Abstract: This disclosure provides systems, method, and computer-readable media for operating a sensor network. The sensor network can include an array of sensor devices and gateways. The sensor devices can sense various conditions of an environment. The sensor devices can include temperature sensors, occupancy detectors, access sensors, motion sensors, tracking devices, etc. The sensor devices can also network and communicate with one another and the gateways in an ad hoc or mesh network. The environment can include different locations, such as warehouse, office building, a complex of buildings, cargo vehicles, containers or the like. The sensor network can be deployed for controlling power consumption, logistics, automated manufacturing, healthcare, intelligent buildings, and smart cities among many other implementations. The sensor devices can communicate sensed conditions and transmit data among each other without a predefined route.

Abstract: The present disclosure provides a method and system for predicting a potential future energy consumption of a plurality of energy loads in a built environment. The method includes a step of collecting a first set of statistical data associated with a plurality of energy consuming devices. The method includes another step of accumulating a second set of statistical data associated with each of a plurality of users present inside the built environment. The method includes yet another step of analyzing the first set of statistical data and the second set of statistical data. In addition, the method includes yet another step of predicting a set of predictions associated with the potential future energy consumption of each of the plurality of energy consuming devices.

Abstract: A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

Abstract: This disclosure provides systems, method, and computer-readable media for operating a sensor network. The sensor network can include an array of sensor devices and gateways. The sensor devices can sense various conditions of an environment. The sensor devices can include temperature sensors, occupancy detectors, access sensors, motion sensors, tracking devices, etc. The sensor devices can also network and communicate with one another and the gateways in an ad hoc or mesh network. The environment can include different locations, such as warehouse, office building, a complex of buildings, cargo vehicles, containers or the like. The sensor network can be deployed for controlling power consumption, logistics, automated manufacturing, healthcare, intelligent buildings, and smart cities among many other implementations. The sensor devices can communicate sensed conditions and transmit data among each other without a predefined route.

Abstract: A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

Abstract: The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

Abstract: The present disclosure provides a computer-implemented method for recommending one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes analysis of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes recommendation of one or more control schemes to a plurality of energy consuming devices and a plurality of energy storage and supply means.

Abstract: The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

Abstract: A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

Abstract: The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.

Abstract: A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

Abstract: A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

Abstract: This disclosure provides systems, method, and computer-readable media for operating a sensor network. The sensor network can include an array of sensor devices and gateways. The sensor devices can sense various conditions of an environment. The sensor devices can include temperature sensors, occupancy detectors, access sensors, motion sensors, tracking devices, etc. The sensor devices can also network and communicate with one another and the gateways in an ad hoc or mesh network. The environment can include different locations, such as warehouse, office building, a complex of buildings, cargo vehicles, containers or the like. The sensor network can be deployed for controlling power consumption, logistics, automated manufacturing, healthcare, intelligent buildings, and smart cities among many other implementations. The sensor devices can communicate sensed conditions and transmit data among each other without a predefined route.

Abstract: A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

Abstract: The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.

Abstract: The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

Abstract: A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

Abstract: The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

Abstract: The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.

Abstract: The present disclosure provides a computer-implemented method for recommending one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes analysis of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes recommendation of one or more control schemes to a plurality of energy consuming devices and a plurality of energy storage and supply means.