A Method to Quantify the Energy Performance in Urban Quarters

Transcription

1 Published in Journal on Indoor air quality, ventilation and energy conservation in buildings, Taylor & Francis, Vol 18:1-2, pp A Method to Quantify the Energy Performance in Urban Quarters Dilay Kesten, Aysegül Tereci, Aneta Strzalka, Ursula Eicker Centre of Applied Research - Sustainable Energy Technology (zafh.net), Stuttgart University of Applied Sciences, Schellingstrasse 24, Stuttgart, Germany ABSTRACT The study aims to derive an estimating method for the energy performance of urban quarters. To quantify the energy performance of city quarters the influence of the surrounding urban form has to be considered; for instance the street distance, height of the neighbor buildings or urban site coverage. In this work, the lighting, heating and cooling performance of urban districts with different urban forms was simulated in detail using software tools such as EnergyPlus, Radiance and Daysim. Interrelations between those effects are analyzed with respect to thermal and visual comfort condition. Electric lighting consumption is calculated first and the thermal effect of artificial lighting is set as an input to the heating and cooling performance calculation. The analyses show that the site thermal performance depends on the arrangement of the urban geometry according to solar gains and there is a correlation between energy savings and efficient urban design. The simulation results are validated with measured data from a case study in Stuttgart, Germany. INTRODUCTION Cities with their structures and organizations shape the requirements and living standards of citizens; on the other hand cities supply the energy, sanitation, water, housing and transportation requirements of their citizens with the same structures and organizations. The design or plan of those structures strongly influences the urban development and the life quality of residents. Another aspect of urban design is related with energy requirements of the buildings. According to climatic conditions the structure of the urban quarters has a big influence on the lighting, heating and cooling energy demand of the buildings. In

2 sustainable city development, less energy demand for the buildings is a first step for energy conservation and combined with green energy supply it can be the right solution for the future of the cities. The physical form and spatial structure of the cities have an influence on energy demand as well as the building design itself. The urban structure contributes to the total energy demand of a city in different ways and the effects are complex and conflicting (Givoni, B. 1998). It sometimes can bring benefits, but it can also create extra loads and undesirable effects according to the dominant climatic conditions. The influence on energy demand can be analyzed in two dimension considering daylighting and thermal behavior. Daylighting can reduce the electric lighting use and decrease heat gains due to electric lighting. Daylight utilization enhances the energy performance of the buildings while reducing total electricity load and peak demand. The total energy performance of the building is related to direct and diffuse sunlight and also reflected light from neighbor facades and the ground. The main factors that affect the daylight on buildings are listed by Santamouris and Asimakopoulos (2001) as the distance between buildings, the height of the facing building, the orientation of and the reflectance from the facing buildings, the size of openings and the size of the shading device. Littlefair (2001) provided a design tool which calculates where global horizontal radiation and illuminance reaches the buildings envelopes. Compagnon (2004) produced irradiation results to determine the viability of passive and active solar energy technologies in an urban context. The previous researches related to the modeling of urban solar availability and energy performance are mostly limited to individual aspects of energy performance, such as solar gains or daylighting performance, and rarely provide a comprehensive and validated methodology to simulate lighting, heating and cooling demand. Solar heat gains depend on the shading of the facade, the solar reflectance according to albedo of the surrounding buildings and the incident irradiation. For winter conditions more solar heat gain is helpful to reduce the heating consumption, on the other hand the shade from the surrounding is helpful to reduce the cooling load for summer conditions. For this reason, lighting, cooling and heating should be investigated together and the overall energy consumption for operating the building has to be considered. The effects caused by urban design on heating, cooling and lighting energy demand of the spaces are different and their influences are varying according to climatic conditions. Those effects are summarized in the Table 1.

