Generic Data-Driven Approaches To Time Series Classification

PhD Thesis – Christophe Gisler: Generic Data-Driven Approaches To Time Series Classification Analysis and development of generic data-driven approaches to classify data recorded as time series.
Realization
  • HES-SO Fribourg
  • Christophe Gisler
  • Prof. Jean Hennebert
Keywords
  • Machine learning
  • Time series classification
  • Data-driven generic approaches
  • Appliance recognition
  • Glaucoma detection
Our skills
Generic approaches to time series classification problems, Machine learning, SVMs, MLPs, GMMs
Valorisation
12 international peer reviewed publications, 2 journal papers
Partners

Our Partners:

  • DIVA – UNIFR
  • Sensimed SA
Funding
CTI/KTI
HES-SO

UniFr CTI Sensimed
GislerPhDImageMain

Time series represent a large part of the data supply worldwide and many data mining tasks, such as prediction and classification, are concerned with them. Most machine learning algorithms do not work well on time series because of their unique data structure which requires particular approaches, using high level representations.

In this thesis, we have reported on our research in the field of generic approaches to time series classification. After a survey on the methodologies commonly used for mining time series data, we took two different case studies to present, apply and evaluate two generic approaches to multivariate time series classification: a global and a local one. The global approach consisted in extracting one global unique feature vector from each multivariate time series. The second approach consisted in dividing each time series in local segments, and extracting as many feature vectors as segments.

The first case study concerned the task of automatic identification of home appliances, based on their electric consumption signatures recorded with a low-end smart outlet sensor. We built two databases of appliance consumption signatures, ACS-F1 and ACS-F2, made freely available for the scientific community. The second case study concerned the task of automatic detection of the glaucoma disease by patients, based on their 24-hour intraocular pressure profiles acquired with a new type of contact lens sensor developed by the Swiss company Sensimed.

For each approach and classification problem, various generic features, some based on representations like Piecewise Aggregate Approximation and Symbolic Aggregate Approximation, were extracted, and three classification algorithms were tested and compared: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Gaussian Mixture Model (GMM). For each problem, each database and test protocol, global and local approaches provided rather similar results. Through systematic tests, we have demonstrated that the global approach provides a lighter representation of the data, and a more efficient handling by the classifiers, in terms of computation time and scalability.

At last, we presented the Generic Time Series Classification Tool elaborated during the case studies. This easy-to-use JavaFX application respects the data mining process and provides functionalities to import, visualize, annotate, select, represent, and classify time series using generic approaches. It was successfully used to perform all our experiments, and show that our generic approaches to time series could be applied to different classification problems independently. Hence, a main contribution of this thesis was the proposition of a generic methodology for time series classification. This methodology involves a systematic and semi-automated process to discover, thanks to the developed tool, the best configuration of extracted features and classifier parameters.

Publications

  • A. Ridi, C. Gisler, and J. Hennebert, “Aggregation procedure of Gaussian Mixture Models for additive features,” in 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 2545-2550.
    [Bibtex]
    @conference{ridi:icpr2016,
    author = "Antonio Ridi and Christophe Gisler and Jean Hennebert",
    abstract = "In this work we provide details on a new and effective approach able to generate Gaussian Mixture Models (GMMs) for the classification of aggregated time series. More specifically, our procedure can be applied to time series that are aggregated together by adding their features. The procedure takes advantage of the additive property of the Gaussians that complies with the additive property of the features. Our goal is to classify aggregated time series, i.e. we aim to identify the classes of the single time series contributing to the total. The standard approach consists in training the models using the combination of several time series coming from different classes. However, this has the drawback of being a very slow operation given the amount of data. The proposed approach, called GMMs aggregation procedure, addresses this problem. It consists of three steps: (i) modeling the independent classes, (ii) generation of the models for the class combinations and (iii) simplification of the generated models. We show the effectiveness of our approach by using time series in the context of electrical appliance consumption, where the time series are aggregated by adding the active and reactive power. Finally, we compare the proposed approach with the standard procedure.",
    booktitle = "23rd International Conference on Pattern Recognition (ICPR)",
    editor = "IEEE",
    keywords = "machine learning, electric signal, appliance signatures, GMMs",
    month = "December",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    pages = "2545-2550",
    title = "{A}ggregation procedure of {G}aussian {M}ixture {M}odels for additive features",
    url = "http://www.hennebert.org/download/publications/icpr-2016-aggregation-procedure-of-Gaussian-mixture-models-for-additive-features.pdf",
    year = "2016",
    }
  • C. Gisler, A. Ridi, J. Hennebert, R. N. Weinreb, and K. Mansouri, “Automated Detection and Quantification of Circadian Eye Blinks Using a Contact Lens Sensor,” Translational Vision Science and Technology (TVST), vol. 4, iss. 1, pp. 1-10, 2015.
