Abstract: Methods, devices, and systems for fault propagation in a building automation system are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to receive an input associated with a fault occurring in the building automation system, execute a fault propagation of the fault using fault rules for the building automation system and causality relationships in a building information model associated with the building automation system, and generate, using the fault propagation of the fault, a fault output with respect to the building automation system.

Abstract: A tool for providing a visualization of a system may reveal an interactive navigation environment for building performance observation and assessment. The tool may be associated with a processor. The environment may incorporate a treemap, a graph pane, a treemap filter, a graph pane selector, a selected units box and a date/time control mechanism. A visualization of the environment, among other things, may be presented on a display. The treemap may exhibit a building geometry and/or equipment units hierarchically, along with some data information. Units may be interactively selected from the treemap and placed in the box for analysis. The graph pane may show a configuration and display of unit analysis. Selection of detailed views for units in the box may be provided by the graph pane selector. Date and time intervals for analysis may be selected by the control mechanism.

Abstract: A method includes obtaining energy consumption information representative of energy consumption behaviors of multiple customers, grouping the multiple customers into multiple different clusters based on the consumption behaviors of the multiple customers, and generating an energy consumption model for each different cluster to enable forecasting of energy demand of the multiple customers.

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: A method includes obtaining energy consumption information representative of energy consumption behaviors of multiple customers, grouping the multiple customers into multiple different clusters based on the consumption behaviors of the multiple customers, and generating an energy consumption model for each different cluster to enable forecasting of energy demand of the multiple customers.

Abstract: A property of a signal may be detected by sampling a signal and determining a scalar indicator of the samples. The samples can be transformed to create a transformed signal. A scalar indicator of the transformed signal may be determined and compared to the scalar indicator of the sample. If the comparison yields a number greater than a selected threshold value then the signal that was sampled may be deemed to include the property. A property indicator signal may be driven low or high to indicate that the signal has the property. The time and duration of time that the signal is deemed to include the property may be recorded. The recorded data may be reviewed and appropriate action may be taken.

Abstract: A tool for providing a visualization of a system may reveal an interactive navigation environment for building performance observation and assessment. The tool may be associated with a processor. The environment may incorporate a treemap, a graph pane, a treemap filter, a graph pane selector, a selected units box and a date/time control mechanism. A visualization of the environment, among other things, may be presented on a display. The treemap may exhibit a building geometry and/or equipment units hierarchically, along with some data information. Units may be interactively selected from the treemap and placed in the box for analysis. The graph pane may show a configuration and display of unit analysis. Selection of detailed views for units in the box may be provided by the graph pane selector. Date and time intervals for analysis may be selected by the control mechanism.

Abstract: A tool for providing a visualization of a system may reveal an interactive navigation environment for building performance observation and assessment. The tool may be associated with a processor. The environment may incorporate a treemap, a graph pane, a treemap filter, a graph pane selector, a selected units box and a date/time control mechanism. A visualization of the environment, among other things, may be presented on a display. The treemap may exhibit a building geometry and/or equipment units hierarchically, along with some data information. Units may be interactively selected from the treemap and placed in the box for analysis. The graph pane may show a configuration and display of unit analysis. Selection of detailed views for units in the box may be provided by the graph pane selector. Date and time intervals for analysis may be selected by the control mechanism.

Abstract: A virtual sub-metering process using combined classifiers includes generating an electric power consumption signature database by receiving data from an electric power consumption measuring meter and auxiliary data from a building management system, and clustering the data. After the generation of the electric power consumption signature database, additional data from the electric power consumption measuring meter and the auxiliary data from the building management system is received, and this additional data is processed to generate a second steady-state load classification component. A second transient state component is extracted from the additional data.

Abstract: A demand response system having an improved load forecaster connected to a decision engine. A basis of the improved forecaster may be an introduction of an explanatory variable which is a time-based shaping function that allows capturing a demand response (DR) lead and DR rebound effect, and the like, capturing a shape of load reduction, given by an applied DR action. The engine may receive information from the forecaster and utility relative to behavior of a DR customer, market price, renewable energy generation, grid status, and so on. The engine may provide optimal timing, selection of resources, and so forth, to a DR automation server, which in turn may provide DR signals to customers. The customers may provide data consumption data to a database. Electricity generation data may also be provided to the database. Selected relevant data from the database and weather information may go to the load forecaster.

