Abstract: Identifying models of dynamic systems is described herein. One method for identifying a model of a dynamic system includes estimating a number of parameters for each of a number of models of the dynamic system, predicting an output using the estimated number of parameters for each of the number of models, calculating a rate of error of the predicted output for each of the number of models compared to an observed output, and identifying a best model among the number of models of the dynamic system based on the calculated rate of errors.

Abstract: Tuning model structures of dynamic systems are described herein. One method for tuning model structures of a dynamic system includes predicting a variable for each of a number of models associated with a number of model structures of a dynamic system, calculating a rate of error of the predicted variable for each of the number of models compared to an observed variable, determining a best model structure among the number of model structures based on the calculated rate of error, and creating a revised model structure using the best model structure to tune the number of model structures of the dynamic system.

Abstract: A thermal control system for a building is disclosed, which includes a regression model: Given a forecast temperature outside the building, the regression model predicts how much an HVAC system will cost to run during a day, for a given set of time-varying target temperatures for all the thermostats in the thermal control system. The thermal control system may also include an optimizer, which invokes multiple applications of the regression model. Given a forecast temperature outside the building, the optimizer predicts an optimal set of time-varying target temperatures for all the thermostats in the thermal control system. Running the HVAC system with the optimal set of time-varying target temperatures should have a reduced or a minimized cost, or a reduced or minimized total energy usage. The optimizer works by running the regression model repeatedly, while adjusting the time-varying target temperature for each thermostat between runs of the model.

Abstract: Devices, methods, and systems for determining a heating, ventilation, and air conditioning (HVAC) model for a building are described herein. One method includes receiving a description of the building, receiving an HVAC meta-model, wherein the HVAC meta-model is independent of the building, and determining an HVAC model for the building based on the description of the building and the HVAC meta-model.

Abstract: Systems, methods, and devices for operation of a thermal comfort system are described herein. For example, one or more embodiments include receiving data, including a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system, and determining, through a regression model, from the received data, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.

Abstract: A system and method obtain a database stored on a storage device containing information on multiple assets, the information including measurements taken from devices monitoring each asset, and context information corresponding to the environment the items are subjected to. The system and method groups assets via a computer system into a homogenous group as a function of selected context information and performs analytics via the computer system on the grouped assets to manage the assets.

Abstract: In an embodiment, a system and method are described that enable a display of a parallel coordinate plot. Such a parallel coordinate plot includes a plurality of parallel axes, and each of the parallel axes represents a variable. The display further includes one or more polygonal lines connecting values of the variables of the parallel axes. The system and method further enable a display of information about an additional variable on the parallel coordinate plot by varying the property of the one or more polygonal lines.

Abstract: A system for utility base lining records historic values of utility loads for regions within a facility. The system also records historic values of independent variables such as outside temperature, time, date, workday versus non-workday, and occupancy. A similar data selector seeks out similar times in the past and submits the data from those times to a base line estimator which produces a baseline estimate. Differences between the current load and the estimated load can trigger alarms or investigations to determine why the utility load has changed. False alarms can occur when the utility load drifts over time. Detecting drift can help reduce false alarms or help in the investigation of alarms. A graphical user interface (GUI) can present an easily understood representation of the regions, load estimation errors, alarms, and detected drift.

Abstract: A system for utility base lining records historic values of utility loads for regions within a facility. The system also records historic values of independent variables such as outside temperature, time, date, workday versus non-workday, and occupancy. A similar data selector seeks out similar times in the past and submits the data from those times to a base line estimator which produces a baseline estimate. Differences between the current load and the estimated load can trigger alarms or investigations to determine why the utility load has changed. False alarms can occur when the utility load drifts over time. Detecting drift can help reduce false alarms or help in the investigation of alarms. A graphical user interface (GUI) can present an easily understood representation of the regions, load estimation errors, alarms, and detected drift.