    [Website] [PDF] [Bibtex]
    @article{gisler2015automated,
    Abstract = {Purpose: To detect and quantify eye blinks during 24-hour intraocular pressure (IOP) monitoring with a contact lens sensor (CLS). Methods: A total of 249 recordings of 24-hour IOP patterns from 202 participants using a CLS were included. Software was developed to automatically detect eye blinks, and wake and sleep periods. The blink detection method was based on detection of CLS signal peaks greater than a threshold proportional to the signal amplitude. Three methods for automated detection of the sleep and wake periods were evaluated. These relied on blink detection and subsequent comparison of the local signal amplitude with a threshold proportional to the mean signal amplitude. These methods were compared to manual sleep/wake verification. In a pilot, simultaneous video recording of 10 subjects was performed to compare the software to observer-measured blink rates. Results: Mean (SD) age of participants was 57.4 $\pm$ 16.5 years (males, 49.5%). There was excellent agreement between software-detected number of blinks and visually measured blinks for both observers (intraclass correlation coefficient [ICC], 0.97 for observer 1; ICC, 0.98 for observer 2). The CLS measured a mean blink frequency of 29.8 $\pm$ 15.4 blinks/min, a blink duration of 0.26 $\pm$ 0.21 seconds and an interblink interval of 1.91 $\pm$ 2.03 seconds. The best method for identifying sleep periods had an accuracy of 95.2 $\pm$ 0.5%. Conclusions: Automated analysis of CLS 24-hour IOP recordings can accurately quantify eye blinks, and identify sleep and wake periods. Translational Relevance: This study sheds new light on the potential importance of eye blinks in glaucoma and may contribute to improved understanding of circadian IOP characteristics.},
    Author = {Christophe Gisler and Antonio Ridi and Jean Hennebert and Robert N Weinreb and Kaweh Mansouri},
    Doi = {10.1167/tvst.4.1.4},
    Journal = {Translational Vision Science and Technology (TVST)},
    Keywords = {machine learning, bio-medical signals, glaucoma prediction},
    Month = {January},
    Note = {Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.},
    Number = {1},
    Pages = {1-10},
    Publisher = {The Association for Research in Vision and Ophthalmology},
    Title = {{A}utomated {D}etection and {Q}uantification of {C}ircadian {E}ye {B}links {U}sing a {C}ontact {L}ens {S}ensor},
    Pdf = {http://www.hennebert.org/download/publications/TVST-2015-Automated-Detection-and-Quantification-of-Circadian-Eye-Blinks-Using-a-Contact-Lens-Sensor.pdf},
    Volume = {4},
    Year = {2015},
    Pdf = {http://www.hennebert.org/download/publications/TVST-2015-Automated-Detection-and-Quantification-of-Circadian-Eye-Blinks-Using-a-Contact-Lens-Sensor.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1167/tvst.4.1.4}}
  • A. Ridi, N. Zarkadis, C. Gisler, and J. Hennebert, “Duration Models for Activity Recognition and Prediction in Buildings using Hidden Markov Models,” in Proceedings of the 2015 International Conference on Data Science and Advanced Analytics (DSAA 2015), Paris, France, 2015, p. 10.
