| 1. |
- de Rubeis, Tullio, et al.
(författare)
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A novel method for daylight harvesting optimization based on lighting simulation and data-driven optimal control
- 2020
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Ingår i: Proceedings of Building Simulation 2019: 16th Conference of IBPSA. - : IBPSA. - 9781775052012 ; 16, s. 1036-1043
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Konferensbidrag (refereegranskat)abstract
- To date, the best daylighting assessment technique is provided by climate-based simulation tools, which require remarkable efforts to create and calibrate realistic models. The data-driven approaches represent an interesting opportunity to support the physics-based modelling. This work proposes a novel method aimed at the optimization of energy use and luminous environment for a set of lighting control system solutions. The method processes experimental data of occupancy and lighting switch on/off events of an individual side-lit office in an academic building at high latitude via DIVA4Rhino; then, the climate-based simulation results provide the data necessary for the data-driven static optimal control that allow different control strategies of the lighting systems according to their lighting power density. The control allows optimal strategies giving priority to either energy saving or luminous environment improvement, depending on the energy efficiency of the lighting installation, while guaranteeing comfort base level. The results show that the method allows to achieve energy savings up to 18.6% by maintaining high visual comfort levels.
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| 2. |
- de Rubeis, Tullio, et al.
(författare)
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Learning lighting models for optimal control of lighting system via experimental and numerical approach
- 2021
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Ingår i: Science and Technology for the Built Environment. - : Informa UK Limited. - 2374-4731 .- 2374-474X. ; 27:8, s. 1018-1030
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Tidskriftsartikel (refereegranskat)abstract
- Lighting control systems have been traditionally employed to reduce energy use for lighting by, for example, maximizing daylight harvesting. When highly efficient light sources are installed and for tasks where maintaining target illuminance is particularly important, designers may decide to prioritize the latter together with energy use. In this context, the use of data-driven algorithms is emerging. In this paper different data-driven approaches are proposed as lighting control systems, to maximize daylight harvestingand to optimize energy consumption. The approaches employ experimental data of occupancy and lighting switch on/off events of a private side-lit office in an academic building. The office is later modeled in DIVA4Rhino to provide yearly illuminances and electric lighting dimming profiles. These data are used to implement data-driven optimal controls. Three different approaches have beenemployed: Regression Trees; Random Forests; Least Squares. Different lighting control strategies have been hypothesized based on installed Lighting Power Densities (LPD). Results show that Regression Trees outperforms both Least Squares and Random Forests, in terms of model accuracy and control performance.
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