HVAC control system and method

A method for controlling a Heating, Ventilation, and Air-Conditioning (HVAC) system, the method comprising:

• receiving sensed data associated with one or more external variables of a building from one or more of a plurality of external sensors, and sensed data associated with one or more internal variables of the building from one or more of a plurality of internal sensors at a Smart Metering Unit (SMU);
• transmitting, wirelessly, the received sensed data to a cloud server over a communication network, wherein the communication network is a self-healing secured network with a built-in route optimization;
• receiving one or more rules generated by a management platform of the cloud server, wherein the one or more rules are generated by the management platform by using an Artificial Intelligence (AI) algorithm;
• the AI algorithm:
• detecting an occupancy status of a virtual HVAC zone of the building based on motion sensor data;
• detecting an open window of the virtual HVAC zone based on the sensed data;
• training an AI model to predict an occupancy status of the virtual HVAC zone based on historic motion sensor data;
• training the AI model to predict an amount time necessary to change the temperature of the virtual HVAC zone by a predetermined amount;
• and
• training the AI model to predict times at which a side of the building is exposed to sunlight;
• and
• generating the one or more rules to control a temperature of the virtual HVAC zone based on the occupancy status, the detected open window, the predicted occupancy status, the predicted amount time necessary to change the temperature, and the predicted times at which a side of the building is exposed to sunlight;
• and
• transmitting the received one or more rules to a plurality of valve control boxes to control an operation of one or more actuator valves installed at one or more radiators and one or more air dampers to control heating and cooling of one or more virtual HVAC zones of the building;
• wherein the SMU is configured to monitor an amount of time taken by the valve control boxes to actuate the one or more valves and a cost of running the one or more valves per unit of time, and determine a cost of running each of the one or more valves against a total energy consumption of the HVAC system;
• and
• wherein the SMU is further configured to determine an energy saving of the building based on the cost of running each of the one or more valves against the total energy consumption of the HVAC system.

A computing device configured to operate as a computer-implemented control and optimization tool for a Heating, Ventilation, and Air-Conditioning (HVAC) system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media storing instructions which, when executed by the one or more processors, cause the computing device to:
• receive sensed data associated with one or more external variables of a building from one or more of a plurality of external sensors, and sensed data associated with one or more internal variables of the building from one or more of a plurality of internal sensors at a Smart Metering Unit (SMU);
• transmit, wirelessly, the received sensed data to a cloud server through a Lobby Control Unit (LCU) installed in the building over a communication network, wherein the communication network is a self-healing secured network with a built-in route optimization;
• receive one or more rules generated by a management platform of the cloud server through the LCU, wherein the one or more rules are generated by the management platform by using an Artificial Intelligence (AI) algorithm;
• the AI algorithm:
• detecting an occupancy status of a virtual HVAC zone of the building based on motion sensor data;
• detecting an open window of the virtual HVAC zone based on the sensed data;
• training an AI model to predict an occupancy status of the virtual HVAC zone based on historic motion sensor data;
• training the AI model to predict an amount time necessary to change the temperature of the virtual HVAC zone by a predetermined amount;
• and
• training the AI model to predict times at which a side of the building is exposed to sunlight;
• and
• generating the one or more rules to control a temperature of the virtual HVAC zone based on the occupancy status, the detected open window, the predicted occupancy status, the predicted amount time necessary to change the temperature, and the predicted times at which a side of the building is exposed to sunlight;
• and
• transmit the received one or more rules to a plurality of valve control boxes to control an operation of one or more actuator valves installed at one or more radiators to control eating and cooling of one or more virtual HVAC zones of the building;
• wherein the SMU is configured to monitor an amount of time taken by the valve control boxes to actuate the one or more valves and a cost of running the one or more valves per unit of time, and determine a cost of running each of the one or more valves against a total energy consumption of the HVAC system;
• and
• wherein the SMU is further configured to determine an energy saving of the building based on the cost of running each of the one or more valves against the total energy consumption of the HVAC system.