User Activity Recognition for Energy Saving in Smart Homes

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30 Citazioni (Scopus)

Abstract

Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall consumption within a predefined range, thus minimizing the impact on the end users. In order to build the models, the inherent recursive structure of user activities is abstracted from raw sensor readings, via an approach based on information theory. Experimental assessment based on publicly available datasets and synthesized consumption models is provided to show the effectiveness of our proposal.
Lingua originaleEnglish
pagine (da-a)-
Numero di pagine38
RivistaPervasive and Mobile Computing
Volume16
Stato di pubblicazionePublished - 2015

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Activity Recognition
Smart Home
Energy Saving
Energy conservation
Energy
Information theory
Human engineering
Human Factors
Structural Model
Energy utilization
Information Theory
Proximity
Energy Consumption
Sensors
Sensor
Optimization
Prediction
Model
Range of data
Demand

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Applied Mathematics

Cita questo

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title = "User Activity Recognition for Energy Saving in Smart Homes",
abstract = "Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall consumption within a predefined range, thus minimizing the impact on the end users. In order to build the models, the inherent recursive structure of user activities is abstracted from raw sensor readings, via an approach based on information theory. Experimental assessment based on publicly available datasets and synthesized consumption models is provided to show the effectiveness of our proposal.",
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T1 - User Activity Recognition for Energy Saving in Smart Homes

AU - Gaglio, Salvatore

AU - Lo Re, Giuseppe

AU - Ortolani, Marco

AU - Cottone, Pietro

PY - 2015

Y1 - 2015

N2 - Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall consumption within a predefined range, thus minimizing the impact on the end users. In order to build the models, the inherent recursive structure of user activities is abstracted from raw sensor readings, via an approach based on information theory. Experimental assessment based on publicly available datasets and synthesized consumption models is provided to show the effectiveness of our proposal.

AB - Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall consumption within a predefined range, thus minimizing the impact on the end users. In order to build the models, the inherent recursive structure of user activities is abstracted from raw sensor readings, via an approach based on information theory. Experimental assessment based on publicly available datasets and synthesized consumption models is provided to show the effectiveness of our proposal.

KW - Activity discovery; Peak load avoidance; Structural modeling

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JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

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