Self-learning algorithms for solar shading systems
ID
MSCA-19-VanMierlo01
Supervisors
Project description
Daylighting in offices creates a comfortable and healthy working environment for its users. Additionally, it can decrease the electricity consumption for artificial lighting. However, maximizing the amount of daylight can cause some issues. In Northern European climates, visual discomfort is the most negative side effect from windows. Also, excessive short-wave directly-transmitted solar radiation and long-wave indirectly-transmitted energy can cause thermal discomfort and an increased energy demand for cooling. To counterbalance these problems, designers implement shading systems, which control the transmitted solar and visual radiation. The control strategy should improve visual comfort by limiting glare and optimising the daylight availability on the working plane. During the cooling season, the solar heat gains are minimised and during the heating season, the solar heat gains are utilised. This methodology ensures that visual comfort is optimised and that the heating and cooling demand and the electricity consumption for artificial lighting are minimised.
The acceptance and satisfaction of the user regarding these automated strategies remains quite low. Research has shown that a single visual discomfort index cannot assure comfortable indoor environment. Therefore, a method is needed to evaluate visual comfort by focusing on the individual user. We can increase user satisfaction by letting the user override the automated control and by learning the user preferred set-point for comfort. This set-point for visual comfort will influence the cooling or heating demand and the electricity consumption for artificial lighting. A self-learning algorithm should be implemented to learn the preferences of the user and to adapt to its wishes.
Hence, the goal is to develop a self-learning control algorithm for solar shading to customise the individual occupant comfort and reduce the energy demand for heating and cooling and the electricity consumption for artificial lighting.
About the research Group
Mobility, Logistics and Automotive Technology Research Centre
MOBI is the research leader in electromobility, socio-economic evaluations for sustainable mobility and logistics. With our multidisciplinary team, we support and study the transition towards a more sustainable urban mobility and logistics system with the goal to achieve concrete and long-lasting positive socio-economic and environmental impacts. Our strength resides from our unique combination of environmental, socio-economic and technical competences, together with tools developed for the sustainable transport sector. Our research focuses on five domains: electric & autonomous vehicle technology, battery innovation, sustainable logistics, urban mobility and sustainable energy communities.