Extracting Human Behavior Patterns from Appliance-level Power Consumption Data

Alhamoud, Alaa; Xu, Pei; Englert, Frank; Reinhardt, Andreas; Scholl, Philipp M.; Boehnstedt, Doreen; Steinmetz, Ralf

doi:10.1007/978-3-319-15582-1_4

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Massive Online Analysis (MOA) is an open source framework for large data streams analysis, project that is the complement of WEKA for Big Data analysis. [3], [40], [44], [49], [64], [67], [84], [94], [129], [153], [160], [204], [212], [214], [245]), fuzzy c-means clustering (7) ( [49], [64], [173], [204], [245], [265], [266]), Hierarchical Clustering (HAC) (7) ( [44], [56], [64], [94], [204], [212], [232]), Support Vector Machine (SVM) (6) [3], [112], [150], [204], [239], [250], Self-Organising Map (SOM) (4) [2], [64], [167], [212], Multi Layer Perceptron (MLP) ANN (3) [40], [150], [232], t-means clustering [183], k-Nearest Neighbour (kNN) [112], [204], Random Forest…”

Section: Sms Resultsmentioning

confidence: 99%

“…SG failures deals with fault status detection, fault type classification, power distribution reliability. [3], [26], [40], [75], [84], [94], [112], [129], [160], [167], [173], [204], [212], [214], [232], [233], [239], [245]), power consumption pattern recognition ( [7], [44], [49], [64], [67], [131], [150], [153], [183], [265], [266]), power load forecasting ( [56], [250]), events/tasks extraction ( [50])…”

Section: B Rq2mentioning

confidence: 99%

“…Java (8) was also used in the data analysis, mostly it was used when there was the need to create and support a framework for data analysis that had broader scope than just data processing and algorithm scripts. Also WEKA (7) was reported for data analysis of Smart Grids data: the usage in this case, is mostly about the application of techniques for data mining that are provided by WEKA itself (like C4.5 (J48) decision trees), while other platforms are used when there are needs for more customized (in terms of algorithms) analysis. C++ ( 2) is limitedly adopted in this context, mostly when some hardware interfacing is needed.…”

Section: Rq4 Which Are the Reported Most Used Techniques In The Ident...mentioning

confidence: 99%

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Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Section: B Rq2mentioning

confidence: 99%

Section: Rq4 Which Are the Reported Most Used Techniques In The Ident...mentioning

confidence: 99%

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Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Third, cluster analysis was performed to group these occupancy presence conditions into 4 types. Alhamoud et al (2015) utilized two sets of data to conduct three experiments for energy-related occupant behavior pattern detection in a residential building. The datasets comprised of power and environmental data while 9 activities were defined on the basis of regular motions.…”

Section: Data Mining Through Electricity Monitoringmentioning

confidence: 99%

Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies

Jia¹,

Srinivasan²

2015

2015 Winter Simulation Conference (WSC)

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At the outset, there is no question that building energy use is largely influenced by the presence and behavior of occupants. Among other, the key to realize energy use reduction while still maintaining occupant comfort is to seamlessly integrate occupant behavior in energy simulation tools with capabilities that would optimally manage building energy systems. This paper provides an in-depth survey of occupant behavior modeling state-of-the-art technologies employed to gather relevant data and modeling methodologies to reduce energy use. Several novel technologies that have been utilized for data collection are discussed in this paper. For the purposes of this review paper, occupant behavior modeling has been organized based on their underlying methodologies namely, statistical analysis; agent -based models; data mining approaches; and stochastic techniques. After providing a thorough review of state-of-the-art research work in the field of occupant behavior modeling for smart, energy efficient buildings, this paper discusses potential areas of improvement.978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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“…The extraction of temporal relations between consecutive activities has been proven to improve the activity detection accuracy. Alhamoud et al investigate activity sequence patterns using the Apriori algorithm [11]. The algorithm scans the whole dataset to find all frequent activities and high dependency of two consecutive activities.…”

Section: Power Meter Applicationsmentioning

confidence: 99%

Office Occupancy Detection based on Power Meters and BLE Beaconing

Pratama¹

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To my beloved wife and son, Ascariena and Adnan Contents Acknowledgments xi Symbols xv ix Contents Samenvatting 115 Bibliography 119 x Pursuing a PhD degree in the Netherlands is an unforgettable and invaluable experience for me. I realize that this would not be possible without the assistance and support of many people to whom I wish to thank. First of all, I would like to express my deep gratitude to my promoters, Prof. Marco Aiello and Prof. Alexander Lazovik. Dear Marco, thank you for your kindest support and trust. I remember the first time I met you in March 2014 when you invited me to visit the Distributed Systems group, just after we had a Skype interview. You let me explore ideas without being pessimistic. Your expertise and experience guide me to keep on track amidst the ups and downs. Thank you very much for supervising me while understanding my psychological well-being. Dear Alexander, thank you for supervising me during my PhD trajectory. You teach me to think more critically and see things from other perspectives. We often have friendly discussions, even though sometimes you are very busy. I always feel relieved when I leave your office with new ideas or research planning approvals. I would like to respectfully express my gratitude to the reading committee, Prof. Bayu Jayawardhana, Prof. Boris Koldehofe, and Prof. Heinrich Wörtche, for spending their time to review and giving constructive advice for my thesis.

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Extracting Human Behavior Patterns from Appliance-level Power Consumption Data

Cited by 10 publications

References 12 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies

Office Occupancy Detection based on Power Meters and BLE Beaconing

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