Behavioral Aspects of Real and Simulated Energy Consumption in Artificial Lighting Systems of Office Buildings

Abstract

Given that a considerable portion of the global energy demand is directly attributable to artificial lighting systems in buildings, an understanding of the determinants affecting their energy consumption is imperative to align current building planning practices with environmental policy objectives. Although it is already recognized that individual behaviours of building occupants exert a noteworthy impact on the energy performance of artificial lighting systems, a thorough quantification of this influence remains largely deficient. Based on a one-year, minute-by-minute monitoring of workplace occupancy, environmental conditions and energy consumption in an open-plan office, a synthetic dataset was generated to represent diverse usage scenarios. Leveraging advanced machine learning techniques, this dataset facilitated a comprehensive quantification of the multidimensional factors influencing both real and simulated energy consumption of the system. The results emphasize the critical need for an enhanced incorporation of behavioural aspects in the strategic planning of artificial lighting systems to optimize energy efficiency.