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

Critical review and research roadmap of office building energy management based on occupancy monitoring

Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings’ physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research

Liquid Crystal Shuttered Passive Infrared Sensors for True Presence Detection

PIR sensors, known as motion detectors, are widely used for moving occupancy detection. Made of pyroelectric materials, such as LiTaO₃, generating pyroelectric current when the received infrared radiation changes, PIR sensors only respond to the motion of occupants. This results in frequent false negative detections when stationary occupancy detection is also desired, such as occupancy-based building lighting control. To enable stationary occupancy detection, in this dissertation, we develop optical shutters to actively modulate the radiation received by the PIR sensors in the long-wave infrared (LWIR) region (8-12 µm) where human skin radiates the most. The optical shutter is made of polymer dispersed liquid crystal (PDLC) sandwiched by two germanium substrates. Each germanium substrate has an anti-reflected film on one side (the nonconductive side) to reduce the reflection. The PDLC infrared shutter, a PIR sensor, and a driving circuit forms a synchronized low-energy electronically chopped PIR (SLEEPIR) sensor module. To better improve its performance, we devised SLEEPIR sensor nodes, and formed a SLEEPIR sensor network system with advanced machine learning algorithms. The main contributions of this dissertation include: (i) modeling the SLEEPIR output as a function of the effective modulation, the response time of the PDLC shutter, and the time constants of the PIR sensor; (ii) quantifying the impact of the driving voltage, the mass ratio, the cell gap, and the cooling rate on the effective modulation and the response time of the PDLC shutter to obtain the optimal driving voltage and fabrication conditions that maximize SLEEPIR module’s output; and (iii) experimental validation of the SLEEPIR sensor nodes for presence detection in the lab and uncontrolled environment settings

Dynamic Rule-Based Reasoning in Smart Environments

Slimme huizen en andere soorten slimme omgevingen kunnen worden gedefinieerd door verschillende belangrijke karakteristieken. De belangrijkste hiervan is ongetwijfeld de mogelijkheid om omgevingsbewust te zijn, om de fysieke omgeving te ervaren en om de context van de huidige situatie te begrijpen. Slimme omgevingen zouden in staat moeten zijn om met deze informatie te kunnen redeneren en waardevolle kennis te kunnen afleiden. Daarnaast zullen ze de mogelijkheid moeten hebben om intelligent te reageren in reactie op veranderende situaties, volgens bepaalde doelstellingen. Slimme omgevingen zijn vaak ubiquitous, wat betekent dat hun capaciteiten voor waarnemen en handelen berusten op apparaten die zijn ingebed in de fysieke wereld. De meeste van de huidige commerciele slimme omgevingsproducten presenteren slechts gedeeltelijke oplossingen, zoals automatische verlichting of energiebewustzijn. Verschillende factoren vertragen de commercialisering van volledig slimme huizen, waaronder de noodzaak om de oplossing op iedere nieuwe locatie opnieuw zeer nauwkeurig af te stellen, de inspanningen rondom de integratie en co'ordinatie van verschillende componenten, handelingen om een consistent model over verschillende subsystemen van verschillende bronnen samen te stellen, enzovoorts. Samenvattend, de grote hoeveelheid aan inspanningen die benodigd zijn om de oplossing van een locatie naar een andere te verplaatsen hindert de mogelijkheden voor het stroomlijnen van de uitrol. Wat zijn de overeenkomsten in het ontwerp en het ontwikkelproces van een slimme omgeving? Wat is een effectieve aanpak om een redeneringsmotor voor slimme omgevingen te ontwerpen die aan alle belangrijke vereisten voldoet? Hoe kan het effect van sensorfouten voor wat betreft de besluitvorming worden geminimaliseerd? Hoe kan een slim systeem het bestaan van verschillende energieleveranciers gebruiken om de energiekosten in de tijd te minimaliseren? In dit proefschrift bespreken we en geven we antwoord op een aantal belangrijke onderzoeksvraagstukken voor huidige pervasieve systemen, slimme omgevingen in het bijzonder