A novel method for daylight harvesting optimization based on lighting simulation and data-driven optimal control
(2020) 16th IBPSA 16. p.1036-1043- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2168aab2-30be-4782-b1b9-a0d8fe89272f
- author
- de Rubeis, Tullio ; Gentile, Niko LU ; Smarra, Francesco ; D'Innocenzo, Alessandro ; Ambrosini, Dario and Paoletti, Domenica
- organization
- publishing date
- 2020-03-20
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- visual comfort, machine learning, Data-driven control, lighting control system, lighting, daylighting, Daylighting simulation, energy saving, energy efficiency, control strategies
- host publication
- Proceedings of Building Simulation 2019: 16th Conference of IBPSA
- editor
- Corrado, Vincenzo ; Fabrizio, Enrico ; Gasparella, Andrea and Patuzzi, Francesco
- volume
- 16
- article number
- 210494
- pages
- 5112 pages
- publisher
- International Building Performance Simulation Association (IBPSA)
- conference name
- 16th IBPSA
- conference location
- Rome, Italy
- conference dates
- 2019-09-02 - 2019-09-04
- ISBN
- 978-1-7750520-1-2
- DOI
- 10.26868/25222708.2019.210494
- project
- Högeffektiva belysningssystem för användardriven energibesparing
- language
- English
- LU publication?
- yes
- id
- 2168aab2-30be-4782-b1b9-a0d8fe89272f
- alternative location
- http://www.ibpsa.org/proceedings/BS2019/BS2019_210494.pdf
- date added to LUP
- 2019-05-28 11:43:46
- date last changed
- 2020-04-14 12:08:09
@inproceedings{2168aab2-30be-4782-b1b9-a0d8fe89272f,
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.}},
author = {{de Rubeis, Tullio and Gentile, Niko and Smarra, Francesco and D'Innocenzo, Alessandro and Ambrosini, Dario and Paoletti, Domenica}},
booktitle = {{Proceedings of Building Simulation 2019: 16th Conference of IBPSA}},
editor = {{Corrado, Vincenzo and Fabrizio, Enrico and Gasparella, Andrea and Patuzzi, Francesco}},
isbn = {{978-1-7750520-1-2}},
keywords = {{visual comfort; machine learning; Data-driven control; lighting control system; lighting; daylighting; Daylighting simulation; energy saving; energy efficiency; control strategies}},
language = {{eng}},
month = {{03}},
pages = {{1036--1043}},
publisher = {{International Building Performance Simulation Association (IBPSA)}},
title = {{A novel method for daylight harvesting optimization based on lighting simulation and data-driven optimal control}},
url = {{http://dx.doi.org/10.26868/25222708.2019.210494}},
doi = {{10.26868/25222708.2019.210494}},
volume = {{16}},
year = {{2020}},
}