The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Elisabeth André - One of the best experts on this subject based on the ideXlab platform.

  • Visualization Support for Comparing Energy Consumption Data
    2015 19th International Conference on Information Visualisation, 2015
    Co-Authors: Masood Masoodian, René Bühling, Birgit Lugrin, Elisabeth André
    Abstract:

    Providing effective feedback can empower users to change their behaviour and take the necessary actions to reduce their Energy Consumption. The types of feedback that allow comparison of Energy usage seem to be particularly valuable. This paper introduces the time-stack visualization, which has been designed to support comparisons of individual and collective Energy usage Data. It also describes a user study conducted to compare the effectiveness of time-stack against a similar visualization called time-pie. The results show that although the two visualizations are generally comparable in their effectiveness, users rate time-stack more favourably.

  • Time-Pie visualization: Providing Contextual Information for Energy Consumption Data
    2013 17th International Conference on Information Visualisation, 2013
    Co-Authors: Masood Masoodian, Birgit Endrass, René Bühling, Pavel Ermolin, Elisabeth André
    Abstract:

    In recent years a growing number of information visualization systems have been developed to assist users with monitoring their Energy Consumption, with the hope of reducing Energy use through more effective user-awareness. Most of these visualizations can be categorized into either some form of a time-series or pie chart, each with their own limitations. These visualization systems also often ignore incorporating contextual (e.g. weather, environmental) information which could assist users with better interpretation of their Energy use information. In this paper we introduce the time-pie visualization technique, which combines the concepts of timeseries and pie charts, and allows the addition of contextual information to Energy Consumption Data.

Masood Masoodian - One of the best experts on this subject based on the ideXlab platform.

  • Visualization Support for Comparing Energy Consumption Data
    2015 19th International Conference on Information Visualisation, 2015
    Co-Authors: Masood Masoodian, René Bühling, Birgit Lugrin, Elisabeth André
    Abstract:

    Providing effective feedback can empower users to change their behaviour and take the necessary actions to reduce their Energy Consumption. The types of feedback that allow comparison of Energy usage seem to be particularly valuable. This paper introduces the time-stack visualization, which has been designed to support comparisons of individual and collective Energy usage Data. It also describes a user study conducted to compare the effectiveness of time-stack against a similar visualization called time-pie. The results show that although the two visualizations are generally comparable in their effectiveness, users rate time-stack more favourably.

  • Time-Pie visualization: Providing Contextual Information for Energy Consumption Data
    2013 17th International Conference on Information Visualisation, 2013
    Co-Authors: Masood Masoodian, Birgit Endrass, René Bühling, Pavel Ermolin, Elisabeth André
    Abstract:

    In recent years a growing number of information visualization systems have been developed to assist users with monitoring their Energy Consumption, with the hope of reducing Energy use through more effective user-awareness. Most of these visualizations can be categorized into either some form of a time-series or pie chart, each with their own limitations. These visualization systems also often ignore incorporating contextual (e.g. weather, environmental) information which could assist users with better interpretation of their Energy use information. In this paper we introduce the time-pie visualization technique, which combines the concepts of timeseries and pie charts, and allows the addition of contextual information to Energy Consumption Data.

Zhaohui Tang - One of the best experts on this subject based on the ideXlab platform.

  • Privacy-preserving spatial and temporal aggregation of smart Energy Data
    2016
    Co-Authors: Sye Loong Keoh, Zhaohui Tang
    Abstract:

    Smart grid provides fine-grained real time Energy Consumption, and it is able to improve the efficiency of Energy management. It enables the collection of Energy Consumption Data from consumer and hence has raised serious privacy concerns. Energy Consumption Data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of Energy Consumption Data by enabling the utility to retrieve the aggregated spatial and temporal Consumption without revealing individual Energy Consumption. We use a lightweight cryptographic mechanism to mask the Energy Consumption Data by adding random noises to each Energy reading and use Paillier’s additive homomorphic encryption to protect the noises. When summing up the masked Energy Consumption Data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of Energy consumed in a neighbourhood at a particular time, or the total sum of Energy consumed by a household in a day. No third party is able to derive the Energy Consumption pattern of a household in real time. A proof-of-concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform.

