Some embodiments include electric power demand stabilization methods and systems that may include receiving an indication that a specific controllable device will have a high power draw event; retrieving a power draw profile for the specific controllable device that includes at least a maximum power draw and an event duration; identifying a plurality of low priority controllable devices with a combined power draw that is substantially equal to the maximum power draw of the specific controllable device; and turning off the plurality of low priority controllable devices for a time period substantially equal to the event duration.

There is provided a comfort management system including a networked comfort management control device. The comfort management control device operates an HVAC interface to maintain an environment utilizing a determined comfort zone range for one or more occupants of an area treated by the HVAC system, and utilizes controlled deviations from an initial set point to maintain comfort while maximizing energy efficiency of the HVAC system.

A computer-implemented method for producing probabilistic forecasts of extreme values. The method comprises obtaining input data comprising a plurality of signals of interest and a plurality of covariates associated therewith, each covariate of the plurality of covariates having an associated data type. The method further comprises performing a first forecast based on the input data. Performing the first forecast comprises: obtaining one or more trained machine learning models, each trained machine learning model of the one or more trained machine learning models having been trained to map one or more covariates of a respective data type to one or more surrogate covariates; mapping, using the one or more trained machine learning models and the input data, the plurality of covariates to one or more surrogate covariates, the one or more surrogate covariates corresponding to a compressed representation of the input data; fitting a statistical model of extremes to the plurality of signals of interest and the one or more surrogate covariates thereby generating a fitted statistical model of extremes, the statistical model of extremes being defined according to a predetermined distribution having a plurality of parameters; and obtaining a probabilistic forecast of future extreme values based on the fitted statistical model of extremes for one or more future time steps. The method further comprises causing control of a controllable system based at least in part on the probabilistic forecast of future extremes.

A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.

Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates 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 one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

A method for controlling a plant includes using a neural network modeling technique to calculate a neural network prediction based on plant input data, using a second modeling technique to calculate a second prediction based on the plant input data, and determining whether to use (1) the neural network prediction without the second prediction, (2) the second prediction without the neural network prediction, or (3) both the neural network prediction and the second prediction by comparing a location of the plant input data in a multi-dimensional modeling space to one or more thresholds. The method includes generating a combined prediction using one or both of the neural network prediction and the second prediction in accordance with a result of the determining and controlling the plant using the combined prediction.

A setting method according to the present invention determines a desired time response in an optimum servo control structure corresponding to a servo control structure of a control target, calculates a predetermined gain corresponding to the desired time response, and calculates a first weighting coefficient Qf, a second weighting coefficient Q, and a third weighting coefficient R of a predetermined Riccati equation according to the Riccati equation on the basis of the predetermined gain. The first weighting coefficient Qf, the second weighting coefficient Q, and the third weighting coefficient R are set as a weighting coefficient corresponding to a terminal cost, a weighting coefficient corresponding to a state quantity cost, and a weighting coefficient corresponding to a control input cost, respectively, in a predetermined evaluation function for model prediction control.

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.