3 Table 1: Urban effects on heating, cooling and lighting demand Height and Width of Buildings Heating Cooling Lighting -Multi-storey buildings and compact forms reduce the heat losses from the building envelope. -Multi-storey buildings and compact forms reduce the heat gains from the building envelope. -Heating and cooling systems might be used more efficiently in multi-storey buildings. -Shading impact increases with increasing height to width ratio causing higher artificial lighting requirement. -Daylighting performance should be evaluated considering only useful daylight illuminance levels Street Configuration Thermal and optical properties of buildings -Density of the area increases the mutual shading and reduces the solar gains. -Central or district heating and cooling systems might be used efficiently in dense areas. -Efficiency of solar heating and cooling systems decreases because of the shading effect of other buildings on the solar collecting area. -Low U-value reduces the heat losses from building envelope. -Low U-value reduces the heat gains from building envelope. -Low albedo and high absorbance of the building envelopes increases the urban heat island effect. -High density causes more daylightcontrolled artificial lighting energy consumption at lower floor levels -High reflection provides high illuminance levels and results in less electric consumption. -High visual transmission of glass provides high illuminance levels and results in less electric consumption. In this context site design according to daylighting opportunity is recognized as a useful strategy in energy-efficient building and site operation. The daylighting performance is especially significant for office buildings which are characterized by high lighting energy consumption and where the productivity of the employees is highly affected by lighting conditions. This paper presents the effect of the external urban factors which are related to the quality and quantity of natural light entering a building. The overall energy consumption of buildings in an urban context is calculated and the surrounding effect is discussed. METHODS Simulation models can be used to assess the annual energy consumption for electric lighting or the impact of daylighting on the thermal behavior of the building. The accuracy of the thermal s imulation depends mainly on the quality of the input data. Daylight simulation program validation is still important. Due to the dynamic nature of light, sudden changes of sky-sun relations cause inaccuracies in the measurements. Ashmore and Richens found 30% difference between simulation and experiments and they indicated the experimental error between 25% - 40% depending on the location of the room (Ashmore and

4 Richens, 2001). Recent studies of Mardaljevic on validation of lighting programs for illuminance modeling shows significant errors about 10% to 25% (Mardaljevic, 2004). In order to get the precise energy performance of the urban site, different urban structures have been analyzed with dimensions and site coverage ratios. In the existing literature, the building energy performance in urban context was evaluated by changing the height of the buildings while keeping the urban design density constant (Ng and Tregenza 2001, Ng 2005). As it is generally accepted for quantification of urban canopy layer assessments, the height to width ratio (aspect ratio) of the street canyon was proposed by Oke (1981). Shashua-Bar et al. (2006) and Panao et al. (2008) and used the urban geometrical ratios in their studies. To understand the relationship between urban geometry configuration changes and building energy performance, the analysis should start from room scale which is a basic space of building in an urban quarter (Figure 1.). The room should be defined all its physical properties for instance; construction details heat capacity, reflectances etcetera. Especially to examine the daylight responsive electric lighting demand in the room, daylighting analysis is necessary. With the calculation of annual hourly daylight illuminance level in the room, the electric lighting demand can be calculated according to daylight responsive control system definition. Placement of the luminaries and lamps properties plays also an important role in the energy performance of the space. When the electric lighting demand has been calculated, heating and cooling demand calculations can be done considering the thermal effects generated by artificial lighting artificial lighting. In this way, the total (heating, cooling and electric lighting) energy demand of the room is calculated. The building energy demand is obtained by adding up all rooms` energy demand in the building. Using the same approach, a complete urban site energy performance can be determined.

5 Figure 1: Analysis model for energy performance of buildings in an urban context Performing sets of experiments is important to validate the methodology chosen to assess the urban effect on the energy performance of office buildings. Simulation validation with measured Data To quantify the impact of the surrounding buildings to the energy performance of the office building in the urban context, experimental investigations were carried out in a test office. The work plane daylight illuminance as well as heating, cooling and lighting energy consumption was measured. The test space is located in an office building near the city center of Stuttgart, Germany, geographically located at 48 68'N latitude and 9 22'E longitude. Stuttgart has a moderate climate with the average temperature of the coldest month at -3 C, the average temperature of the warmest month under +22 C and about four winter months with average temperatures of at least +10 C (German Weather Service, 2011). Figure 2 represents the distribution of average durations of sunshine and the annual global

6 horizontal irradiance per square meter in Germany. The duration of sunshine varies between 1300 and 2000 hours, while the global radiation varies between 780 and 1240 kwh/m 2. a Figure 2: Average annual duration of sunshine and global radiation in Germany [Source: Renewable Energies in Bavaria, Bayerisches Staatsministerium für Wirtschaft und Verkehr] The facade of the test room is oriented towards South-Southeast (154.5 o from north). The test room dimensions are 2.31 x 5.84 x 2.08 m (width x depth x height). Floor plan and sections of test room are shown in the Figure 3 and Figure 4. N Figure 3: Site plan (test room in circle)

7 Figure 4: Floor plan and section of the test room The illuminance was measured at four points on the middle axis of the test room and the backward raytracing software Radiance was used to simulate the interior space illuminance (Ward and Shakespeare, 1998). Comparisons were done for clear and overcast sky conditions. Figure 35 and Figure 36 indicates a comparison of illuminance measurement between test room and simulation model under clear sky conditions. Deviations are mainly due to shading effects on sensors, which have a non-negligible surface area, whereas the simulation tool calculates illuminance at infinitely small points. When no direct light occurs, the deviation between measurements and simulation models is small. This means that under overcast conditions good agreement between simulation and monitoring can be achieved (Kesten, Fiedler et al., 2010). Comparing our results with previous studies on daylight simulation program accuracy, the test results seem to be in a reasonable error range.