Abstract: A method includes receiving energy-related information associated with multiple elements in a hierarchically-arranged domain. The method also includes determining a value of an energy-related metric for each of the elements using the energy-related information. The method further includes generating a graphical user interface using the metric values and presenting the graphical user interface to a user. The graphical user interface includes a treemap having multiple sections, each associated with one of the elements. The graphical user interface also includes a graph displaying energy-related information associated with a selected element. A size of each section in the treemap could be based on a size, importance, energy usage, and/or carbon emission of the associated element. A color and a color intensity of each section in the treemap could be based on the metric value of the associated element and/or a comparison of the absolute energy usage to a baseline.

Abstract: In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.

Abstract: A process includes providing a plurality of forecasts from a plurality of forecasting models. The plurality of forecasts each includes a mean and a variance. A model weight is calculated for each forecasting model. The model weight is proportional to the ability of that model to successfully forecast a queried situation. The plurality of forecasts are combined using an aggregate mean and an aggregate variance of the plurality of forecasts.

Abstract: The subject mater herein relates to oil well testing and, more particularly, automated oil well test classification. Various embodiments described herein provide systems, methods, and software for statistical analysis and classification of oil well tests. Some embodiments include receiving a first set of oil well test results from one or more measurement devices of a well test separator, storing the first set of oil well test results in a database, and annotating one or more tests of the first set oil well test results. The annotated test results are then used to build one or more classification models to enable automated oil well test classification as new oil well tests are performed.

Abstract: Methods, computer program products, and systems adapted to aid in data analysis by graphically displaying current situation on the background of historical data density, Historical data density is represented by one or more contour plots. An indicator of how a current data point compares to the historical data may be placed on one or more of the data density graphs. In some examples, data falling outside a threshold contour of historical data density may represent a new or an abnormal situation, and such data and/or a contour plot having such data may be highlighted in a graphical display.

Abstract: The subject mater herein relates to oil well testing and, more particularly, automated oil well test classification. Various embodiments described herein provide systems, methods, and software for statistical analysis and classification of oil well tests. Some embodiments include receiving a first set of oil well test results from one or more measurement devices of a well test separator, storing the first set of oil well test results in a database, and annotating one or more tests of the first set oil well test results. The annotated test results are then used to build one or more classification models to enable automated oil well test classification as new oil well tests are performed.

Abstract: In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process.

Abstract: When modeling an object in a visual input image, a model likelihood function for generating a predicted model based on the input data is moderated as a function of the portion of the object so as to less penalize regions of the model that tend to be more noisy. For instance, background subtraction algorithms often do not accurately interpret shadows of objects because the characteristics of shadows depend on so many factors, such as cloudiness, angle of the sun's rays, topography of the background upon which the shadow is cast, etc. Accordingly, the model likelihood function applied to a predicted shadow region of the model will be moderated so as to less penalize differences between the model and the input image with respect to shadows then with respect to other more reliably predictable portions of the image.

Abstract: In an embodiment, a system and method are described that enable a display of a parallel coordinate plot. Such a parallel coordinate plot includes a plurality of parallel axes, and each of the parallel axes represents a variable. The display further includes one or more polygonal lines connecting values of the variables of the parallel axes. The system and method further enable a display of information about an additional variable on the parallel coordinate plot by varying the property of the one or more polygonal lines.

Abstract: A system and method initializes objects in video data. In an embodiment, the video data is an output of a video tracker, and in a particular embodiment, the video tracker is a particle filter. A histogram is calculated that indicates a number of particles that do not cover an object in an input image from the particle filter at a position in the input image. The system and method then initializes an object to be tracked in the input image as a function of the histogram.

Abstract: A system or algorithm for model tuning and adaptation. The algorithm may be used for system identification and modeling. There may be an estimation of model structure and parameters. There may be filtering for estimating model structure and tuning model parameters. There may additionally be model adaptation in a case of modeling a time-variant system. Each particle of the filter may represent a model structure and model parameters of a system. The weight of a particle may be proportional to an underlying model's ability to simulate a system. The algorithm may continue evaluation by resampling the particle set, applying dynamics to each particle of the set, and updating the particle weight.