Learning lighting models for optimal control of lighting system via experimental and numerical approach

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.

Smarra, FrancescoUniversity of L'Aquila (author)
Gentile, NikoLund University,Lunds universitet,Institutionen för arkitektur och byggd miljö,Institutioner vid LTH,Lunds Tekniska Högskola,Avdelningen för Energi och byggnadsdesign,Institutionen för bygg- och miljöteknologi,Department of Architecture and Built Environment,Departments at LTH,Faculty of Engineering, LTH,Division of Energy and Building Design,Department of Building and Environmental Technology,Faculty of Engineering, LTH(Swepub:lu)arki-ngi (author)
D'Innocenzo, AlessandroUniversity of L'Aquila (author)
Ambrosini, DarioUniversity of L'Aquila (author)
Paoletti, DomenicaUniversity of L'Aquila (author)
University of L'AquilaInstitutionen för arkitektur och byggd miljö (creator_code:org_t)

In:Science and Technology for the Built Environment: Informa UK Limited27:8, s. 1018-10302374-47312374-474X

By the author/editor: de Rubeis, Tulli ...; Smarra, Francesc ...; Gentile, Niko; D'Innocenzo, Ale ...; Ambrosini, Dario; Paoletti, Domeni ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Architectural En ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Building Technol ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.