    [Bibtex]
    @inproceedings{RidiDSAA2015,
    abstract = {Activity recognition and prediction in buildings can have multiple positive effects in buildings: improve elderly monitoring, detect intrusions, maximize energy savings and optimize occupant comfort. In this paper we apply human activity recognition by using data coming from a network of motion and door sensors distributed in a Smart Home environment. We use Hidden Markov Models (HMM) as the basis of a machine learning algorithm on data collected over an 8-month period from a single-occupant home available as part of the WSU CASAS Smart Home project. In the first implementation the HMM models 24 hours of activities and classifies them in 8 distinct activity categories with an accuracy rate of 84.6{\%}. To improve the identification rate and to help detect potential abnormalities related with the duration of an activity (i.e. when certain activities last too much), we implement minimum duration modeling where the algorithm is forced to remain in a certain state for a specific amount of time. Two subsequent implementations of the minimum duration HMM (mean-based length modeling and quantile length modeling) yield a further 2{\%} improvement of the identification rate. To predict the sequence of activities in the future, Artificial Neural Networks (ANN) are employed and identified activities clustered in 3 principal activity groups with an average accuracy rate of 71-77.5{\%}, depending on the forecasting window. To explore the energy savings potential, we apply thermal dynamic simulations on buildings in central European climate for a period of 65 days during the winter and we obtain energy savings for space heating of up to 17{\%} with 3-hour forecasting for two different types of buildings.},
    address = {Paris, France},
    author = {Ridi, Antonio and Zarkadis, Nikos and Gisler, Christophe and Hennebert, Jean},
    booktitle = {Proceedings of the 2015 International Conference on Data Science and Advanced Analytics (DSAA 2015)},
    editor = {Gaussier, Eric and Cao, Longbing},
    file = {:Users/gislerc/Documents/Mendeley/Articles/Ridi et al/Proceedings of the 2015 International Conference on Data Science and Advanced Analytics (DSAA 2015)/Ridi et al. - 2015 - Duration Models for Activity Recognition and Prediction in Buildings using Hidden Markov Models.pdf:pdf},
    isbn = {9781467382731},
    keywords = {Activity recognition,Energy savings in buildings,Expanded Hidden,Markov Models,Minimum Duration modeling,activity recognition,energy savings in buildings,expanded hidden,markov models,minimum duration modeling},
    mendeley-tags = {Activity recognition,Energy savings in buildings,Expanded Hidden,Markov Models,Minimum Duration modeling},
    pages = {10},
    publisher = {IEEE Computer Society},
    title = {{Duration Models for Activity Recognition and Prediction in Buildings using Hidden Markov Models}},
    url = {http://dsaa2015.lip6.fr},
    year = {2015}
    }
  • A. Ridi, C. Gisler, and J. Hennebert, “Processing smart plug signals using machine learning,” in Wireless Communications and Networking Conference Workshops (WCNCW), 2015 IEEE, 2015, pp. 75-80.
    [Website] [PDF] [Bibtex]
    @conference{ridi2015wcnc,
    Abstract = {The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to wall sockets. We observe an increasing number of research teams working on this topic under the umbrella Intrusive Load Monitoring. This term is used as opposition to Non-Intrusive Load Monitoring that refers to the use of global smart meters. We first present the latest evolutions of the ACS-F database, a collections of signatures that we made available for the scientific community. The database contains different brands and/or models of appliances with up to 450 signatures. Two evaluation protocols are provided with the database to benchmark systems able to recognise appliances from their electric signature. We present in this paper two additional evaluation protocols intended to measure the impact of the analysis window length. Finally, we present our current best results using machine learning approaches on the 4 evaluation protocols.},
    Author = {A. Ridi and C. Gisler and J. Hennebert},
    Booktitle = {Wireless Communications and Networking Conference Workshops (WCNCW), 2015 IEEE},
    Doi = {10.1109/WCNCW.2015.7122532},
    Keywords = {learning,artificial intelligence, power engineering computing,power supplies to apparatus,ACS-F database,IoT smart plugs,machine learning approaches,smart buildings,smart plug signals,umbrella intrusive load monitoring,Accuracy,Databases,Hidden Markov mod},
    Month = {March},
    Note = {Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.},
    Pages = {75-80},
    Title = {{P}rocessing smart plug signals using machine learning},
    Pdf = {http://www.hennebert.org/download/publications/wcncw-2015-Processing-smart-plug-signals-using-machine-learning.pdf},
    Year = {2015},
    Pdf = {http://www.hennebert.org/download/publications/wcncw-2015-Processing-smart-plug-signals-using-machine-learning.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/WCNCW.2015.7122532}}
  • A. Ridi, C. Gisler, and J. Hennebert, “User Interaction Event Detection in the Context of Appliance Monitoring,” in The 13th International Conference on Pervasive Computing and Communications (PerCom 2015), Workshop on Pervasive Energy Services (PerEnergy), 2015, pp. 323-328.
