Fuzzy logic model for the categorization of manual lighting control behaviour patterns based on daylight illuminance and interior layout

Abstract

In considering total building energy consumption, lighting plays an important role in shaping energy consumption and use. Although key strategies (such as energy efficient lighting products, lighting control systems and energy simulation software) are developed so far, such attempts may be unsuccessful unless users are not taken into consideration. Users’ behaviours and their manual lighting control actions depend on various factors, though within the scope of this study manual lighting control behaviour was analysed only in terms of interior layout and daylight illuminance. Three private offices in Izmir Institute of Technology were monitored using illuminance metres and occupancy/light detectors under eight different interior layout conditions. In relation to change of interior layout and daylight penetrations, users’ manual lighting control behaviours were monitored. The obtained data were then used to construct a fuzzy logic model in MATLAB FIS editor. A fuzzy logic algorithm was applied to classify behaviour patterns about the tendency to turn on the lights. This kind of prediction of the light usage tendency regarding the occupancy is aimed to foresee the ‘possible’ manual lighting control behaviour within given conditions. The gathered classification can be used further in future studies of manual lighting control behaviour and energy-saving estimations/simulations.

Stochastic Cost Modeling for Operator Allocation and Workstation Planning in Manufacturing: Optimizing Resources and Minimizing Costs

“…The research is related to applying Fuzzy logic to the machine operator allocation problem in the cellular manufacturing setting. Few researchers did utilize fuzzy logic to analyze facility layouts, working conditions, lighting effect on employee behavior, dynamic layout, and even the selection of the layout and machine allocations to estimate the downtime and Fuzzy Inference System (FIS) had been utilized to determine the machine criticality levels for maintenance activities (Kunduracı & Kazanasmaz 2019;Osuch et al 2020;Torun & Çetinkaya, 2019;Zha et al 2020).…”

Utilizing fuzzy logic controller in manufacturing facilities design: Machine and operator allocation

“…Our research is related to applying Fuzzy logic to the machineoperator allocation problem in the cellular manufacturing setting. Few researchers did utilize fuzzy logic to analyze facility layouts, working conditions, lighting effect on employee behavior, dynamic layout, and even the selection of the layout and machine allocations to estimate the downtime and Fuzzy Inference System (FIS) had been utilized to determine the machine criticality levels for maintenance activities (Assem et al, 2019;Cılasun Kunduracı & Kazanasmaz, 2019 ;De Iuliis et al, 2019;Osuch et al, 2020;Torun & Çetinkaya, 2019;Zha et al, 2020).…”