Abstract
This study evaluates the calculation approach of the energy requirements for artificial lighting inside buildings of different use according to EN15193-1:2017, defining the main scope of the standard, highlighting its limitations, and proposing improvements. The evaluation was carried out through a parametric analysis to determine the influence of window-to-wall ratio, distribution of windows, presence of side opening, glazing visible transmittance, and overhang length on the calculation of the Lighting Energy Numeric Indicator (LENI) for a living room and an office at four representative locations (Bratislava, Stockholm, London, Athens). The standard was tested against DAYSIM, a Radiance-based simulation tool for calculating daylight availability, whose results were post-processed to obtain the energy requirements for artificial lighting. For many windows close to each other, the standard's approach to superimpose the daylight factors (DF) for overlapping daylit areas led to an overestimated total DF and therefore an underestimated LENI. For rooms with low window-to-facade and window-to-wall ratio, the standard's calculation was inaccurate. The daylight supply factor tabulated in the standard was too low for latitudes below 45°, leading to an overestimation of the LENI. For latitudes above 60°, the opposite effect was observed. Summarising, the standard underestimated the LENI by about 10% on average.
Highlights
Lighting in commercial buildings accounts for up to 45% of overall electricity demand, with significant variation from one building to another [1]
To be consistent with the daylight factor (DF) estimation as per standard, we evaluated with Radiance considering only daylit areas
In line with the parameterization for the DF estimation shown in Fig. 3.2, each graph can be subdivided into four groups according to the variation of the south facade window-to-wall ratio (WWR)
Summary
Lighting in commercial buildings accounts for up to 45% of overall electricity demand, with significant variation from one building to another [1]. Electric lighting can provide substantial energy savings with the introduction of reasonable investments [2]. Tools for lighting simulation in buildings have become a promising and widely-used method by designers for lighting energy analysis in order to identify the most suitable energy saving options [4]. Their use is challenging because they require a detailed representation of the real environment. This leads to time-consuming model design and long computation time in case of complex geometries [4]. Setting up simulations requires very specific knowledge, and the user interface may not be user-friendly [4]
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