    [Website] [PDF] [Bibtex]
    @conference{ridi2015percom,
    Abstract = {In this paper we assess about the recognition of User Interaction events when handling electrical devices. This work is placed in the context of Intrusive Load Monitoring used for appliance recognition. ILM implies several Smart Metering Sensors to be placed inside the environment under analysis (in our case we have one Smart Metering Sensor per device). Our existing system is able to recognise the appliance class (as coffee machine, printer, etc.) and the sequence of states (typically Active / Non-Active) by using Hidden Markov Models as machine learning algorithm. In this paper we add a new layer to our system architecture called User Interaction Layer, aimed to infer the moments (called User Interaction events) during which the user interacts with the appliance. This layer uses as input the information coming from HMM (i.e. the recognised appliance class and the sequence of states). The User Interaction events are derived from the analysis of the transitions in the sequences of states and a ruled-based system adds or removes these events depending on the recognised class. Finally we compare the list of events with the ground truth and we obtain three different accuracy rates: (i) 96.3% when the correct model and the real sequence of states are known a priori, (ii) 82.5% when only the correct model is known and (iii) 80.5% with no a priori information.},
    Author = {Antonio Ridi and Christophe Gisler and Jean Hennebert},
    Booktitle = {The 13th International Conference on Pervasive Computing and Communications (PerCom 2015), Workshop on Pervasive Energy Services (PerEnergy)},
    Doi = {10.1109/PERCOMW.2015.7134056},
    Keywords = {domestic appliances;hidden Markov models;home automation;human computer interaction;learning (artificial intelligence);smart meters;HMM;ILM;appliance monitoring;appliance recognition;electrical devices;hidden Markov models;intrusive load monitoring;machine learning algorithm;ruled-based system;smart metering sensors;user interaction event detection;user interaction layer;Accuracy;Databases;Hidden Markov models;Home appliances;Mobile handsets;Monitoring;Senior citizens;Appliance Identification;Intrusive Load Monitoring (ILM);User-Appliance Interaction},
    Note = {Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.},
    Pages = {323-328},
    Title = {{U}ser {I}nteraction {E}vent {D}etection in the {C}ontext of {A}ppliance {M}onitoring},
    Pdf = {http://www.hennebert.org/download/publications/percom-2015-User-Interaction-Event-Detection-in-the-Context-of-Appliance-Monitoring.pdf},
    Year = {2015},
    Pdf = {http://www.hennebert.org/download/publications/percom-2015-User-Interaction-Event-Detection-in-the-Context-of-Appliance-Monitoring.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/PERCOMW.2015.7134056}}
  • C. Gisler, A. Ridi, M. Fauquey, D. Genoud, and J. Hennebert, “Towards Glaucoma Detection Using Intraocular Pressure Monitoring,” in The 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR 2014), 2014, pp. 255-260.
