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Parametric Analysis

Updated: Jan 23, 2023

Optimizing daylight and electric light to reduce energy use and carbon footprint.

A parametric study for a simple cell office in Stockholm, Sweden, was carried out varying several parameters that influence both the daylight and electric light levels. The aim of the study was to evaluate the effect of these parameters on the energy needed for heating and cooling, the primary energy, the energy needed for electric lighting to reach the specified illuminance threshold, and the resulting carbon emissions. This was carried out using a dynamic energy simulation, that accounts for the changes in daylight levels and solar irradiation, which is a more accurate evaluation of the effect of various parameters for electric light, instead of primarily calculating electric energy savings based on light power density or reducing the number of luminaires in a building. Since the simulation was done for a simple cell office room, the aim was not to compare the results with local Swedish regulations (BBR), since the results generated on a single room would not be comparable to a complete building that will have different internal gains and different occupancy profiles. Instead, the aim of this study was to compare the interconnected effects of various parameters and the associated differences in the results.


Optimizing daylight and electric lighting in buildings is essential for creating sustainable and healthy environments. It has been identified that electric lighting accounts for about 15% of the world's total electricity use [1] and approximately 5% of global Co2 emissions [2]. In addition to that, in Sweden, electricity for lighting accounts for 9% of Sweden's total electricity consumption [3].

Research has shown that LED lighting reduced the carbon emissions in 2017 by 570 million tons of carbon dioxide [4], however this study only considered the effect of changing more inefficient light sources to LED, and did not account for the overall increase in using artificial light between 2012 and 2016, which was an annual increase of 2.2% globally [5]. Reducing the need for electric lighting in buildings is therefore a crucial aspect in reducing the electricity demand, and the carbon footprint in turn. To achieve this, it is neccessary to optimize the built environment starting from the city level, to district and then to building level by finding better synergies between daylight availability, electric lighting and lighting controls.


Seven parameters were evaluated with 28 different values, which resulted in a total of 4800 simulations. Despite the large number of simulations, it was decided not to optimize the outcomes (by performing a genetic optimization using Grasshopper plug-ins in Rhino such as Galapagos), which would have given results showing the optimum forms with minimum energy use and minimum carbon footprint. The reason for this was to have the opportunity to compare the simulations and to be able to reach generalized conclusions that can be compared with other case studies afterwards. The values for the different parameters were chosen based on best practice. In addition, according to local regulations in Sweden, it is recommended to have a minimum window to floor ratio of 10%, which is equivalent to 20% window to wall ratio in this case.

Summary of examined parameters in the parametric case study

EXAMPLE OF RESULTS (additional results are available inside the PDF below)

Changing Window to Wall Ratio (WWR) and dimming type

The graph below shows the effect of changing the WWR in relation to the three different dimming types evaluated in this case; no dimming, stepped dimming, and continuous dimming. In the case of stepped dimming, electric lighting turns on/off from zero to 100% when the illuminance from daylight is below the threshold, while the continuous dimming continuously adapts to the exact amount of light needed to reach the illuminance threshold.

The lowest energy consumption was observed when using the continuous dimming, with an increase of 11% in the total energy use, and 4% in the carbon footprint when increasing window to wall ratio from 40% (MDF 0.9-1.2%) to 60% (MDF 1.5-1.9%), because increasing the WWR increased the energy needed for cooling, which in turn increased the total energy use and the carbon footprint. At 60% WWR, energy savings between the stepped and continuous were 4% in comparison to no dimming. Generally, using continuous dimming yields best results, but since the illuminance threshold in this case was already 500 lux, which can be considered high, the difference between the continuous and the stepped dimming system when it comes to saving more energy than the other was small. It was also observed that at 60% WWR the carbon footprint was reduced by 14% when having a continuous dimming system and by 8% when having a stepped dimming system in comparison to not having a dimming system


To further simplify the conclusions from the study, the following table shows the effect of changing each parameter separately on both the total energy use and the carbon footprint. Similar to the previous table, the biggest difference in the total energy use and carbon footprint was when comparing levels 0 and 2. This was followed by optimizing the WWR from 60% to 40%. Furthermore, changing the dimming type to continuous dimming did not result in high energy savings, which would be different when considering an entire building in reality, yet it still showed a potential for reducing the carbon footprint up to 8%. This is because the energy for electric lighting was not included in total energy use, so the savings in total energy use only represented the reduction in the energy use for cooling, while the carbon foot print also included the savings in energy use for electric lighting, as shown in the diagram below.

The table will be used to communicate the possible savings in the total energy use and the carbon footprint to different stakeholders, including architects and engineers.


After analyzing the results, there were several limitations that were observed in this study:

- The simulation was carried out for a simple room, therefore the difference in the results are marginal, which results in a high percentage when calculating the increased or decreased differences. The results should be further validated by carrying out a study on a bigger scale, i.e an entire building.

- Since the study was performed on a simple room, the results for the total energy use were not compared with the local regulations in Sweden (BBR), so it is unclear whether a full office building with the same parameters used in this study would meet the BBR requirements or not.

- The evaluated distances to the surrounding context was considered big compared to a dense city scape, which meant that in some cases there was not a huge difference in the results when changing the distance.

- Examining the efect of dimming systems was only based on the illuminance thresholds, and did not consider the occupancy behaviour, which could be a potential strategy to further reduce the electric energy use in lighting. However this is a shortcoming in research in general, and needs further examinations in the lighting research field.


In conclusion, when using a dynamic simulation and considering both the heating and cooling loads in a room, in some cases both loads balance each other when doing such a parametric simulation and looking at annual results, i.e the heating loads for example decreases due to higher sun exposure while the cooling loads increases. This would result in similar primary energy use as the electricity for lighting is not included. However all the results in this case study showed that the carbon footprint changes as the energy used for electric light increases or decreases as this was included in the carbon footprint calculations. In addition to that, internal heat gains from electric lighting have a big contribution towards the energy used for heating and cooling the building. This shows the importance of evaluating the energy loads from electric lighting when designing a building and analyzing the actual total carbon footprint of the building including the electricity used for lighting to make better and more sustainable design choices at early stages.

Furthermore, it is important to consider the correct representation of electric lighting system in the building, as it affects the total energy use in the building. Using a high LPD (as recommended by Ljus & Rum 4th edition and SVEBY, version 1.1), can actually decrease the total energy use as the internal heat gain from electricity is high, however, it results in a higher carbon footprint, which is currently not being accurately included in the carbon assessments in Sweden. The results show that by optmizing different parameters in the room geometry, and adding a dimming system, total energy use can be reduced by 2%-24%, and the carbon footprint can also be reduced by 4%-12%.

Read the full report of the Parametri Analysis inside our final report, starting on page 66:


[1] UNEP. 2017 Accelerating the Global Adoption of Energy-Efficient Lighting Paris p 4

[2] Climategroup. (2020). Climategroup. Retrieved from LED:

[3] Energimyndigheten 2018 Belysningsutmaningen p 6, 15, 32

[4] Anthropocene. (2018). Anthropocene. Retrieved from: LEDs saved a half billion tons of carbon dioxide emissions last year. Or did They?:

[5] Christopher C. M. Kyba et al. Artificially lit surface of Earth at night increasing in radiance and extent. Science Advances. 2017

[6] Gentile, N. (2017). Lighting Control Systems to Save Energy in the non-Residential Sector, State-of-the-art, Field Studies, and Simulations. Sweden: Lund university

[7] Levin, T. (2017). Daylighting in environmentally certified buildings, subjective and objective assessment of MKB Greenhouse, Malmö, Sweden. Sweden: Lund University.

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