Unsupervised Learning for Lighting Control System

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2018-10-08
Department
Major/Subject
Embedded Systems
Mcode
SCI3024
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
67+0
Series
Abstract
Finding a balance point between the energy consumption and user experience is one of the most important tasks in current lighting control systems. For achieving that goal, finding resonalble group configuration of sensors/luminaires is necessary. However, most of the current lighting control systems use fixed group configuration based on empirical knowledge from human experts. This thesis uses time-series clustering, a specific method in the unsupervised learning, for exploring possible solutions of building a model that can find that balance point automatically and dynamically. For evaluating the clutering result of our model, this thesis proposes three evaluation methods specifically designed for the lighting control system, since this topic is relatively new in this industry. After testing with difierent combinations of tools, this thesis builds a model that proved to be useful for solving the problem of finding proper group configurations for lighting control system.
Description
Supervisor
Hirvisalo, Vesa
Thesis advisor
Sepponen, Laura
Jung, Alexander
Keywords
lighting control system, time-series clustering, dynamic time warping, hierarchical clustering, K-means
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Citation