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 energy management system of the present invention can measure and integrate a power consumption amount of each device, can integrate and monitor regional total power consumption amounts, and allows energy to be effectively used by collecting, analyzing, storing and transmitting a use pattern and data through the Internet of Things (IoT) between devices and completely and automatically cutting off and controlling the power to be wasted in a device when the device is not used. The energy management system remotely controls devices and allows energy to be effectively managed through the minimization of power consumption by automatically and completely cutting off the power to be supplied to the devices when the devices are not used, and by cutting off the power to be supplied to the system when the power of all the devices connected to the system is cut off and when the system is in standby.

A condition control of a robot cleaner is performed or service is provided with a user using the robot cleaner to improve the convenience of the user. Various data items obtained through a network connection are used in the condition control or service to estimate/determine a behavior, condition, or request of the user. Specifically, the operation of the robot cleaner is controlled based on operations of other associate devices disposed in the same room where the robot cleaner runs.

A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

An example of a building automation system utilizes intelligent system elements, some of which are lighting devices having light sources, and some of which are utility building control and automation elements. Some utility building control and automation elements include a controllable mechanism for use in control of some aspect of the building other than lighting. Another intelligent system element may include either a user interface component and be configured as a building controller, or include a detector and be configured as a sensor. Each intelligent system element includes a network communication interface, processor, memory and programming to configure the intelligent system element as a lighting device, utility building control and automation element, controller or sensor. At least one of the intelligent lighting devices is configured as a building control and automation system server. Several examples, however, implement the overall control using distributed processing.

A method for controlling a smart home using a smart watch is disclosed. The method includes: detecting whether the smart watch has entered a sensing range of the smart home; detecting, after the smart watch has entered the sensing range of the smart home, whether the smart watch has established a wireless connection with the smart home; turning on, after the smart watch has established the wireless connection with the smart home, a smart-home-control function of the smart watch; and while controlling the smart home using the smart-home-control function, recognizing hand gestures of the user using the smart watch and controlling the smart home through the wireless connection to switch current working state of the smart home based on the recognized hand gestures of the user.

A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

A system for remote diagnostic analysis of a heating, ventilation and air condition (HVAC) system is provided. The system includes a thermostat in operable communication with at least one peripheral component of the HVAC system and configured to receive information relating to the at least one peripheral component, and a server in operable communication with the thermostat for receiving and analyzing the information. The server causes the at least one peripheral component to conduct a diagnostic test and analyzes the test result to perform a root cause analysis of a system malfunction.

A system comprises premises devices located at a premises. A gateway device is located at the premises and may communicate with the premises devices. A server is configured to interact with the premises devices and the gateway device. A touchscreen device may communicate with the server and configured to interact with the premises devices. The touchscreen device includes a user interface configured to interact with the gateway device. The user interface is configured to control interactions between the premises devices and the gateway device and trigger, based on at least one automation rule, an action of at least one of the premises devices. Corresponding methods, apparatuses and other systems are also provided.

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.