Abstract:
An energy optimization system for a building includes a processing circuit configured to generate a user interface including an indication of one or more economic load demand response (ELDR) operation parameters, one or more first participation hours, and a first load reduction amount for each of the one or more first participation hours. The processing circuit is configured to receive one or more overrides of the one or more first participation hours from the user interface, generate one or more second participation hours, a second load reduction amount for each of the one or more second participation hours, and one or more second equipment loads for the one or more pieces of building equipment based on the received one or more overrides, and operate the one or more pieces of building equipment to affect an environmental condition of the building based on the one or more second equipment loads.
Abstract:
A heating, ventilation, or air conditioning (HVAC) system for a building includes an air handling unit (AHU) and a controller. The AHU is configured to provide mechanical cooling for a cooling load in the building when operating in a mechanical cooling state and provide free cooling for the cooling load in the building when operating in a free cooling state. The controller is configured to calculate a minimum free cooling time based on an estimated cost savings resulting from operating in the free cooling state relative to operating in the mechanical cooling state and transition the AHU from operating in the mechanical cooling state to operating in the free cooling state in response to predicting that outside air temperature will be less than a free cooling temperature threshold for at least the minimum free cooling time.
Abstract:
A state-based control system for an air handling unit (AHU) includes a finite state machine configured to transition between a high cooling load state and a low cooling load state. In the high cooling load state, the system maintains the temperature of a supply airstream provided by the AHU at a fixed setpoint and controls the temperature of a building zone by modulating the speed of a supply air fan. In the low cooling load state, the system operates the supply air fan at a fixed speed and controls the zone temperature by modulating an amount of cooling applied to the supply airstream by one or more cooling stages. A feed-forward module manages disturbances caused by adding or shedding cooling stages by applying a feed-forward gain to the supply air fan setpoint.
Abstract:
Systems and methods for controlling a central plant for a building are provided. The central plant has a plant load. An optimal combination of plant equipment for meeting the plant load is estimated. Estimating the optimal combination of plant equipment includes using binary optimization to determine at least two potential combinations of plant equipment. Estimating the optimal combination of plant equipment also includes using nonlinear optimization to determine a potential power consumption minimum for each of the at least two potential combinations. The central plant is controlled according to the estimated optimal combination of plant equipment.
Abstract:
Systems and methods for auto-commissioning and self-diagnostics of equipment in a building management system are provided. A self-testing module is implemented in a control unit of the building management system. The self-testing module exercises equipment of the building management system using a state-based testing procedure that differs from normal operation of the equipment and monitors feedback received from a sensor of the building management system in response to exercising the equipment. The self-testing module uses the feedback from the sensor to evaluate a state transition condition of the state-based testing procedure and to transition between states of the state-based testing procedure using a result of the evaluation.
Abstract:
An adaptive capacity constraint management system receives a measured value affected by HVAC equipment at actual operating conditions and uses the measured value to determine an operating value for a variable that affects a capacity of the HVAC equipment at the actual operating condition. The system uses the operating value to calculate a gain factor for the variable relative to design conditions and uses the calculated gain factor to determine a capacity gain for the HVAC equipment relative to the design conditions. The system applies the capacity gain to a design capacity limit for the HVAC equipment to determine a new capacity limit for the HVAC equipment at the actual operating conditions. The system may use the new capacity limit as a constraint in an optimization routine that that selects one or more devices of the HVAC equipment to satisfy a load setpoint.
Abstract:
A state-based control system for an air handling unit (AHU) includes a finite state machine configured to transition between a high cooling load state and a low cooling load state. In the high cooling load state, the system maintains the temperature of a supply airstream provided by the AHU at a fixed setpoint and controls the temperature of a building zone by modulating the speed of a supply air fan. In the low cooling load state, the system operates the supply air fan at a fixed speed and controls the zone temperature by modulating an amount of cooling applied to the supply airstream by one or more cooling stages. A feed-forward module manages disturbances caused by adding or shedding cooling stages by applying a feed-forward gain to the supply air fan setpoint.
Abstract:
An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment.