electric power grid may be or may include an electric power microgrid.

Utility grid, intermittent energy management system

A method for generating a net load forecast for a utility grid, the grid including intermittent distributed energy resources and loads, comprising: defining two or more load forecast zones, each zone being associated with a load profile type and a climate zone type; assigning each of the loads to one of the zones based on the load profile and climate zone types associated with the load; assigning each of the energy resources to at least one of the zones based on the climate zone type associated with the energy resource; for each zone, generating an electrical energy consumption forecast for loads, an electric power generation forecast for energy resources, and a net load forecast from the electrical energy consumption and electric power generation forecasts; combining the net load forecast for each zone to generate the net load forecast for the grid; and, presenting the net load forecast on a display.

A method for controlling an operating condition of an electric power grid, the electric power grid having an intermittent power supply coupled thereto, the method comprising: using an energy variability controller, controlling variability of a delivered power output of the intermittent power supply to the electric power grid by: monitoring an actual environmental value for a location proximate the intermittent power supply, an available power output of the intermittent power supply being dependent on the actual environmental value; when the actual environmental value is increasing and hence the available power output is increasing, increasing the delivered power output according to a predetermined rate of increase; monitoring a forecast environmental value for the location; when the forecast environmental value is decreasing, decreasing the delivered power output according to a predetermined rate of decrease; and, limiting the delivered power output to below a predetermined threshold.

A method for controlling temperature in a thermal zone within a building, comprising: using a processor, receiving a desired temperature range for the thermal zone; determining a forecast ambient temperature value for an external surface of the building proximate the thermal zone; using a predictive model for the building, determining set points for a heating, ventilating, and air conditioning ('HVAC') system associated with the thermal zone that minimize energy use by the building; the desired temperature range and the forecast ambient temperature value being inputs to the predictive model; the predictive model being trained using respective historical measured value data for at least one of the inputs; and, controlling the HVAC system with the set points to maintain an actual temperature value of the thermal zone within the desired temperature range for the thermal zone.

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Bedford, Canada

Alexandre Pavlovski