Mining Appliance Usage Patterns in Smart Home Environment

Chen, Yi Cheng; Ko, Yu Lun; Peng, Wen Chih; Lee, Wang Chien

doi:10.1007/978-3-642-37453-1_9

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…An anomaly score of an appliance is defined as (2). An appliance which its anomaly score is high and between the Gumbel value of f(x) can be viewed as anomaly.…”

Section: C) Weibull and Gumbel Distributionsmentioning

confidence: 99%

“…Abnormal usage detection can help residents not only reducing electricity consumption, but also having benefit for the environment. However, previous researches [1,2] have been focused on analysis of the usage behavior on single device and neglect of the appliance correlations. Actually, the correlation among the usage of some appliances can provide valuable information to assist residents better detect abnormal usage of their appliances.…”

Section: Introductionmentioning

confidence: 99%

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Methods for Abnormal Usage Detection in Developing Intelligent Systems for Smart Homes

Minh-Thai

Thai-Nghe

2015

2015 Seventh International Conference on Knowledge and Systems Engineering (KSE)

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Detecting abnormal usages in intelligent systems, especially in smart home systems, is an important task. By exploring log data, useful information and patterns can be discovered which may help users/organizations to better understand the usage of their appliances and to distinguish unnecessary usages as well as abnormal problems which can cause waste, damages, or even fire. This work proposes several methods which can be used to detect the abnormal usages of appliances in smart home system and proposes a parameter tuning strategy to optimize the model's accuracy. The proposed methods are validated by using a real dataset. Experimental results show that these approaches perform nicely and could be applied for practice.

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“…An anomaly score of an appliance is defined as (2). An appliance which its anomaly score is high and between the Gumbel value of f(x) can be viewed as anomaly.…”

Section: C) Weibull and Gumbel Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methods for Abnormal Usage Detection in Developing Intelligent Systems for Smart Homes

Minh-Thai

Thai-Nghe

2015

2015 Seventh International Conference on Knowledge and Systems Engineering (KSE)

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“…By solving the two tasks, we could analyze the appliance usage patterns in each ADL within a household to decide appliance priority; we also could compare the appliance usage patterns of different households to detect abnormal appliance usages. Chen et al [3] mine representative appliance usage patterns based on the time of day from appliance power consumption patterns in a household. However, appliance usage patterns depend on ADLs directly, not the time of day.…”

Section: Intoductionmentioning

confidence: 99%

Learning a context-aware personal model of appliance usage patterns in smart home

Zhang

Kato

Matsuyama

2014

2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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To conserve electricity, we have been proposing to control the power consumption of home electrical appliances according to their priority. In a household, appliances are used in performing activities of daily life (ADLs), e.g. cooking, bathing, sleeping, etc. It is assumed that the priority of an appliance changes dynamically according to its usage context of ADLs in a household; it also varies depending on the personal appliance usage habits of each household. In this paper, towards evaluating appliance priority, we learn a personal model of appliance usage patterns, which is aware of the context of ADLs, from appliance power consumption patterns.

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“…Beyond the grid, sustainable energy usage is an ever-present concern in today's energy market. Several solutions have been proposed including energy efficient appliances and smart appliances, which learn usage patterns through machine learning, and automatically switch themselves on or off [37,38].…”

Section: Use-case 5: Smart Energy and Gridsmentioning

confidence: 99%

The Fourth Industrial Revolution: A Technological Wave of Change

Ajayi¹,

Bagula²,

Maluleke³

2023

Artificial Intelligence

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This chapter focuses on the technological wave of change called the fourth industrial revolution (4IR), which is also known as the information age or industry 4.0. It starts off with a brief history of the concept, describing the evolution through the ages, from the age of industrialization to the current technological age. The chapter then presents industry 4.0 through three lenses, which are i) the key enabling technologies that serve as its foundational pillars, such as the Internet and Cloud Computing; ii) technologies and concepts that emanate from 4IR, as well as their applications, which are discussed using use-cases; iii) the impacts of industry 4.0 on the wider society (both positive and negative). Finally, the chapter closes with a discussion on some open challenges that need to be considered in future research works to enhance the widespread adaptation and/or implementation of industry 4.0.

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Mining Appliance Usage Patterns in Smart Home Environment

Cited by 19 publications

References 4 publications

Methods for Abnormal Usage Detection in Developing Intelligent Systems for Smart Homes

Methods for Abnormal Usage Detection in Developing Intelligent Systems for Smart Homes

Learning a context-aware personal model of appliance usage patterns in smart home

The Fourth Industrial Revolution: A Technological Wave of Change

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