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    Non-Intrusive Load Monitoring techniques for Activity of Daily Living recognition

    TitleNon-Intrusive Load Monitoring techniques for Activity of Daily Living recognition
    Publication TypePhD Thesis
    Año de publicación2017
    Thesis Advisor(s)Ureña, J, Hernández, Á
    AutoresAlcalá, JM
    Academic DepartmentElectronics
    Numero de volúmenes1
    Number of Pages202
    CityAlcalá de Henares
    Fecha de publicaciónFebruary-2017

    Among the great challenges of this era, those that raise the most interest, it can be
    highlighted those that are direct consequence of the development of our society and its
    overpopulation: the climate change, the lack of energy resources and the ageing population.
    With regard the two first challenges, the energy demand is dramatically increasing and
    it is envisaged that, in a matter of a few years, it will overcome the produced one. It
    is, hence, not only necessary to replace fossil fuels with clean and renewable energy, but also to reach an efficient energy consumption and distribution. Thus, the Smart Grid concept emerges from this idea during the first decade of this century and, since then, many countries have been largely rolling out Smart Meters to achieve this milestone.
    This fact is bringing in the interest of a large number of researchers who are devising
    new techniques to predict the demand and to promote efficiency. NILM (Non-Intrusive
    Load Monitoring) is among them and it is raising a great interest. It tries to foresee what devices are plugged and how much they are consuming from a single sensor: the Smart Meter. However, the motivation in NILM between researchers and electricity companies is not corresponded by end-consumers, who find no benefit on their bills from energy savings, mostly leveraged by taxes.
    Moreover, great achievements in the modern medicine is leading to a significant life expectancy incresing. Nonetheless, this longevity, along with low fertility in developed countries, entails a side effect: the ageing population. One of the greatest reached milestones is the technology integration in the daily life, which supports the elderlies to lead an independent life. The deployment of a sensor network within the household allows monitoring and assistance in everyday tasks. Although, they are not prepared to cope with the increasing demand of this community as they are intrusive, not scalable and, in cases, expensive.

    This doctoral thesis is born from the motivation to address these issues. It has two main objectives: the achievement of a sustainable monitoring model for elderlies and, in turn, to give an added value to NILM that awakes the interest of the end-consumer. To this end, novel monitoring techniques based on NILM are presented, combining the best of both domanins. This represents considerable savings in monitoring resources since it only uses a sensor: the Smart Meter. Hence, scalability is at reach.
    The contributions of this Thesis are organised in two blocks. The first introduces new
    event-based NILM algorithm optimised for human activity detection. The proposal detects
    events in real time, such as appliance swithing-on; and it classifies them into appliance types, all aimed to be integrated in a Smart Meter. It is worth noting that the classifier is based on general models of appliance types and, hence, it does not need specific knowledge about household. It is an unsupervised training method and, consequently, it can be trained offline and loaded into Smart Meter memories at once for all households.
    Concerning the second block, three novel activity monitoring techniques for elderlies are presented. They are based on NILM and they use the appliance usage patterns to infer the human activity. The aim is to provide basic but efficient and highly scalable monitoring, saving resources during the deployment. Thus, the log Gaussian Cox Processes (LGCP) approach monitors a single appliance, in case the daily routine is strongly correlated to it.
    It implements an alarm system whether a rapid pattern deviation is detected. LGCP has
    the advantage of being able to model periodicities and uncertainties inherent to the human behavior. Whenever the routine does not depend on a single appliance, two techniques are proposed: the Gaussian Mixture Model (GMM) and the Dempster-Shafer Theory (DST) approach. Both monitor and detect deteriorations in the activity, possibly caused by diseases such as dementia and Alzheimer’s disease. Nevertheless, only the DST approach simulate uncertainties, due to the human’s free will, and, therefore, it allows to trigger alarms in case of a sudden pattern deviation in the behavior.
    All proposals have been validated by the evaluation of metrics and the obtaining of experimental results. To this end, real household measurements have been used, which have been collected in datasets. Satifactory results have been obtained, proving that this type of monitoring is feasible and very beneficial to our society. In addition, this thesis has yielded new proposals to be addressed in the future.

    ja_tesis_vf.pdf12.53 MB