A predictor that projects the service offering available at a point in time on the basis of the agreed technical and contractual rules is provided. The predictor further makes the same available to the operating and scheduling systems of the industrial company and optimizes the prediction parameters by observing and comparing the real situation with the calculated situation, or else indicates unused potential.

Certain examples described herein provide a system and a method for disaggregating energy load data for a building. The system (410) may have an energy load data interface (420) to receive energy load data (422) originating from energy use sensors for the building; a weather data interface (430) to receive weather data (432) for a location that includes the building; a weather adjustment pre-processor (440) to process the energy load data and the weather data and to determine a weather-dependent energy use component (442) of the energy load data and a weather-independent energy use component (444) of the energy load data; a baseline adjustment pre-processor (450) to process the weather-independent energy use component of the energy load data and determine a baseline energy use component (456) of the energy load data, wherein the baseline adjustment pre-processor is configured to remove the baseline energy use component from the weather-independent energy use component to determine a variable energy use component (458) of the energy load data; and an energy use disaggregator (460) to process the variable energy use component of the energy load data and determine a plurality of time-varying load components (462) of the energy load data.

Disclosed is a coordinated peak shaving optimization method for a plurality of power supplies based on a fluctuation characteristic of a renewable energy source, including the following steps: s1: determining a weekly generated electricity quantity of hydropower based on an available capacity and a storage capacity of an electricity quantity; s2: predicting a renewable energy power generation curve and a load curve of a system weekly; s3: determining a start point of peak shaving of the hydropower based on an external transmission curve, the renewable energy generation curve, and the load curve of the system and a generating capacity of the hydropower; s4: determining a weekly peak shaving demand of the system; and s5: establishing an optimization model with a maximum peak shaving demand. The present disclosure proposes a reasonable arrangement for peak shaving, so as to resolve an accommodation problem caused by large-scale access of a renewable energy source.

Systems and methods for machine forward energy and energy credit purchase are disclosed. An example transaction-enabling system may include a machine having an energy requirement for a task and a controller. The controller may include a resource requirement circuit to determine an amount of an energy resource for the machine to service the energy requirement, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute a transaction of on the forward resource market in response to the determined amount of the energy resource.

A controller for building equipment that operate to produce or consume resources for a building or campus. The controller performs an optimization of an objective function subject to an override constraint to determine values for a plurality of decision variables indicating amounts of resources to be produced or consumed by the building equipment. The override constraint defines one or more of the values for a subset of the plurality of decision variables by specifying an override amount of a first resource of the resources to be produced or consumed by a first subset of the building equipment and the optimization determines a remainder of the values for a remainder of the plurality of decision variables. The controller controls the building equipment to produce or consume the amounts of the resources determined by performing the optimization subject to the override constraint.

A computer-implemented method for determining a mitigated peak power exchange value for power exchange between a supplier and a facility includes receiving a timeseries comprising power exchange values for power exchange between the supplier and the facility over a predetermined time period; executing an optimization algorithm configured to determine a mitigated maximum power exchange value in the timeseries using data defining one or more peak shaving capabilities at the facility as variables; and outputting the determined mitigated maximum power exchange value.

Systems and methods for forward market purchase of machine resources are disclosed. An example transaction-enabling system may include a fleet of machines, each one of the fleet of machines having a resource requirement comprising at least one of a plurality of machine-related resources and a controller. The controller may include an intelligent agent circuit to aggregate data for the plurality of machine-related resources from at least one data source comprising an external data source or an internal data source; an expert system circuit to configure a purchase of at least one of the plurality of machine-related resources; and a machine resource acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one resource of the plurality of machine-related resources.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility having a core task and a controller. The controller may include a facility description circuit to interpret historical facility parameter values and corresponding outcome values. A facility prediction circuit operates an adaptive learning system to train a facility resource allocation circuit in response to the historical facility parameter values and corresponding outcome values. The facility description circuit further interprets a plurality of present state facility parameter values and the trained facility resource allocation circuit adjusts facility resource values in response.

A control system of a wind power installation and/or a wind farm is provided. The control system has a plurality of operating modes and has an interface. The interface is configured for providing maximum adjustability of the wind power installation and/or the wind farm in critical power grid situations. The interface is configured to receive a signal of a power grid operator, as a result of which all of the plurality of operating modes are released and made available to the power grid operator.

A load prediction method includes determining for a current prediction time point of a prediction day, a similar days of a prediction day from historical days before the prediction day; acquiring an air temperature difference between an air temperature at the time point of the prediction day and an air temperature at the time point of the similar day; determining a difference of loads at the time point of the prediction day and the similar day according to the air temperature difference; and determining a predicted load value at the time point of the prediction day according to a load value at the time point of the similar day and the load difference.