  • IAS - A lightweight privacy-preserved spatial and temporal aggregation of Energy Data
    2015 11th International Conference on Information Assurance and Security (IAS), 2015
    Co-Authors: Sye Loong Keoh, Zhaohui Tang
    Abstract:

    Smart grid provides fine-grained real time Energy Consumption, and it is able to improve the efficiency of Energy management. It enables the collection of Energy Consumption Data from consumer and hence has raised serious privacy concerns. Energy Consumption Data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer's household or appliances usage habit. This paper proposes to enhance the privacy of Energy Consumption Data by enabling the utility to retrieve the aggregated spatial and temporal Consumption without revealing individual Energy Consumption. We use a lightweight cryptographic mechanism to mask the Energy Consumption Data by adding random noises to each Energy reading and use Paillier's additive homomorphic encryption to protect the noises. When summing up the masked Energy Consumption Data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of Energy consumed in a neighbourhood at a particular time, or the total sum of Energy consumed by a household in a day. No third party is able to derive the Energy Consumption pattern of a household in real time. A proof-of-concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform.

René Bühling - One of the best experts on this subject based on the ideXlab platform.

  • Visualization Support for Comparing Energy Consumption Data
    2015 19th International Conference on Information Visualisation, 2015
    Co-Authors: Masood Masoodian, René Bühling, Birgit Lugrin, Elisabeth André
    Abstract:

    Providing effective feedback can empower users to change their behaviour and take the necessary actions to reduce their Energy Consumption. The types of feedback that allow comparison of Energy usage seem to be particularly valuable. This paper introduces the time-stack visualization, which has been designed to support comparisons of individual and collective Energy usage Data. It also describes a user study conducted to compare the effectiveness of time-stack against a similar visualization called time-pie. The results show that although the two visualizations are generally comparable in their effectiveness, users rate time-stack more favourably.

  • Time-Pie visualization: Providing Contextual Information for Energy Consumption Data
    2013 17th International Conference on Information Visualisation, 2013
    Co-Authors: Masood Masoodian, Birgit Endrass, René Bühling, Pavel Ermolin, Elisabeth André
    Abstract:

    In recent years a growing number of information visualization systems have been developed to assist users with monitoring their Energy Consumption, with the hope of reducing Energy use through more effective user-awareness. Most of these visualizations can be categorized into either some form of a time-series or pie chart, each with their own limitations. These visualization systems also often ignore incorporating contextual (e.g. weather, environmental) information which could assist users with better interpretation of their Energy use information. In this paper we introduce the time-pie visualization technique, which combines the concepts of timeseries and pie charts, and allows the addition of contextual information to Energy Consumption Data.

Yu Gu - One of the best experts on this subject based on the ideXlab platform.

  • BigData Conference - E-Sketch: Gathering large-scale Energy Consumption Data based on Consumption patterns
    2014 IEEE International Conference on Big Data (Big Data), 2014
    Co-Authors: Zhichuan Huang, David Skoda, Yu Gu
    Abstract:

    To reduce peak demand, many utility companies are transitioning from fixed rate pricing plans to real-time pricing plans. To apply real-time pricing plans, it is crucial to collect accurate real-time power Consumption readings from individual homes. Thus, utility companies are increasing the installation of smart meters in individual homes. Smart meters can record Energy related Data (e.g., power Consumption) every second. However, power Consumption Data with high time granularity needs huge Data storage space and generates significant communication overhead for utility companies to gather all the Data for the pricing plans. In this paper, we present E-Sketch, a middleware for utility companies to gather Data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power Consumption changes in time domain. Then frequency compression is applied to further compress the sampled Data. We conducted extensive system evaluations with 30 homes' second-level power Consumption Data for more than 2 months. Results indicate i) our design can reduce Data storage space significantly by 90% with more than 99% accuracy of second-level power Consumption on average for a single home, and ii) our design can achieve even more than 99.8% accuracy on average for aggregated power Consumption of 30 homes.

  • E-Sketch: Gathering large-scale Energy Consumption Data based on Consumption patterns
    2014 IEEE International Conference on Big Data (Big Data), 2014
    Co-Authors: Zhichuan Huang, David Skoda, Yu Gu
    Abstract:

    To reduce peak demand, many utility companies are transitioning from fixed rate pricing plans to real-time pricing plans. To apply real-time pricing plans, it is crucial to collect accurate real-time power Consumption readings from individual homes. Thus, utility companies are increasing the installation of smart meters in individual homes. Smart meters can record Energy related Data (e.g., power Consumption) every second. However, power Consumption Data with high time granularity needs huge Data storage space and generates significant communication overhead for utility companies to gather all the Data for the pricing plans. In this paper, we present E-Sketch, a middleware for utility companies to gather Data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power Consumption changes in time domain. Then frequency compression is applied to further compress the sampled Data. We conducted extensive system evaluations with 30 homes' second-level power Consumption Data for more than 2 months. Results indicate i) our design can reduce Data storage space significantly by 90% with more than 99% accuracy of second-level power Consumption on average for a single home, and ii) our design can achieve even more than 99.8% accuracy on average for aggregated power Consumption of 30 homes.