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SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 348-361 | Cite as

User Behavior Modeling for Estimating Residential Energy Consumption

  • Baris Aksanli
  • Alper Sinan Akyurek
  • Tajana Simunic Rosing
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)

Abstract

Residential energy constitutes a significant portion of the total US energy consumption. Several researchers proposed energy-aware solutions for houses, promising significant energy and cost savings. However, it is important to evaluate the outcomes of these methods on larger scale, with hundreds of houses. This paper presents a human-activity based residential energy modeling framework, that can create power demand profiles considering the characteristics of household members. It constructs a mathematical model to show the detailed relationships between human activities and house power consumption. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends seen in real data. We also show a case study that evaluates voltage deviation in a neighborhood, which requires accurate estimation of the trends in power consumption.

Keywords

Residential energy Modeling Appliance User activity 

Notes

Acknowledgment

This work was supported in part by TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Baris Aksanli
    • 1
  • Alper Sinan Akyurek
    • 1
  • Tajana Simunic Rosing
    • 1
  1. 1.University of California San DiegoLa JollaUSA

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