Abstract: A method of controlling the heating, ventilation and air conditioning (HVAC) system of a building, the method comprising the steps of: (a) developing an initial thermal model of the building, and continuously updating the thermal model over time; (b) utilising the thermal model to continuously develop a daily HVAC operating plan for the building; and (c) continuously examining a current HVAC operating plan and optimising the alignment of the current HVAC operation with the current HVAC operating plan.

Abstract: A method of controlling the heating, ventilation and air conditioning (HVAC) system of a building, the method comprising the steps of: (a) developing an initial thermal model of the building, and continuously updating the thermal model over time; (b) utilising the thermal model to continuously develop a daily HVAC operating plan for the building; and (c) continuously examining a current HVAC operating plan and optimising the alignment of the current HVAC operation with the current HVAC operating plan.

Abstract: A method for detecting faulty operation of a multi-variable system is described. The method includes receiving operational data from a plurality of components of the multi-variable system and processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results. Each fault detection model uses a plurality of variables to model one or more components of the multi-variable system and is adapted to detect normal or faulty operation of an associated component or set of components of the multi-variable system. The plurality of fault detection results are output.

Abstract: A method of controlling the heating, ventilation and air conditioning (HVAC) system of a building, the method comprising the steps of: (a) developing an initial thermal model of the building, and continuously updating the thermal model over time; (b) utilising the thermal model to continuously develop a daily HVAC operating plan for the building; and (c) continuously examining a current HVAC operating plan and optimising the alignment of the current HVAC operation with the current HVAC operating plan.

Abstract: A method for detecting faulty operation of a multi-variable system is described. The method includes receiving operational data from a plurality of components of the multi-variable system and processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results. Each fault detection model uses a plurality of variables to model one or more components of the multi-variable system and is adapted to detect normal or faulty operation of an associated component or set of components of the multi-variable system. The plurality of fault detection results are output.