An operation management device includes a state acquiring unit that acquires a measurement value of a first state amount indicating an operation state of a power generation plant, a state updating unit that updates an estimation value of a second state amount, which indicates the operation state of the power generation plant and is a state amount different from the first state amount, based on the measurement value of the first state amount, and a managing unit that manages the operation state of the power generation plant based on the estimation value of the second state amount.

Systems and methods for identifying optimal siting locations for an energy storage system. Siting locations are identified based on a value index derived from pricing data associated with a plurality of nodes on an electrical grid. An index is derived for each selected node of the plurality of nodes to produce a siting recommendation.

A system for controlling a subplant comprising one or more assets includes one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations including generating a design curve for a first asset included in the subplant based on an asset model, the design curve comprising a plurality of data points that define an operation of the first asset, obtaining operational data for the first asset, determining a degradation factor for the first asset by comparing the design curve and the operational data, generating an operational curve for the first asset by derating the design curve based on the degradation factor, and operating the subplant based on the operational curve.

An electricity automation application may automate control over an HVAC system in a consumer's home or other building to reduce or eliminate electricity consumption during a high-price or high-demand time interval. The consumer may grant authorization for the application to control the HVAC system at appropriate timepoints while maintaining an inside air temperature that is between minimum and maximum temperature setpoints set by the consumer. The application models temperature gains and losses for the home or building as a function of inside and outside temperatures and HVAC operating status. If a high-price or high-demand time interval is predicted, the application may determine a timepoint to precool or preheat the home or building to reduce or eliminate electricity use during the duration of the high-price or high-demand time interval.

There is provided a load controller for a system, the system comprising a first sub-system arranged to deliver a first load, the load controller being operable to: acquire a first target load profile, being the load initially desired for delivery by the first sub-system over an operational period; measure in real time during an update window within the operational period: a first parameter of the first sub-system, to obtain a first measured Load Controller monitor signal; and the first load, to obtain a first measured load signal; develop in real time a model of the first sub-system, using the first measured monitor signal and the first measured load signal, the model relating the first load to the first parameter; given the first target load profile, and the model of the first sub-system, generate for a future period a first predicted monitor signal, the future period being ahead of the update window; and determine whether the first predicted monitor signal satisfies at least one predetermined criterion.

Described are methods and systems for evaluating glycemic control and glucose homeostasis in a subject. Also described is a model of glucose homeostasis based on proportional and integral terms in a control system. A representative curve is generated based on glucose time series data and fit to the model in order to determine coefficients for each subject. The coefficients provide a digital biomarker of glycemic control for the subject and may be used to identify subjects with glycemic dysfunction.

A machining condition searching method includes: generating a machining condition to be set in a machining device; collecting a machining state; collecting the machining result performed under the machining condition; calculating an evaluation value of the machining on the basis of the machining result; constructing an evaluation value prediction model predicting, on the basis of the machining condition, the machining state, and the evaluation value, an evaluation value corresponding to the machining condition that has not been tried; and constructing the evaluation value prediction model on the basis of a change degree in the relationship between the machining condition and the evaluation value, and performs weighting based on the machining state on the evaluation value prediction model. The machining condition to be tried next is generated using a predictive value of the evaluation value. Each of the above processes is repeatedly performed until it is determined to end a search.

Provided is a prediction apparatus including: a data acquisition unit configured to acquire setting value data indicating a setting value of a controlled object and physical quantity data indicating a physical quantity of a product obtained by controlling the controlled object; a prediction unit configured to calculate, using the setting value data and the physical quantity data, a plurality of prediction values obtained by predicting a plurality of physical quantities in the product on a basis of a setting value used for control of the controlled object; an evaluation unit configured to evaluate the plurality of prediction values on a basis of a predefined reference; and an output unit configured to output a setting value recommended according to a result of the evaluation.

Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

A method for controlling building equipment includes providing a user interface comprising a graphical representation of a relationship between a carbon emissions control objective and a second control objective that competes with the carbon emissions control objective over a range of control strategies for the building equipment, and assigning a weight to the carbon emissions control objective or the second control objective in an objective function. The weight is associated with a control strategy that corresponds to a user selection based on the graphical representation. The method also includes generating control decisions for the building equipment using the objective function with the weight assigned to the carbon emissions control objective or the second control objective and operating the building equipment in accordance with the control decisions.