The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Rahul Balani - One of the best experts on this subject based on the ideXlab platform.

  • BuildSys@SenSys - Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Rahul Balani, Han Zhao, Mani Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design systematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable information unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants' various activities. Understanding the causal relationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of individual power outlets and/or extensive user studies that either increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy monitoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relationship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users' PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illustrate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occupants in a lab environment.

  • Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Younghun Kim, Rahul Balani, Han Zhao, Mani B. Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design sys- tematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable informa- tion unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants’ various activities. Understanding the causal re- lationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of in- dividual power outlets and/or extensive user studies that ei- ther increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy mon- itoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relation- ship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users’ PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illus- trate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occu- pants in a lab environment.

Mani Srivastava - One of the best experts on this subject based on the ideXlab platform.

  • BuildSys@SenSys - Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Rahul Balani, Han Zhao, Mani Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design systematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable information unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants' various activities. Understanding the causal relationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of individual power outlets and/or extensive user studies that either increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy monitoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relationship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users' PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illustrate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occupants in a lab environment.

Mani B. Srivastava - One of the best experts on this subject based on the ideXlab platform.

  • Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Younghun Kim, Rahul Balani, Han Zhao, Mani B. Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design sys- tematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable informa- tion unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants’ various activities. Understanding the causal re- lationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of in- dividual power outlets and/or extensive user studies that ei- ther increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy mon- itoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relation- ship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users’ PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illus- trate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occu- pants in a lab environment.

Han Zhao - One of the best experts on this subject based on the ideXlab platform.

  • BuildSys@SenSys - Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Rahul Balani, Han Zhao, Mani Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design systematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable information unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants' various activities. Understanding the causal relationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of individual power outlets and/or extensive user studies that either increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy monitoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relationship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users' PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illustrate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occupants in a lab environment.

  • Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Younghun Kim, Rahul Balani, Han Zhao, Mani B. Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design sys- tematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable informa- tion unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants’ various activities. Understanding the causal re- lationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of in- dividual power outlets and/or extensive user studies that ei- ther increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy mon- itoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relation- ship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users’ PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illus- trate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occu- pants in a lab environment.

Younghun Kim - One of the best experts on this subject based on the ideXlab platform.

  • Granger causality analysis on IP traffic and circuit-level energy monitoring
    Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, 2010
    Co-Authors: Younghun Kim, Rahul Balani, Han Zhao, Mani B. Srivastava
    Abstract:

    Device-level energy monitoring has been increasingly proposed to understand inefficient energy use and design sys- tematic processes for efficient building operation. Its sole use, however, is not sufficient to provide actionable informa- tion unless we understand the causes and context of energy use. Fundamentally, energy consumption in a building is due to occupants’ various activities. Understanding the causal re- lationship between occupants and their energy use is thus the key to an efficient building operation. This usually involves fine-grained sensing through intensive instrumentation of in- dividual power outlets and/or extensive user studies that ei- ther increase the system cost or become too intrusive. Instead, we advocate that circuit branch level energy mon- itoring combined with statistical Granger causality analysis is adequate to automatically understand the causal relation- ship. We monitor energy consumption of various zones in an office using a circuit level power monitor. IP traffic from users’ PCs, obtained from a Local Firewall, is used to relate occupants with their energy use in each micro zone. The output is expressed in the form of causality graphs that illus- trate how each individual influences energy use in different zones. We discuss the effectiveness and limitations of this causal analysis in capturing energy use patterns of the occu- pants in a lab environment.