Generic Occupancy Sensor | EPFL Graph Search

The goal of this project was to work on room occupancy and to provide solutions and ideas on how to detect if a room is occupied or not using generic and non expensive sensors. This is motivated by the fact that buildings account for 50% of the total energy consumption in the US and 30% of it is wasted mainly due to a bad management of light, heating, cooling and ventilation systems. Being able to know if rooms are occupied or not would then enable us to automatize these systems and make them more energy efficient. The sensors studied in the process were a CO2, relative humidity and temperature sensor, a camera, a WiFi sniffer and a PIR(Passive infrared) sensor. All of them were implemented and tested to see how they could measure occupancy. After analyzing each sensor alone we had to combine them to obtain the best possible accuracy. To do so, two methods were tried. The first using all the sensors mentioned was tested and the accuracy achieved was 93%. The second was based on the use of neural network on images provided by the camera to detect people and the accuracy was 97%.

Novel models towards predictive control of advanced building systems and occupant comfort in buildings

Nikolaos Zarkadis

People in developed countries spend today most of their time inside buildings as part of the modern way of life. As a result, the building sector accounts for almost 40% of the total energy consumption and a big part of the energy bill goes to maintain the visual and thermal comfort of their occupants. At the same time, awareness is being raised during the last decades about the greenhouse gas emissions and the possibly irreversible effects of global warming; both linked to excessive use of non-renewable primary energy sources which still power most of the world, including our buildings. Thus, moderating the energy consumed in them is a top priority. However, this does not imply a horizontal cut in energy consumption that would result in a drop of user comfort. Instead, we suggest that improving energy efficiency in buildings while maintaining or even improving the user comfort is the optimal solution. It is indeed the core of this thesis that there is a great energy saving potential in refining the control of building systems such as electric lighting, heating, cooling and ventilation, which more than often consume a lot of energy without delivering the analogous amount of visual and thermal comfort. In this direction, this thesis proposes the development of a novel predictive control algorithm for the control of electrochromic glazing using a low cost sky scanner using a simple web camera. The developed algorithm demonstrated an average prediction accuracy of 92% and integrates and controls the blinds and electric lighting to maximise visual comfort taking into account outdoor and indoor conditions, presence and user actions. Measurements and extensive simulations showed that the elaborated algorithm improves thermal and visual comfort when compared to standard glazing coupled with blinds and exhibits acceptable levels of energy consumption for space heating and electric lighting. In the same subject of improving building control, a novel approach for controlling building systems by using state-based stochastic data-driven models to identify "season" is defined and developed. We reason that the season variable is unique to every building and it depends on weather conditions, user behaviour and building construction. The developed models identified "season" with an accuracy that ranged from 69 to 91% and it was shown through simulations that a controller based on Hidden Markov Models can reduce energy demand for heating and improve the thermal comfort of occupants in different building construction types. Finally, the use of Hidden Markov Models was further explored in this thesis by suggesting a novel model for the estimation of occupants' visual comfort in buildings. The proposed model is based on horizontal workplane illuminance measurements using ceiling-mounted sensors as well on vertical illuminance monitoring at the observer's eyes plane (pupillary illuminance) by means of wearable portable sensors. We argue that the proposed model improves greatly over the various existing discomfort glare indices and metrics and it is also convincingly demonstrated that it can be seamlessly integrated and used in building automation systems based on fuzzy logic.

EPFL2015

A monolithic MEMS position sensor for closed-loop high-speed atomic force microscopy

Jonathan David Adams, Georg Fantner, Nahid Hosseini, Adrian Pascal Nievergelt

The accuracy and repeatability of atomic force microscopy (AFM) imaging significantly depend on the accuracy of the piezoactuator. However, nonlinear properties of piezoactuators can distort the image, necessitating sensor-based closed-loop actuators to achieve high accuracy AFM imaging. The advent of high-speed AFM has made the requirements on the position sensors in such a system even more stringent, requiring higher bandwidths and lower sensor mass than traditional sensors can provide. In this paper, we demonstrate a way for high-speed, high-precision closed-loop AFM nanopositioning using a novel, miniaturized micro-electro-mechanical system position sensor in conjunction with a simple PID controller. The sensor was developed to respond to the need for small, lightweight, high-bandwidth, long-range and sub-nm-resolution position measurements in high-speed AFM applications. We demonstrate the use of this sensor for closed-loop operation of conventional as well as high-speed AFM operation to provide distortion-free images. The presented implementation of this closed-loop approach allows for positioning precision down to 2.1 Å, reduces the integral nonlinearity to below 0.2%, and allows for accurate closed loop imaging at line rates up to 300 Hz.

2016

Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices

Farnaz Forooghifar

Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces the burden on their family members. Moreover, this improvement reduces the socioeconomic consequences that are caused due to the disability of patients to work despite their health conditions. Wearable technologies offer a promising solution in pervasive health monitoring by relaxing the constraints concerning time and location. There are two major challenges in developing these technologies. The first challenge is real-time monitoring of the patients to fulfill early detection of life-threatening conditions using advanced machine learning techniques. As a solution for this issue, in this thesis, I considered a paradigm shift toward self-aware approaches and frameworks, which resulted in less energy consumption and longer battery lifetime, hence enabling ambulatory patient monitoring. I provided the possibility of switching between energy-efficient and high-performance modes in these systems. The second challenge is improving performance while guaranteeing the accuracy of the system. In this work, I have addressed this second challenge by combining multi-parametric bio-signals analysis and personalizing the detection for each patient. Throughout this thesis, I focused on epileptic seizure detection systems as a principal case study to analyze and evaluate the proposed techniques.

EPFL2022

Novel models towards predictive control of advanced building systems and occupant comfort in buildings

Nikolaos Zarkadis

EPFL2015