A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

[EN]Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system’s real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.European Commision (EC). Funding H2020/MSCARISE. Project Code: 64179

Non-intrusive load monitoring techniques for activity of daily living recognition

Esta tesis nace con la motivación de afrontar dos grandes problemas de nuestra era: la falta de recursos energéticos y el envejecimiento de la población. Respecto al primer problema, nace en la primera década de este siglo el concepto de Smart Grids con el objetivo de alcanzar la eficiencia energética. Numerosos países comienzan a realizar despliegues masivos de contadores inteligentes ("Smart Meters"), lo que despierta el interés de investigadores que comienzan a desarrollar nuevas técnicas para predecir la demanda. Así, los sistemas NILM (Non-Intrusive Load Monitoring) tratan de predecir el consumo individual de los dispositivos conectados a partir de un único sensor: el contador inteligente. Por otra parte, los grandes avances en la medicina moderna han permitido que nuestra esperanza de vida aumente considerablemente. No obstante, esta longevidad, junto con la baja fertilidad en los países desarrollados, tiene un efecto secundario: el envejecimiento de la población. Unos de los grandes avances es la incorporación de la tecnología en la vida cotidiana, lo que ayuda a los más mayores a llevar una vida independiente. El despliegue de una red de sensores dentro de la vivienda permite su monitorización y asistencia en las tareas cotidianas. Sin embargo, son intrusivos, no escalables y, en algunas ocasiones, de alto coste, por lo que no están preparados para hacer frente al incremento de la demanda de esta comunidad. Esta tesis doctoral nace de la motivación de afrontar estos problemas y tiene dos objetivos principales: lograr un modelo de monitorización sostenible para personas mayores y, a su vez, dar un valor añadido a los sistemas NILM que despierte el interés del usuario final. Con este objetivo, se presentan nuevas técnicas de monitorización basadas en NILM, aunando lo mejor de ambos campos. Esto supone un ahorro considerable de recursos en la monitorización, ya que únicamente se necesita un sensor: el contador inteligente; lo cual da escalabilidad a estos sistemas. Las contribuciones de esta tesis se dividen en dos bloques principales. En el primero se proponen nuevas técnicas NILM optimizadas para la detección de la actividad humana. Así, se desarrolla una propuesta basada en detección de eventos (conexiones de dispositivos) en tiempo real y su clasificación a un dispositivo. Con el objetivo de que pueda integrarse en contadores inteligentes. Cabe destacar que el clasificador se basa en modelos generalizados de dispositivos y no necesita conocimiento específico de la vivienda. El segundo bloque presenta tres nuevas técnicas de monitorización de personas mayores basadas en NILM. El objetivo es proporcionar una monitorización básica pero eficiente y altamente escalable, ahorrando en recursos. Los procesos Cox, log Gaussian Cox Processes (LGCP), monitorizan un único dispositivo si la rutina está estrechamente ligada a este. Así, se propone un sistema de alarmas si se detectan cambios en el comportamiento. LGCP tiene la ventaja de poder modelar periodicidades e incertidumbres propias del comportamiento humano. Cuando la rutina no depende de un único dispositivo, se proponen dos técnicas: una basada en gaussianas mixtas, Gaussian Mixture Models (GMM); y la otra basada en la Teoría de la Evidencia de Dempster-Shafer (DST). Ambas monitorizan y detectan deterioros en la actividad, causados por enfermedades como la demencia y el alzhéimer. Únicamente DST usa incertidumbres que simulan mejor el comportamiento humano y, por tanto, permite alarmas en caso de un repentino desvío. Finalmente, todas las propuestas han sido validadas mediante la evaluación de métricas y la obtención de resultados experimentales. Para ello, se han usado medidas de escenarios reales que han sido recopiladas en bases de datos. Los resultados obtenidos han sido satisfactorios, demostrando que este tipo de monitorización es posible y muy beneficioso para nuestra sociedad. Además, se ha dado a lugar nuevas propuestas que serán desarrolladas en el futuro. Códigos UNESCO: 120320 - sistemas de control medico, 332201 – distribución de la energía, 120701 – análisis de actividades, 120304 – inteligencia artificial, 120807 – plausibilidad, 221402 – patrones

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

Advanced photonic and electronic systems - WILGA 2017

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445

Smart Grids: A Comprehensive Survey of Challenges, Industry Applications, and Future Trends

With the increased energy demands of the 21st century, there is a clear need for developing a more sustainable method of energy generation, distribution, and transmission. The popularity of Smart Grid continues to grow as it presents its benefits, including interconnectivity, improved efficiency, the ability to integrate renewable energy sources, and many more. However, it is not without its challenges. This survey aims to provide an introductory background of smart grids, detail some of the main aspects and current challenges, and review the most recent papers and proposed solutions. It will also highlight the current state of implementation of the smart grid by describing various prototypes, as well as various countries and continents implementation plans and projects.Comment: Paper has been submitted for review to the journal Energy Reports (January 23, 2024). 58 pages, 7 figures, 7 table

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Monitoring Occupancy and Office Equipment Energy Consumption Using Real-Time Location System and Wireless Energy Meters

Buildings are one of the major energy consumers because of the need to meet occupants’ requirements. The commercial/institutional sector accounted for 14% of total energy consumption in Canada in 2009 while office buildings consumed 35% of this amount. Auxiliary equipment used 19% of the total energy consumed in office buildings. Previous studies showed the impact of occupancy behavior on IT equipment energy consumption. This thesis proposes a new method for monitoring occupancy behavior and energy consumption of IT equipment. Two wireless sensor technologies are investigated to collect the required data and to build an occupancy behavior estimation profile: Ultra-Wideband Real-Time Location System for occupancy location monitoring and ZigBee wireless energy meters for monitoring the energy consumption of IT equipment. The occupancy monitoring data gained from the UWB are used to create the occupants’ hourly profile. The occupancy profile based on short-time monitoring can be used to simulate long-term energy consumption. In conclusion, the comparison between the results shows up to 11% and 24% saving for heating loads and cooling loads, respectively. The proposed method profiles also resulted in up to 65% and 78% reduction for lighting and IT equipment energy consumption in the office, respectively. Therefore, dynamic occupancy driven profiles will reduce the energy consumption