    [Website] [PDF] [Bibtex]
    @conference{gisler2014:socpar,
    Abstract = {Diagnosing the glaucoma is a very difficult task for healthcare professionals. High intraocular pressure (IOP) remains the main treatable symptom of this degenerative disease which leads to blindness. Nowadays, new types of wearable sensors, such as the contact lens sensor Triggerfish{\textregistered}, provide an automated recording of 24-hour profile of ocular dimensional changes related to IOP. Through several clinical studies, more and more IOP-related profiles have been recorded by those sensors and made available for elaborating data-driven experiments. The objective of such experiments is to analyse and detect IOP pattern differences between ill and healthy subjects. The potential is to provide medical doctors with analysis and detection tools allowing them to better diagnose and treat glaucoma. In this paper we present the methodologies, signal processing and machine learning algorithms elaborated in the task of automated detection of glaucomatous IOP- related profiles within a set of 100 24-hour recordings. As first convincing results, we obtained a classification ROC AUC of 81.5%.},
    Author = {Christophe Gisler and Antonio Ridi and Mil{\`e}ne Fauquey and Dominique Genoud and Jean Hennebert},
    Booktitle = {The 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR 2014)},
    Doi = {10.1109/SOCPAR.2014.7008015},
    Isbn = {9781479959358},
    Keywords = {Biomedical signal processing, Glaucoma diagnosis, Machine learning},
    Pages = {255-260},
    Publisher = {Institute of Electrical and Electronics Engineers ( IEEE )},
    Title = {{T}owards {G}laucoma {D}etection {U}sing {I}ntraocular {P}ressure {M}onitoring},
    Pdf = {http://www.hennebert.org/download/publications/socpar-2014-towards-glaucoma-detection-using-intraocular-pressure-monitoring.pdf},
    Year = {2014},
    Pdf = {http://www.hennebert.org/download/publications/socpar-2014-towards-glaucoma-detection-using-intraocular-pressure-monitoring.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/SOCPAR.2014.7008015}}
  • A. Ridi, C. Gisler, and J. Hennebert, “ACS-F2 – A new database of appliance consumption signatures,” in Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of, 2014, pp. 145-150.
    [Website] [PDF] [Bibtex]
    @conference{ridi2014socpar,
    Abstract = {We present ACS-F2, a new electric consumption signature database acquired from domestic appliances. The scenario of use is appliance identification with emerging applications such as domestic electricity consumption understanding, load shedding management and indirect human activity monitoring. The novelty of our work is to use low-end electricity consumption sensors typically located at the plug. Our approach consists in acquiring signatures at a low frequency, which contrast with high frequency transient analysis approaches that are costlier and have been well studied in former research works. Electrical consumption signatures comprise real power, reactive power, RMS current, RMS voltage, frequency and phase of voltage relative to current. A total of 225 appliances were recorded over two sessions of one hour. The database is balanced with 15 different brands/models spread into 15 categories. Two realistic appliance recognition protocols are proposed and the database is made freely available to the scientific community for the experiment reproducibility. We also report on recognition results following these protocols and using baseline recognition algorithms like k-NN and GMM.},
    Author = {A. Ridi and C. Gisler and J. Hennebert},
    Booktitle = {Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of},
    Doi = {10.1109/SOCPAR.2014.7007996},
    Isbn = {9781479959358},
    Keywords = {machine learning, electric signal, appliance signatures},
    Month = {Aug},
    Pages = {145-150},
    Publisher = {IEEE},
    Title = {{ACS}-{F}2 - {A} new database of appliance consumption signatures},
    Pdf = {http://www.hennebert.org/download/publications/socpar-2014-ACS-F2-a-new-databas-of-appliance-consumption-signatures.pdf},
    Year = {2014},
    Pdf = {http://www.hennebert.org/download/publications/socpar-2014-ACS-F2-a-new-databas-of-appliance-consumption-signatures.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/SOCPAR.2014.7007996}}
  • A. Ridi, C. Gisler, and J. Hennebert, “Appliance and State Recognition using Hidden Markov Models,” in The 2014 International Conference on Data Science and Advanced Analytics (DSAA 2014), Shangai, China, 2014, pp. 270-276.
