Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Saving energy by maximising daylight and minimising the impact on occupants: An automatic lighting system approach
Graphical abstract Display Omitted
Highlights Definition of a state-of-the-art DL/EL automatic system based upon surrogate models. Thisapproach allows to compute KPIsfast enough for quasi-real time system update. A user experiment (N=60) assessed the comfort and performance provided by our system. Our system reduced lighting energy while keeping occupants' comfort and performance.
Abstract Electric lighting energy expenses has gained importance in the last decades representing a large part of the global energy expense worldwide. The increase of the proportion of glazed building facades is an incomplete solution raising other problems regarding thermal and visual comfort, while rebound effects in terms of lighting usage lowered the energetic benefits of LED technology. Another strategy to address the issue of energy is the well-known strategy of the re-introduction of task lights. The current study presents a system arbitrating the contribution of daylight with general and task electric lighting from the prediction of key performance indicators (work plane illuminance and daylight glare probability) by a machine learning model. It aims at reducing energy consumption while minimizing the impact on occupants’ comfort and productivity. This system was validated by the mean of a controlled user experiment with 60 participants. Altogether, our data revealed that RADIANCE simulations model accurately predicts on-site monitoring () and that the machine learning model accurately predicts RADIANCE simulations for both work plane illuminance () and daylight glare probability () allowing to operate the automated system. Data from the user experiment shows that our system successfully saves 35 Wh on average compared to manual operation of the room by users, by making prior use of daylight and task lighting. This represents more than halving the energy demand. Importantly, the automation impacted neither users’ comfort or task performance. System acceptance constructs such as system reliability or trust in the system were found to be linked with an attribution of the locus of control to powerful others. This result suggests that the key towards high user acceptance might lie in purposefully addressing this dimension. Altogether, the presented results support the potential for energy saving through the development of user-centred lighting automation.
Saving energy by maximising daylight and minimising the impact on occupants: An automatic lighting system approach
Graphical abstract Display Omitted
Highlights Definition of a state-of-the-art DL/EL automatic system based upon surrogate models. Thisapproach allows to compute KPIsfast enough for quasi-real time system update. A user experiment (N=60) assessed the comfort and performance provided by our system. Our system reduced lighting energy while keeping occupants' comfort and performance.
Abstract Electric lighting energy expenses has gained importance in the last decades representing a large part of the global energy expense worldwide. The increase of the proportion of glazed building facades is an incomplete solution raising other problems regarding thermal and visual comfort, while rebound effects in terms of lighting usage lowered the energetic benefits of LED technology. Another strategy to address the issue of energy is the well-known strategy of the re-introduction of task lights. The current study presents a system arbitrating the contribution of daylight with general and task electric lighting from the prediction of key performance indicators (work plane illuminance and daylight glare probability) by a machine learning model. It aims at reducing energy consumption while minimizing the impact on occupants’ comfort and productivity. This system was validated by the mean of a controlled user experiment with 60 participants. Altogether, our data revealed that RADIANCE simulations model accurately predicts on-site monitoring () and that the machine learning model accurately predicts RADIANCE simulations for both work plane illuminance () and daylight glare probability () allowing to operate the automated system. Data from the user experiment shows that our system successfully saves 35 Wh on average compared to manual operation of the room by users, by making prior use of daylight and task lighting. This represents more than halving the energy demand. Importantly, the automation impacted neither users’ comfort or task performance. System acceptance constructs such as system reliability or trust in the system were found to be linked with an attribution of the locus of control to powerful others. This result suggests that the key towards high user acceptance might lie in purposefully addressing this dimension. Altogether, the presented results support the potential for energy saving through the development of user-centred lighting automation.
Saving energy by maximising daylight and minimising the impact on occupants: An automatic lighting system approach
Papinutto, Michael (Autor:in) / Boghetti, Roberto (Autor:in) / Colombo, Moreno (Autor:in) / Basurto, Chantal (Autor:in) / Reutter, Kornelius (Autor:in) / Lalanne, Denis (Autor:in) / Kämpf, Jérôme H. (Autor:in) / Nembrini, Julien (Autor:in)
Energy and Buildings ; 268
11.05.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
0000 , 1111 , CCT , Correlated Color Temperature , DGP , Daylight Glare Probability , DL , Daylighting , EL , Electric Lighting , KPIs , Key Performance Indicators , ML , Machine Learning , WPI , Work Plane Illuminance , Electric lighting , Surrogate models , Automatic control , Energy savings , User experiment , Psychological assessment , Visual comfort
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