Toward an Integrated Platform for Energy Efficient Lighting Control of Non-Residential Buildings

Net energy import of Switzerland corresponds to about 13 BCHF (petroleum, uranium, etc.) each year. According to the energy perspective 2035 defined by Swiss Federal Office Energy, the energy import should be reduced to 7.3 BCHF. Meeting the goals defined by this perspective can be envisaged by mitigating the energy demand of buildings since the built environment consumes 40% of the total primary energy. In Switzerland; 20 % of the buildings’ energy consumption is moreover related to electric lighting. Therein, integrated building automation technologies, such as automatic regulation of sun shadings and electric lighting can play an important role in reducing lighting energy demand while maintaining user comfort. For this purpose, a rapid, reliable, accurate evaluation of the lighting conditions in a building is essential. In this paper, a control platform installed in two offices of the LESO experimental building on the EPFL campus in Lausanne, Switzerland is presented. This data acquisition and actuation platform hosts two motorized sun shadings per office, a dimmable lighting system, a presence detector, energy meters, ceiling mounted luminance-meters and two novel High Dynamic Range (HDR) vision sensors. A sample controller is implemented in the platform to evaluate its robustness and performance. This platform is currently ready for integrating novel controllers for testing several energy efficient control scenarios/algorithms.

On the integration of Non-Image-Forming effects of light on venetian blinds and electric lighting control

Marta Benedetti, Laurent Deschamps, Ali Motamed, Jean-Louis Scartezzini

Light induces important “Non-Image-Forming” (NIF) effects on human physiology and behavior, such as improvement of alertness, cognitive performance and mood. Considering these effects in office lighting can improve well-being and productivity of the occupants. This study introduces and evaluates NIF effects of light on humans within a Building Automation System of an office environment. Real-time luminance mapping is assessed by novel HDR vision sensors; the monitored data is used for a smart user-centric controller of the indoor electric lighting and sun shadings. A preliminary evaluation of this advanced controller was carried out during one week in a single-occupied office in the LESO solar experimental building at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland. The system demonstrated to have sound capabilities in satisfying visual comfort conditions and well-being for the office's occupant.

Elsevier2017

Integrated Daylighting and Artificial Lighting Control based on High Dynamic Range Vision Sensors

Ali Motamed

One fifth of the electricity consumption of Swiss buildings is due to electric lighting. Integrated control of sun shading and artificial lighting can mitigate this demand while maintaining user comfort. However, the drawback of existing building control approaches is that they do not consider one of the main aspects of human-centric lighting: visual comfort. The goal of this doctoral thesis is to develop an integrated energy efficient sun shading and electric lighting control system that incorporates widely accepted visual comfort criteria and privileges daylighting over electric lighting. The first part is dedicated to High Dynamic Range (HDR) vision sensor calibration, programing, validation and preliminary testing. A sensor originally developed by the Centre Suisse dâElectronique et de Microtechnique (CSEM) was photometrically, spectrally and geometrically calibrated and validated with respect to reliable illuminance and multi-point luminance meters. This HDR vision sensor was then equipped with an embedded image processing routine in order to assess âon the flyâ discomfort glare indices. It has been demonstrated that the developed device, is able to serve as an enhanced visual comfort feedback sensor in building automation systems. On the other hand, it can be employed to characterize highly glazed facades and workspaces regarding visual comfort and glare risks, as demonstrated in a project in Singapore. Two monitoring campaigns are reported in the second part of this thesis. Firstly, 30 human subjects occupied two identical office rooms of the LESO solar experimental building for 15 afternoons to compare the performance of a fuzzy logic control system incorporating two HDR vision sensors with respect to a âbest-practiceâ controller. Subjective self-reported visual comfort surveys, paper- and computer-based visual tests and monitoring of the electric lighting consumption were carried out simultaneously in both offices. It was shown that the electricity demand of the office with the advanced controller is 32% lower than that of the reference room, while the subjectsâ visual performance remained comparable. Secondly, an eight-month data monitoring campaign was carried out in the same building in order to study the ability of a novel control approach to maintain optimal visual and thermal comfort conditions while reducing the energy performance gap of a room as well as its electric lighting demand. The experimental results showed that the advanced controller mitigated the performance gap during the heating season by 72% with regard to standard occupant behavior and by 19% with respect to a best-practice automated system. This system reduced backup heating demand leading to lower CO2 gas emissions. At the same time, visual comfort constraints regarding Daylight Glare Probability (DGP) and workplane horizontal illuminance were respected during work hours. Finally, a self-commissioning integrated controller for Venetian blinds enhanced with a learning module was developed and validated for 22 days in a daylighting testbed at the Fraunhofer Institute for Solar Energy (ISE) in Freiburg, Germany. It has been shown that the visual comfort constraints are respected for 96% of the work hours and that the controller can successfully limit the number of shading movements. The market potential for HDR vision sensors and integrated control platforms has been studied and possible commercialization tracks have been identified.

EPFL2017

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.