    [Website] [PDF] [Bibtex]
    @conference{ridi2014dsaa,
    Abstract = {We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hidden Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (101 Hz) and are sourced from a specific database. We follow two predefined protocols for providing comparable results. Recovering information on the actual appliance state permits to potentially adopt energy saving measures, as switching off stand-by appliances or, generally speaking, changing their state. Moreover, in most of the cases appliance states are related to user activities: the user interaction usually involves a transition of the appliance state. Information about the state transition could be useful in Smart Home / Building Systems to reduce energy consumption and increase human comfort. We report the results of the classification tasks in terms of confusion matrices and accuracy rates. Finally, we present our application for a real-time data visualization and the recognition of the appliance category with its actual state.},
    Address = {Shangai, China},
    Author = {Antonio Ridi and Christophe Gisler and Jean Hennebert},
    Booktitle = {The 2014 International Conference on Data Science and Advanced Analytics (DSAA 2014)},
    Doi = {10.1109/DSAA.2014.7058084},
    Isbn = {9781479969821},
    Keywords = {Appliance Identification, Appliance State Recognition, Intrusive Load Monitoring, ILM},
    Month = {10/2014},
    Note = {Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.},
    Pages = {270-276},
    Publisher = {Institute of Electrical and Electronics Engineers ( IEEE )},
    Title = {{A}ppliance and {S}tate {R}ecognition using {H}idden {M}arkov {M}odels},
    Pdf = {http://www.hennebert.org/download/publications/DSAA-2014-appliance-and-state-recognition-using-hidden-markov-models.pdf},
    Year = {2014},
    Pdf = {http://www.hennebert.org/download/publications/DSAA-2014-appliance-and-state-recognition-using-hidden-markov-models.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/DSAA.2014.7058084}}
  • A. Ridi, C. Gisler, and J. Hennebert, “A Survey on Intrusive Load Monitoring for Appliance Recognition,” in 22nd International Conference on Pattern Recognition – ICPR, 2014, pp. 3702-3707.
    [Website] [PDF] [Bibtex]
    @conference{ridi2014:icpr,
    Abstract = {Electricity load monitoring of appliances has be- come an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on low- end electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.},
    Author = {Antonio Ridi and Christophe Gisler and Jean Hennebert},
    Booktitle = {22nd International Conference on Pattern Recognition - ICPR},
    Doi = {10.1109/ICPR.2014.636},
    Isbn = {9781479952106},
    Keywords = {Machine Learning, Intrusive Load Monitoring, ILM, IT for efficiency, Green Computing},
    Month = {August},
    Note = {Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.},
    Organization = {IEEE},
    Pages = {3702-3707},
    Title = {{A} {S}urvey on {I}ntrusive {L}oad {M}onitoring for {A}ppliance {R}ecognition},
    Pdf = {http://www.hennebert.org/download/publications/icpr-2014-a-survey-on-intrusive-load-monitoring-for-appliance-recognition.pdf},
    Year = {2014},
    Pdf = {http://www.hennebert.org/download/publications/icpr-2014-a-survey-on-intrusive-load-monitoring-for-appliance-recognition.pdf},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/ICPR.2014.636}}
  • A. Ridi, C. Gisler, and J. Hennebert, “A Survey on Intrusive Load Monitoring for Appliance Recognition,” in 22nd International Conference on Pattern Recognition – ICPR, 2014, pp. 3702-3707.
    [Website] [PDF] [Bibtex]
    @conference{ridi2014:icpr,
    author = "Antonio Ridi and Christophe Gisler and Jean Hennebert",
    abstract = "Electricity load monitoring of appliances has be- come an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on low- end electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.",
    booktitle = "22nd International Conference on Pattern Recognition - ICPR",
    doi = "10.1109/ICPR.2014.636",
    isbn = "9781479952106",
    keywords = "Machine Learning, Intrusive Load Monitoring, ILM, IT for efficiency, Green Computing",
    month = "August",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    organization = "IEEE",
    pages = "3702-3707",
    title = "{A} {S}urvey on {I}ntrusive {L}oad {M}onitoring for {A}ppliance {R}ecognition",
    Pdf = "http://www.hennebert.org/download/publications/icpr-2014-a-survey-on-intrusive-load-monitoring-for-appliance-recognition.pdf",
    year = "2014",
    }
  • C. Gisler, A. Ridi, and J. Hennebert, “Appliance Consumption Signature Database and Recognition Test Protocols,” in WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013, 2013, pp. 336-341.
