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Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018 Energies  
To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting.  ...  These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations  ...  The work presented by [17, 18] mine sequential patterns to understand appliance usage patterns to save energy.  ... 
doi:10.3390/en11020452 fatcat:m3zhtmxgajcoxkyopartzpcglq

A survey on health prediction using human activity patterns through smart devices

G. Akhila, N. Madhubhavana, N. V. Ramareddy, M. Hurshitha, N. Ravinder
2017 International Journal of Engineering & Technology  
In this research, the work mainly focuses on analyzing the human activity patterns for health prediction through smart devices.  ...  This paper represents the survey depends on the needs of analyzing energy utilization patterns of the appliance level, which completely depends on the human activity patterns.  ...  be used at which hour. [7] Paper on prediction system for home appliance usage (Sept 2013).  ... 
doi:10.14419/ijet.v7i1.1.9472 fatcat:e4rukelm7ngv5mybxsuh5lseti

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

Sarah Osama, Marco Alfonse, Abdel-Badeeh M. Salem
2019 Big Data and Cognitive Computing  
The main contribution of this paper is to discover the association between appliances' usage through mining temporal association rules in addition to applying the temporal clustering technique for grouping  ...  that have similar usage patterns with respect to the 24 h of the day.  ...  In [11] , the authors used the Sequential PAttern Discovery using Equivalence classes (SPADE) algorithm to extract appliances' sequential patterns and then introduced its results to a proposed prediction  ... 
doi:10.3390/bdcc3020020 fatcat:ilcocww6zrhurnpwlorynfviva

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

2021 KSII Transactions on Internet and Information Systems  
Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy.  ...  In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented  ...  The source information for regular pattern mining and cluster analysis comprises all appliances reported active during this 30-minute time period.  ... 
doi:10.3837/tiis.2021.06.001 fatcat:ntcm7kwilrhchkcow6bamgp7zm

Using Clustering Analysis and Association Rule Technology in Cross-Marketing

Yang Cheng, Ming Cheng, Tao Pang, Sizhen Liu
2021 Complexity  
pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied.  ...  are generated directly from the mined sequence patterns, so as to reduce the construction of the database.  ...  If a .sup ≥ min sup is satisfied, then a is called the sequential pattern. Sequential pattern mining is to find out all the sequential patterns in DB.  ... 
doi:10.1155/2021/9979874 doaj:2c0b001f7a37479ba7c29a5bc3adebe1 fatcat:u7lga4abdjf6ffmhn6r7t4cwia

Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

Krzysztof Gajowniczek, Tomasz Ząbkowski
2015 Energies  
appliances and the time of their usage.  ...  The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home  ...  Acknowledgments This research was financed by VEDIA Inc. leading a project partially supported by National Centre for Research and Development in Poland (NCBiR).  ... 
doi:10.3390/en8077407 fatcat:6elny54y7jf4dh6r7zti42jav4

Evaluation of Wellness Detection Techniques using Complex Activities Association for Smart Home Ambient

Farhan Sabir, Azhar Mahmood, Shaheen Khatoon, Muhammad Imran, Umair Abdullah
2016 International Journal of Advanced Computer Science and Applications  
In this paper, an association rules based model is proposed for the simple and complex (overlapped) activities recognition and comprehensive wellness detection mechanism after analyzing existing techniques  ...  Learning component is an important module of our proposed model to accommodate the changing trends in the frequent pattern behavior of an elderly person and recommend a caregiver/expert to adjust the expert  ...  Suryadevara [8] , proposed another probabilistic model is for the prediction wellness in the smart home environment based on appliance usage. Two wellness functions been used.  ... 
doi:10.14569/ijacsa.2016.070833 fatcat:5jdqu6y5j5e2nfqz32qgik2gku

A Proposed Framework for Reducing Electricity Consumption in Smart Homes using Big Data Analytics

