A control device has a monitoring section that monitors an amount of electric energy of a first machine, and a power supply controller that controls power supply of a second machine in accordance with an amount of electric energy of the first machine and schedule information indicative of an operation schedule of each of the first machine and the second machine.

A machine control device includes a command unit that outputs individual operation commands to respective manufacturing machines; a power excess/shortage decision unit that decides whether time-series data on total power consumption of the manufacturing machines associated with the operation commands has maximum peak power larger than a power supply capacity of the power facility; and an operation command adjustment unit that adjusts the operation commands to the manufacturing machines from the command unit so as to adjust the maximum peak power to the power supply capacity of the power facility or less and maximize a production amount of the manufacturing machines.

A control system for a central energy facility with distributed energy storage includes a high level coordinator, a low level airside controller, a central plant controller, and a battery controller. The high level coordinator is configured to perform a high level optimization to generate an airside load profile for an airside system, a subplant load profile for a central plant, and a battery power profile for a battery. The low level airside controller is configured to use the airside load profile to operate airside HVAC equipment of the airside subsystem. The central plant controller is configured to use the subplant load profile to operate central plant equipment of the central plant. The battery controller is configured to use the battery power profile to control an amount of electric energy stored in the battery or discharged from the battery at each of a plurality of time steps in an optimization period.

A planning tool used to facilitate more efficient design of a central plant is configured to provide an advanced user interface. The user interface includes a symbol palette with selectable symbols that represent resource suppliers, subplants, energy loads, and resource storage devices associated with a central plant. The user interface allows users to drag these symbols onto a workspace and form various connections between the symbols. The user interface provides feedback to the user and prevents improper connections by evaluating user inputs according to a set of rules. The rules define valid relationships between the resource suppliers, subplants, energy loads, and resource storage devices. The user interface also allows users to specify equipment contained within the subplants. After a central plant model is created via the user interface, users can simulate operation of a central plant according to the model for planning, budgeting, and/or design considerations.

A management device includes a data acquisition unit that collects data related to power consumption supplied to a plurality of industrial machines from at least a power supply facility, a preprocessing unit that creates power consumption data based on the collected data, a decision making unit that determines a behavior of allocating a predetermined total suspension time to the plurality of industrial machines with respect to a current state of power consumption by the plurality of industrial machines with reference to a learning model associating a value of a behavior of allocating the predetermined total suspension time to the plurality of industrial machines with a state of power consumption by the plurality of industrial machines based on power consumption data, and a suspension time allocation unit that allocates the suspension time to the plurality of industrial machines.

A demand prediction unit predicts a time series of demand values related to a predetermined prediction period using a predictive model. The predictive model is a learned model learned to output a demand value of an energy source by inputting an operation plan value of a plant and a predicted value related to an environment of the plant. An optimizing unit specifies operating indices of a plant that satisfy a plurality of demand values and satisfy a desired condition for each time related to the predicted time series of demand values. A presentation unit presents information related to the time series of operating indices related to the prediction period.

A device and method are disclosed for optimizing self-power consumption. The device may sense one or more operating conditions of the device. The device may further select one or more operating parameters associated with at least one of the one or more operating conditions. The device may also estimate a power consumption associated with executing an algorithm to generate at least one updated value for at least one of the one or more operating parameters as well as estimate a power savings associated with operating using the updated value. The device may compare the estimated power consumption to the estimated power savings and determine whether to execute the algorithm based on the comparing.

A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

A building management system (BMS) includes sensors that measure time series values of building variables and a deterministic model generator that uses historical values for the time series of building variables to train a deterministic model that predicts deterministic values for the time series. The BMS includes a stochastic model generator that uses differences between actual values for the time series and the predicted deterministic values to train a stochastic model that predicts a stochastic value for the time series. The BMS includes a forecast adjuster that adjusts the predicted deterministic values using the predicted stochastic value to generate an adjusted forecast for the time series. The BMS includes a demand response optimizer that uses the adjusted forecast to generate an optimal set of control actions for building equipment of the BMS. The building equipment operate to affect the building variables.

A system for operating or designing building equipment is configured to provide a user interface comprising a workspace with selectable symbols representing multiple devices of building equipment, evaluate a user input defining a connection between at least two of the selectable symbols on the workspace according to a set of rules to determine whether the connection is valid or invalid, prevent the user from making the connection in response to determining that the connection is invalid, generate a valid model of a system comprising the multiple devices of building equipment in response to determining that the connection is valid, execute a model-based control process or simulation using the valid model to generate control decisions or design decisions for the multiple devices of building equipment, and operate or design the multiple devices of building equipment in accordance with the control decisions or the design decisions.