8 Deviation Deviation Test room, clear sky: deviation between measurement and simulation values 100% 80% 60% 40% 20% 0% -20% -40% -60% -80% -100% Measurement points _ 11: _ 11: _ 11: _ 11: _ 11: _ 11: _ 12: _ 12:16 Figure 5: Deviation between measurements and simulation under clear sky conditions Test room, overcast sky: deviation between measurement and simulation values 10% 8% 6% 4% 2% 0% -2% -4% -6% -8% -10% _15: _15: _11: _11: _11: _11: _11: Measurement points Figure 6: Deviation between measurements and simulation under overcast sky conditions

9 The heating energy consumption was also measured in the test room, using measurement equipment described in details in [Strzalka et al. 2010]. The heating system of the test room was an elect rical heater, which delivered power from 0 to 2000 W. The room was equipped with temperature sensors to measure the indoor temperature of this room as well as the adjacent rooms. Also the outside temperature and global radiation were measured using the weather station on the roof of a building close to the test room. The measurements considered in this analysis were taken from 8 th (11 am) until 18 th (10 am) of February 2010, including one night break. These values were then used to validate the calculated heating energy demand. The thermal simulation parameters can be seen in Table 2. Use of Building: test room Daily profiles: Weekdays from 8 till 12 February 2010 No internal gains Air exchange rates: 0.2 h -1 Table 2: Thermal Simulation Parameters Construction details U value (W/m 2 K) Thickness (m) Exterior Wall Internal Ceiling Exterior Ground Floor Internal Walls Window Temperature set point: Monday-Friday: 19 C The comparison of the daily calculated and measured heating energy values shows reasonable agreement, also in case of the energy balance model. A dynamic simulation model was also used to compare hourly measured and simulated heating energy demand.

10 Figure 7: Comparison of hourly measured and simulated values for test room The static energy balance model calculates the required demand to reach the indoor air temperatures and therefore has a demand during night hours (Figure 7). The dynamic simulation model shows better correlation with the measurements and has been used for all further investigations. Office building energy use assessment in the urban context with dimensional ratios The real urban texture is highly complex to compute. In order to limit these complexities some archetypes were defined and these simplified types are used especially for energy use studies (Ratti et al., 2003). In this assessment two generic urban types were chosen: separated and continuous units. The separated unit, defined by geometrical ratios, can be seen in figure 6. Sixty different building configurations were analyzed, corresponding to five levels of spacing distance (L1/L2), four levels of building depth (D/L2), four levels of aspect ratios (H/W) and the cases without surrounding blocks. All simulations were conducted for all four cardinal directions i.e., N, E, S and W.

11 Sample office room Figure 8: The form structure labels (Shashua-Bar L, Hoffman ME, Tzamir Y, 2006) 1 Three dimensional urban quarter simulation was done for different generic urban form and geometrical ratios. To calculate the daylight illuminance the raytracing program Radiance was used. The Radiance parameters that are used for simulations are; ambient bounces: 5 ambient division: 1000 ambient sampling: 20 ambient accuracy: 0.1 ambient resolution: 300 direct threshold: 0 The room was also equipped with artificial lighting. The placement of the artificial lighting system in the test office and the luminary lamp properties can be seen in Figure 9. The total luminous fluxes of the lamps are 2600 lm and the efficiency is 72.8%. Each luminary power is 32 W. A daylight responsive dimming system is integrated into the model. The annual electricity consumption with a daylight responsive control system was simulated in Radiance based on the lighting program Daysim (Reinhart, 2006). 1 H, D and L2 refer to the height, depth, frontal length of each unit, L1 refers to the spacing between the units and W refers to the width of the street

12 Figure 9: Artificial Lighting System Placement Artificial lights with dimmable electronic control are added to the daylight in the room as required. Illuminance sensors of lamps are controlled according to their position in the room so that a predefined illuminance level of 500 lux is maintained in the whole working area (CEN, 2002). The sensors mounted at the luminaries measure the illuminance density of the area below. Therefore the sensor sends dimming values (0-100 %) to the control algorithm and it sets the artificial lighting level. To evaluate the overall energy demand of the building in urban context, the daylight analysis should be integrated in to the thermal analysis. In this study, the combination of Relux (artificial lighting luminaire and lamp selection and placement), Daysim (Annual daylight illuminance profiles based on local climate data) results are combined with a Microsoft Excel based evaluation tool to create the hourly electric lighting dimming schedules for thermal calculation. The heating and cooling analysis including the annual electricity consumption with daylight responsive control was carried out using the EnergyPlus simulation program (US-DOE, 2010). EnergyPlus uses the hourly electric lighting schedules to integrate daylight responsive electric lighting into thermal calculations.