    [Website] [PDF] [Bibtex]
    @conference{gisler:2013:wosspa,
    author = "Christophe Gisler and Antonio Ridi and Jean Hennebert",
    abstract = "We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave oven, printers, and televisions (LCD or LED). We measured their consumption in terms of real power (W), reactive power (var), RMS current (A) and phase of voltage relative to current (varphi). We plan to give free access to the database for the whole scientific community. The proposed test protocols will help to objectively compare new algorithms. ",
    booktitle = "WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013",
    doi = "10.1109/WoSSPA.2013.6602387",
    isbn = "9781467355407",
    keywords = "electric consumption modelling, benchmark protocols",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    pages = "336-341",
    title = "{A}ppliance {C}onsumption {S}ignature {D}atabase and {R}ecognition {T}est {P}rotocols",
    Pdf = "http://hennebert.org/download/publications/wosspa-2013-appliance-consumption-signature-database-and-recognition-test-protocols.pdf",
    year = "2013",
    }
  • A. Ridi, C. Gisler, and J. Hennebert, “Unseen Appliances Identification,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2013, p. 75–82.
    [Website] [PDF] [Bibtex]
    @conference{ridi2013:ciarp,
    author = "Antonio Ridi and Christophe Gisler and Jean Hennebert",
    abstract = "We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to provide comparable results to the scientific community. We first evaluate the drop of performance when going from seen to unseen appliances. We then analyze the results of different machine learning algorithms, as the k-Nearest Neighbor (k-NN) and Gaussian Mixture Models (GMMs). Several tunings allow us to achieve 74% correct accuracy using GMMs which is our current best system.",
    booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications",
    doi = "10.1007/978-3-642-41827-3_10",
    editor = "Jos{\'e} Ruiz-Shulcloper and Gabriella Sanniti di Baja",
    isbn = "9783642418266",
    keywords = "machine learning, nilm, appliance identification, load monitoring",
    pages = "75--82",
    publisher = "Springer",
    series = "Lecture Notes in Computer Science",
    title = "{U}nseen {A}ppliances {I}dentification",
    Pdf = "http://www.hennebert.org/download/publications/ciarp-2013-unseen-appliance-identification.pdf",
    volume = "8259",
    year = "2013",
    }
  • A. Ridi, C. Gisler, and J. Hennebert, “Le machine learning: un atout pour une meilleure efficacité – applications à la gestion énergétique des bâtiments,” Bulletin Electrosuisse, iss. 10s, pp. 21-24, 2013.
    [PDF] [Bibtex]
    @article{ridi2013:electrosuisse,
    author = "Antonio Ridi and Christophe Gisler and Jean Hennebert",
    abstract = "Comment g{\'e}rer de mani{\`e}re intelligente les consommations et productions d’{\'e}nergie dans les b{\^a}timents ? Les solutions {\`a} ce probl{\`e}me complexe pourraient venir du monde de l’apprentissage automatique ou «machine learning». Celui-ci permet la mise au point d’algorithmes de contr{\^o}le avanc{\'e}s visant simultan{\'e}ment la r{\'e}duction de la consommation d’{\'e}nergie, l’am{\'e}lioration du confort de l’utilisateur et l’adaptation {\`a} ses besoins.",
    issn = "1660-6728",
    journal = "Bulletin Electrosuisse",
    keywords = "Machine Learning, Energy Efficiency, Smart Buildings",
    number = "10s",
    pages = "21-24",
    title = "{L}e machine learning: un atout pour une meilleure efficacit{\'e} - applications {\`a} la gestion {\'e}nerg{\'e}tique des b{\^a}timents",
    Pdf = "http://hennebert.org/download/publications/electrosuisse-2013-machine-learning-meilleure-efficacite.pdf",
    year = "2013",
    }
  • A. Ridi, C. Gisler, and J. Hennebert, “Automatic Identification of Electrical Appliances Using Smart Plugs,” in WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013, Algeria, 2013, pp. 301-305.
