Conference Paper

User Behavior Modeling for Estimating Residential Energy Consumption

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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.

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... Earlier researchers recognized that understanding human behaviors is an important task to accomplish such goals. For example, diverse techniques exploited human activities and contexts as key control knobs of various system managements including mobile systems [9] and smart homes [1]. ...
... For example, prior research has shown that understanding users behavior and exploiting the behavioral characteristics can be used to improve system efficiency. In this context, earlier work proposed diverse system optimization techniques by identifying user behaviors and interactions for mobile systems [9] and smart homes [1]. Prior work often utilized ML techniques to identify the activities, while relying on computing capability of clouds through offloading, e.g., [18]. ...
... The results are reported for the non-binarized models, since the overhead of the model binarization is negligible. 1 In this comparison, the HD modeling and the neural network training were both executed on x86 processor. The results show that the proposed method presents higher performance efficiency as compared to the neural network training. ...
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... However, there are also models that are not based on clustering techniques. For example, in [17] the authors establish a framework that is able to establish profiles of energy demand in residential areas by means of a mathematical model that details the relation- ship between human activity and energy consumption. In addition, use an autoregressive moving average model (ARMA) to detect malicious consumption patterns due to electrical intrusions [18]. ...
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