In some implementations, a system performs proactive performance tests for an appliance before a time for an operational change in usage of the appliance. Usage data for an appliance associated with a property may be obtained. The obtained usage data indicates past activity of the appliance and present operational status of the appliance. Weather forecast data associated with a location of the property can be obtained. A time for an operational change in usage of the appliance can be predicted based at least on the obtained usage data for the appliance and the obtained weather forecast data. An operation directed to conducting one or more performance tests on the appliance can be performed before the predicted time for the operational change in usage of the appliance. One or more communications related to the one or more performance tests of the appliance can be provided to a client device.

A demand response system includes a mobile application of a mobile device that is configured to initiate altering an operating condition of a network device disposed at a site using location based services. A demand response application interface module is configured to enable access between a utility company and the network device to communicate energy management information therebetween. The network device is configured to be remotely altered by each of the demand response application interface module and the mobile application separately based on the location based services and the energy management information. A method of managing a demand response system includes detecting a user being disposed away from a site, detecting energy management information from a utility company associated with the site, and initiating a reduction in energy use at the site in response to the relative location of the user and the energy management information.

Apparatus, systems and methods for ascertaining the occupancy of a building are presented. The building is divided into one or more control zones which correspond to physical areas of the building associated with controllable modules, such as HVAC units, lighting, irrigation, or other environmental features such as fountains, music, video, and the like. Zone parameters define how zone devices shall react to the number of occupants located in the particular zone. A building control system detects individual mobile devices in and around the building, and determines the locations of each device by using trilateration and/or location services. The identified mobile devices act as proxies for building occupants. The locations of these devices are correlated with the locations of the zones in the building, and the building control system then adjusts the operating parameters of the zone based on the number of devices present in the zone.

An HVAC controller is configured to automatically change between a HEAT mode and a COOL mode in accordance with a sensed temperature in the building structure, a HEAT temperature set point and a COOL temperature set point. The user is allowed to adjust the HEAT temperature set point and the COOL temperature set point, with the HVAC controller automatically adjusting one of the set points in response to the user making a change to the other of the other of the set points that violates a minimum deadband. If the user readjusts the user-adjusted set point in a way that no longer violates the minimum deadband, the HVAC controller will adjust the other set point back towards its previous setting.

Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

A thermal energy exchange and ventilated hollow core slab system and method within a building where the slab has an air passage with an inlet and outlet, an air handler unit having adjustable heating/cooling structure and, and ventilation structure connected to the hollow core concrete slab, and a building control connected to the hollow core concrete slab and air handler system for relative thermal exchange between the air and hollow core concrete slab to control user comfort; where the building is grid enabled.

A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

Systems and methods for operating a water recovery system and include activating a plurality of dampers, a fan, and a refrigeration system of the water recovery system. The method includes measuring an ambient air temperature of the water recovery system based on data obtained from an ambient air temperature sensor. The method includes measuring one or more evaporator temperatures associated with an evaporator of the water recovery system based on data obtained from one or more evaporator temperature sensors. The method includes determining an optimal evaporator air temperature of the water recovery system based on the one or more evaporator temperatures and the ambient air temperature. The method includes setting a speed of the fan of the water recovery system based on the optimal evaporator air temperature.