Abstract
This paper will continue application of neural networks to the concept of environmental quality management technology described elsewhere. Human preferences are different and may vary depending on type of the work or psycho-physical conditions of workers. This paper deals with application of artificial neural networks (ANNs) to control of general lighting systems to provide personal (individual) illuminance on worktables. Two-layer feedforward ANN is used to identify and model the system. Introduction of ANN to model illumination systems opens a new possibility of individual control systems in which specific areas have different set of requirements than remaining areas of a lighting system.
Highlights
It is estimated that lighting is responsible for 20-40% [1] of total energy consumption in buildings
[7] shows two artificial neural networks (ANNs) models and compares their performance with optimization based on a linear model
* Corresponding author: mdechnik@pk.edu.pl https://doi.org/10.10 51/matecconf /201928202069 an ANN model of a lighting system composed of 6 lamps and 6 desks was considered
Summary
It is estimated that lighting is responsible for 20-40% [1] of total energy consumption in buildings. Artificial neural networks (ANN), which are one of the AI methods, have been used to identify and model illumination systems in several papers. Their use was considered in residential buildings in relation to residents’ behavior patterns [4] or in classrooms [5]. An office room lighting system with independently controlled dimmable LED luminaires was considered. Local lighting control on the level of the desk introduces new concept of quality, enabling the adjustment of lighting conditions to individual preferences of people, as well as optimizing the energy efficiency of the lighting system [14]
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