The invention relates to a ventilator unit comprising a housing defining an air passage with a first port and a second port, a valve arranged in the air passage to control the air flow rate through the air passage, an actuator configured to operate the valve, and a controller configured to drive the actuator. The housing at the first port is configured to connect the first port to a duct outlet, and wherein the controller is configured to autonomously determine a drive signal to be sent to the actuator based on air measurement data.

Using processes and methods described herein, a digital twin of a physical space can train itself using sensors and other information available from the building. In some embodiments, a system to be controlled comprises a controller that is connected to sensors. This controller also has a thermodynamic model of the system to be controlled within memory associated with the controller. The thermodynamic model has neurons that represent distinct pieces of a controlled space, such as a piece of equipment or a thermodynamically coherent section of a building, such as a window. The neurons represent these distinct pieces of the controlled space using parameter values and equations that model physical behavior of state with reference to the distinct piece of the controlled state. A machine learning process refines the thermodynamic model by modifying the parameter values of the neurons, using sensor data gathered from the system to be controlled as ground truth to be matched by behavior of the thermodynamic model. The thermodynamic model may be warmed up by running the model using state data as input.

Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

A distributed zone control system having one or more vents with sensors and effectors is provided. The effectors operate in response to the sensors. In various embodiments, the effectors of one vent may operate in response to the sensors of another vent. In various instances, the distributed zone control system connects to an HVAC system and controls operation of the HVAC system. In this manner, temperatures may be detected by a vent and conditioned air from the HVAC system directed to appropriate zones to maintain desired conditions.