Browsing by Keyword "Smart building"
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Item An IoT sensor network to model occupancy profiles for energy usage simulation tools(IEEE, 2018-11-13) Saralegui, Unai; Anton, Miguel Angel; Arbelaitz, Olatz; Muguerza, Javier; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThe development of IoT devices has allowed to install large amounts of sensors in different environments. Consequently, monitoring small houses and entire buildings has become possible. In addition, buildings are one of the biggest energy consumers, so the monitoring of the energy waste, and its sources, is gaining attention. Human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, thus being able to easily monitor such behaviour will bring greater insight in the building usage. In this paper, an IoT sensor network is proposed to model occupancy profiles at room level. Such measurement of users’ behaviour along with additional information such as temperature or humidity can be used to develop strategies to save energy, especially regarding heating, ventilating and airconditioning (HVAC) systems. The proposed equipment has been gathering data for some months in a workplace containing several meeting rooms. Four of those rooms were monitored and later analysed to test the validity of the proposed approach. The results show that it is possible to obtain occupancy profiles by using simple IoT equipment.Item Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People: Dealing with noisy patterns to help older people(2019-07-02) Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Saralegui, Unai; Sun, Shengjing; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; Tecnalia Research & InnovationThis paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.