A controller for building equipment that operate to produce or consume resources for a building or campus. The controller performs an optimization of an objective function subject to an override constraint to determine values for a plurality of decision variables indicating amounts of resources to be produced or consumed by the building equipment. The override constraint defines one or more of the values for a subset of the plurality of decision variables by specifying an override amount of a first resource of the resources to be produced or consumed by a first subset of the building equipment and the optimization determines a remainder of the values for a remainder of the plurality of decision variables. The controller controls the building equipment to produce or consume the amounts of the resources determined by performing the optimization subject to the override constraint.

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

In an example method, a power management system receives sensor data regarding an operation of a primary power source, a secondary power source, an environmental regulation system, and a plurality of electrically-powered sub-systems. Further, the system receives a plurality of parameter sets for the sub-systems, each including a first parameter indicting a priority of a respective sub-system relative to the other sub-systems, a second parameter indicating an amount of heat dissipated by the respective sub-system during operation, and a third parameter indicating a temperature requirement associated with the respective sub-system. The system controls, based on the sensor data and the parameter sets, a delivery of electrical power from the primary and secondary power sources to the environmental regulation system and the sub-systems. Further, the system controls, based on the sensor data and the parameter sets, a consumption of electrical power by the environmental regulation system and the sub-systems.

Systems and methods for orchestrating the operation of energy-consuming loads, so as to minimize power consumption, are described. In some embodiments, the loads can be HVAC, refrigeration systems, air compressors, and the like, and orchestration is effected either directly or by means of the loads' respective controllers. In some aspects, the controllers can be Smart Thermostats and orchestration is effected through a Cloud-based orchestration platform or “COP.” In certain aspects, a COP uses specifically programmed application programming interfaces, or APIs, to control the operation of a single manufacturer's Smart Thermostats, where the manufacturer provides its own Cloud-based control platform, through which the COP operates. The COP can similarly orchestrate the operation of two or more manufacturers' Smart Thermostats through their respective Cloud-based control platforms. By these and other means, the operation of a variety of energy-consuming loads can be more easily and efficiently orchestrated.

A load control system for controlling the amount of power delivered from an AC power source to a plurality of electrical load includes a plurality of energy controllers. Each energy controller is operable to control at least one of the electrical loads. The load control system also includes a first broadcast controller that has a first antenna and a second antenna. The first antenna is arranged in a first position and the second antenna is arranged in a second position that is orthogonal to the first position. The broadcast controller is operable to transmit a first wireless signal via the first antenna and a second wireless signal via the second antenna. Each of the energy controllers is operable to receive at least one of the first and second wireless signals, and to control the respective load in response to the received wireless signal.

A home appliance can operate in a future time frame. Information is obtained from a power distributor in order to determine a time when to operate the home appliance in this future time frame. The home appliance then operates at the time determined with information from the power distributor.

A load control system for a building having a lighting load, a window, and a heating and cooling system comprises a lighting control device, a daylight control device, and a temperature control device operable to be controlled so as to decrease a total power consumption of the load control system in an energy-savings mode. The energy-savings mode can be manually overridden in response to actuation of the actuator of an input control device, such that the load control system enters a manual mode for manually adjusting the loads controlled by the lighting control device, the daylight control device, and the temperature control device. The load control system is operable to automatically return to the energy-savings mode at a time after the load control system entered the manual mode.

Methods and systems are provided for managing environmental conditions and energy usage associated with a site. One exemplary method of regulating an environment condition at a site involves a server receiving environmental measurement data from a monitoring system at the site via a network, determining an action for an electrical appliance at the site based at least in part on the environmental measurement data and one or more monitoring rules associated with the site, and providing an indication of the action to an actuator for the electrical appliance.

A model identification system includes a device information acquiring unit that acquires device information used to identify a model of an electric device, an operation extracting unit that extracts data of a predetermined operation section, a feature quantity extracting unit that extracts a parameter used to identify the electric device, and a model identifying unit that identifies a model of an electric device, wherein the feature quantity extracting unit performs a machine learning process by sampling the data of the predetermined operation section extracted from the operation extracting unit a plurality of times, extracts a parameter corresponding to each sampling, and extracts a parameter appropriate to identify a model among a plurality of sampled parameters.

A method and system for power supply integration, comprising interfacing at least one of: i) an AC power supply and ii) a DC power supply, and supplying, from the at least one of: i) an AC power supply and a ii) a DC power supply, DC power to the at least one DC load. The energy supply to the at least one DC load from the at least one AC power supply and the at least one DC power supply is controlled so as to selectively supply power from renewable energies for example.