Mira Tamer Shaker, Ayman E. Khedr, Sherif Kholeif
2019 Journal of Computer Science  
All connected appliances are Internet of Things (IoT) devices that support applications inside a smart home which produce an amount of data that shows what households are doing during their daily life.  ...  This research paper contains a review study for recent papers with different techniques that discusses BDA challenges and benefits of a smart home and its relationship with IoT.  ...  Prefix Span Prefix Projected Sequential Pattern Growth: PrefixSpan mines the complete set of The database will keep shrinking.  ... 
doi:10.3844/jcssp.2019.537.549 fatcat:3d4s7j6kmjayjijo23pdm5dema

Smart Home based Big Data Analysis for Healthcare Applications

2020 International journal of recent technology and engineering  
In this work, a model has been anticipated to utilize smart home big data analysis as a discovering and learning human activity patterns for huge health care applications.  ...  Subsequently, places all around the world are spending in digital evolution in an attempt to offer healthy eco-system for huge people.  ...  mining pattern.  ... 
doi:10.35940/ijrte.a1406.059120 fatcat:qj77t5nevrhyvi35rxgwybhkvu

Using consumer behavior data to reduce energy consumption in smart homes [article]

Daniel Schweizer, Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez
2015 arXiv   pre-print
We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data.  ...  This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings.  ...  through sequential pattern mining.  ... 
arXiv:1510.00165v1 fatcat:bey35avk3fbuji5ar2hdcivljm

Forecasting the behavior of an elderly using wireless sensors data in a smart home

N.K. Suryadevara, S.C. Mukhopadhyay, R. Wang, R.K. Rayudu
2013 Engineering applications of artificial intelligence  
The developed prototype is used to forecast the behavior and wellness of the elderly by monitoring the daily usages of appliances in a smart home.  ...  A framework integrating temporal and spatial contextual information for determining the wellness of an elderly has been modeled.  ...  (e) Chair usage (10th week forecast pattern). (f) Chair usage (9th week forecast pattern). Fig. 10 . 10 Block diagram of Time Series Data Mining (TSDM).  ... 
doi:10.1016/j.engappai.2013.08.004 fatcat:rzch7agggnhsxk4lrz55bm276m

Predicting Energy Measurements of Service-Enabled Devices in the Future Smartgrid

Domnic Savio, Lubomir Karlik, Stamatis Karnouskos
2010 2010 12th International Conference on Computer Modelling and Simulation  
We present some methodologies for mining data gathered from devices in the energy domain i.e. web service enabled smart meters and home appliances.  ...  We present here an approach that realise short-term prediction based on neural networks or support vector machines.  ...  Efficient algorithms for mining periodic patterns from a sequence of events were studied [12] .  ... 
doi:10.1109/uksim.2010.89 dblp:conf/uksim/SavioKK10 fatcat:a4aoxywuzjgb7a354lmmidljia


2020 Issues in Information Systems  
usage patterns of the electrical appliances.  ...  The results show that the pre-trained feature extractors for people's electricity consumption patterns can help infer their privacies, which in turn proves that people's private traits will affect their  ...  Table 2 shows that the features of the household's high-power appliances usage patterns can be captured even from the incomplete low-frequency records of the main grid load.  ... 
doi:10.48009/3_iis_2020_41-52 fatcat:q3azpwdodrg6xgvle36rk4hh6q

The user side of sustainability: Modeling behavior and energy usage in the home

Chao Chen, Diane J. Cook, Aaron S. Crandall
2013 Pervasive and Mobile Computing  
It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data.  ...  Next, it describes a method to correlate homebased activities with electricity usage.  ...  human behaviors in an individual building as primary features for predicting energy usage.  ... 
doi:10.1016/j.pmcj.2012.10.004 fatcat:2whavevlbjbhrngvdoiubgrdo4

Electricity forecasting on the individual household level enhanced based on activity patterns

Krzysztof Gajowniczek, Tomasz Ząbkowski, Xiaosong Hu
2017 PLoS ONE  
and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.  ...  The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model.  ...  Acknowledgments The computations were performed partially employing the computational resources of the Interdisciplinary Centre for Mathematical and Computational Modelling at the Warsaw University (Computational  ... 
doi:10.1371/journal.pone.0174098 pmid:28423039 pmcid:PMC5396872 fatcat:gur4vgrqxrd2joicv64cx5wqzi
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