    [Website] [PDF] [Bibtex]
    @conference{ridi:2013:wosspa,
    author = "Antonio Ridi and Christophe Gisler and Jean Hennebert",
    abstract = "We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applications, such electric signatures can be used to identify the type of appliance in use. In this paper, we propose to use dynamic features based on time derivative and time second derivative features and we compare different classification algorithms including K-Nearest Neighbor and Gaussian Mixture Models. We use the recently recorded electric signature database ACS-F1 and its intersession protocol to evaluate our algorithm propositions. The best combination of features and classifiers shows 93.6% accuracy.",
    address = "Algeria",
    booktitle = "WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013",
    doi = "10.1109/WoSSPA.2013.6602380",
    isbn = "9781467355407",
    keywords = "machine learning, electric consumption analysis, GMM, HMM",
    month = "May",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    pages = "301-305",
    publisher = "IEEE",
    title = "{A}utomatic {I}dentification of {E}lectrical {A}ppliances {U}sing {S}mart {P}lugs",
    Pdf = "http://hennebert.org/download/publications/wosspa-2013-automatic-identification-of-electrical-appliances-using-smart-plugs.pdf",
    year = "2013",
    }
  • C. Gisler, G. Barchi, G. Bovet, E. Mugellini, and J. Hennebert, “Demonstration Of A Monitoring Lamp To Visualize The Energy Consumption In Houses,” in The 10th International Conference on Pervasive Computing (Pervasive2012), Newcastle, 2012.
    [PDF] [Bibtex]
    @conference{gisl12:pervasive,
    author = "Christophe Gisler and Grazia Barchi and G{\'e}r{\^o}me Bovet and Elena Mugellini and Jean Hennebert",
    abstract = "We report on the development of a wireless lamp dedicated to the feedback of energy consumption. The principle is to provide a simple and intuitive feedback to residents through color variations of the lamp depending on the amount of energy consumed in a house. Our system is demonstrated on the basis of inexpensive components piloted by a gateway storing and processing the energy data in a WoT framework. Different versions of the color choosing algorithm are also presented.",
    address = "Newcastle",
    booktitle = "The 10th International Conference on Pervasive Computing (Pervasive2012)",
    keywords = "Web of Things; Energy feedback; Green Computing; IT-for-Green",
    month = "jun",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    title = "{D}emonstration {O}f {A} {M}onitoring {L}amp {T}o {V}isualize {T}he {E}nergy {C}onsumption {I}n {H}ouses",
    Pdf = "http://www.hennebert.org/download/publications/pervasive-2012-demonstration-of-a-monitoring-lamp-to-visualize-the-energy-consumption-in-houses.pdf",
    year = "2012",
    }
  • D. Zufferey, C. Gisler, O. A. Khaled, and J. Hennebert, “Machine Learning Approaches for Electric Appliance Classification,” in The 11th International Conference on Information Sciences, Signal Processing and their Applications: Main Tracks (ISSPA2012 – Tracks), Montreal, Canada, Canada, 2012, pp. 740-745.
    [Website] [PDF] [Bibtex]
    @conference{zuff12:isspa,
    author = "Damien Zufferey and Christophe Gisler and Omar Abou Khaled and Jean Hennebert",
    abstract = "We report on the development of an innovative system which can automatically recognize home appliances based on their electric consumption profiles. The purpose of our system is to apply adequate rules to control electric appliance in order to save energy and money. The novelty of our approach is in the use of plug-based low-end sensors that measure the electric consumption at low frequency, typically every 10 seconds. Another novelty is the use of machine learning approaches to perform the classification of the appliances. In this paper, we present the system architecture, the data acquisition protocol and the evaluation framework. More details are also given on the feature extraction and classification models being used. The evaluation showed promising results with a correct rate of identification of 85\%.",
    address = "Montreal, Canada, Canada",
    booktitle = "The 11th International Conference on Information Sciences, Signal Processing and their Applications: Main Tracks (ISSPA2012 - Tracks)",
    comments = "9781467303811",
    doi = "10.1109/ISSPA.2012.6310651",
    editor = "IEEE",
    isbn = "978-1-4673-0381-1",
    keywords = "Signal processing, machine learning algorithms, power system analysis computing, energy consumption, energy efficiency, sustainable development, green-computing, it-for-green",
    month = "jul",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    pages = "740-745",
    title = "{M}achine {L}earning {A}pproaches for {E}lectric {A}ppliance {C}lassification",
    Pdf = "http://www.hennebert.org/download/publications/isspa-2012-machine-learning-approaches-for-electrical-appliance-classification.pdf",
    year = "2012",
    }