Abstract: A system for monitoring thermal comfort of a room. Sensor data concerning temperature, relative humidity, air speed and air flow may be collected from the room. The sensor data may provide thermal comfort level information about the room. The level information may be quantified in terms of a thermal comfort level index, such as a predicted mean vote, which can be used to identify one or more thermal comfort levels in the room with a numerical measure. The one or more levels may be portrayed as a visualization in terms of a 3D plot of temperature, humidity and air speed, several 2D plots, a dashboard, or other items. The visualization may easily enable one to see where setpoint adjustment is possible in a heating, ventilation and air conditioning system to save energy while maintaining thermal comfort acceptable to occupants in the room, whether during a heating season or a cooling season.

Abstract: A tool for providing a visualization of a system may reveal an interactive navigation environment for building performance observation and assessment. The tool may be associated with a processor. The environment may incorporate a treemap, a graph pane, a treemap filter, a graph pane selector, a selected units box and a date/time control mechanism. A visualization of the environment, among other things, may be presented on a display. The treemap may exhibit a building geometry and/or equipment units hierarchically, along with some data information. Units may be interactively selected from the treemap and placed in the box for analysis. The graph pane may show a configuration and display of unit analysis. Selection of detailed views for units in the box may be provided by the graph pane selector. Date and time intervals for analysis may be selected by the control mechanism.

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: An approach for monetizing performance of building system equipment such as HVAC equipment. Expected and actual performance curves may be obtained for the HVAC equipment. Differences between the curves may indicate energy consumption. The energy consumption may be monetized. The monetizing may be of degradation that occurs when the equipment deteriorates, incurs a fault or has a loss of performance as accrued over time. Monetizing may incorporate maintenance and capital risk exposure. The monetizing may be a conversion of analyses of the equipment to money in real time. Performance monitoring of the equipment may incorporate predictive trending which may lead to fault prognosis and preventative maintenance. Automation of the conversion may result in immediate information and feedback to customers. The information may be stored for historical purposes and future analyses.

Abstract: A property of a signal may be detected by sampling a signal and determining a scalar indicator of the samples. The samples can be transformed to create a transformed signal. A scalar indicator of the transformed signal may be determined and compared to the scalar indicator of the sample. If the comparison yields a number greater than a selected threshold value then the signal that was sampled may be deemed to include the property. A property indicator signal may be driven low or high to indicate that the signal has the property. The time and duration of time that the signal is deemed to include the property may be recorded. The recorded data may be reviewed and appropriate action may be taken.

Abstract: An approach for providing setpoint optimization for air handling units. An area for such optimization may incorporate choosing a thermal comfort level index, finding a zone air properties target area and computing a supply air properties target area. Beyond the present approach, an outdoor air portion may be optimized, a feasible psychrometric path found, supply airflow versus temperature optimized, and a controller run to meet setpoints.

Abstract: A system that transforms information into fuzzy observable states. These states may be matched against a mapping table which indicates which observable state admits or excludes particular faults. This information may be processed over time when in each time instant the admitted or excluded faults are used for updating the rate for each fault.

Abstract: A virtual sub-metering process using combined classifiers includes generating an electric power consumption signature database by receiving data from an electric power consumption measuring meter and auxiliary data from a building management system, and clustering the data. After the generation of the electric power consumption signature database, additional data from the electric power consumption measuring meter and the auxiliary data from the building management system is received, and this additional data is processed to generate a second steady-state load classification component. A second transient state component is extracted from the additional data.

Abstract: An approach for visualization of time series data. The approach for conveying time-series data may be a “heatmap timeline”. Rather than use a spatial dimension indicate a datum value for each timestamp, the heatmap timeline may employ hue, saturation, or value of color, and/or pattern and/or shading, perhaps shown within a geometric shape, to indicate the datum value along a timeline. Data values may be aggregated into one value indication. A tooltip may be pointed to a specific place on of the heatmap timeline to obtain a precise datum value at that place. More than one heatmap timeline may be on a display. Traditional line plots synchronized to the timeline may be added to the same display for comparison purposes. The heatmap timelines of various items within a hierarchical structure may be presented on a display. Data may also be presented in a mosaic fashion.

Abstract: An approach for monetizing performance of building system equipment such as HVAC equipment. Expected and actual performance curves may be obtained for the HVAC equipment. Differences between the curves may indicate energy consumption. The energy consumption may be monetized. The monetizing may be of degradation that occurs when the equipment deteriorates, incurs a fault or has a loss of performance as accrued over time. Monetizing may incorporate maintenance and capital risk exposure. The monetizing may be a conversion of analyses of the equipment to money in real time. Performance monitoring of the equipment may incorporate predictive trending which may lead to fault prognosis and preventative maintenance. Automation of the conversion may result in immediate information and feedback to customers. The information may be stored for historical purposes and future analyses.