13 Table 3: The simulation sets for EnergyPlus calculation Use of Building: cellular (private) plan office Daily profiles: Weekdays from 08:00 till 18:00 (without lunch breaks for internal gains point of view) People sensible gain: 150 W/person, Occupancy density:10m 2 /person, Appliances sensible heat gain: 10 W/m 2 Infiltration rate: 0.2 h -1 Ventilation air exchange rate: 0.8 h -1 Construction details U value (W/m 2 K) Thickness (m) Reflectance (%) Visible light transmittance (%) Exterior Walls Roofs Ground Floors Internal ceilings/floors Windows Heating set points time schedule: Monday-Friday 00:00-08:30 = 16 C 08:30-18:30 = 21 C 18:30-24:00 = 16 C Cooling set points time schedule: Monday-Friday 00:00-08:30 = Off 08:30-18:30 = 25 C 00:00-24:00 = Off Weekend Summer period 00:00-24:00= 16 C 00:00-24:00= Off Weekend 00:00-24:00= Off The building type is a cellular (private) plan office and the working schedule is weekdays from 08:30 AM till 6:30 PM. The envelope was designed according to the German Energy Saving Ordinance 2009 (EnEV 2009). The basic set points are described in the Table 3. The pavilion type of urban generic form which means detached block o f building is selected for evaluation and comparison. The three story reference building has 10m depth and 20m length. The reference office is located in the first floor - middle axis of the building and facing to the south. The dimensions of office are 2.5 m width, 4.5 m depth, 2.5 m height and it has 50 % windows to wall ratio.

14 Results for pavilion type office buildings The simulated annual heating, cooling and electric lighting energy demand of the south facing side -lit office room with different L1/L2 ratios were investigated in terms of total annual energy demand (Figure 10). Figure 10: The annual energy demand simulation results as a function of the spacing distance to the frontal length (L1/L2) Figure 10 shows a slight increase of heating and lighting demand (5%) with increasing site coverage, i.e. smaller distances between the buildings, and a slight decrease of cooling demand (7%). These results show that changing the L1/L2 ratio is not effective for blocking or increasing solar gains entering the space. Figure 11: The annual energy demand simulation results as a function of aspect ratios (H/W)

15 Figure 11 shows total annual energy demand of the south oriented sample office space under four different aspect ratio (H/W) scenarios. The energy demand is strongly influenced by the H/W ratio when it changes from 0.5 to 1 and increases by 20%. The total amount of energy demand slightly decreases with higher aspect ratios above 1. The increase of the heating and electric lighting energy demand is more than compensated by the decrease of the cooling energy demand. Therefore, the total energy demand is decreasing by 6%. On the other hand, the daylight responsive electric lighting demand rises from 7 kwh/m 2 to 10 kwh/m 2. Figure 12: The annual energy demand simulation results as a function of building depth to frontal length (D/L2) In Figure 12, the annual energy demand simulation results are shown as a function of building depth to the frontal length (D/L2). The annual heat demand increases by less than 2% with the influence of increasing D/L2 ratios, as the solar gains reduce. A similar tendency can be observed in the daylight responsive electricity demand. High-Rise Office building energy results High-rise office blocks with different site coverage are simulated for Stuttgart, Germany. The 10 storey reference building has 24.4 m depth, 24.4 m length and 30 m height. The energy consumption of the office room s daylight-controlled artificial lighting was evaluated for different site coverage. The required illumination level of the office room is 500 lux and the artificial lighting system was designed to supply this level.

16 When the shading effect due to the surrounding buildings is taken into account the electric lighting demand increases. The cooling energy demand is also affected by daylight responsive controlled art ificial lighting, as there is less heat gain generated by artificial lighting. In figure 13, the effects caused by external obstructions can be observed. The shading effect results in less cooling requirement due to a decrease in solar gains. The simulated annual heating demand varies from 36 kwh/m 2 to 40 kwh/m 2. At 60% site coverage, the annual cooling loads decrease to 17 kwh/m 2 which is about 36% less than at 30% site coverage. Figure 13: Heating, cooling and lighting demand of high-rise office blocks for different densities in Stuttgart climate. For the sample office room, the annual electric lighting demand was also evaluated for each site coverage percentage as a function of the floor height. On the first and fifth floor, the influence of site coverage is very important, whereas the top floor is no longer shaded by neighboring buildings (Figure 14). This height sensitivity of lighting energy demand makes urban simulations complex.

17 Figure 14: Daylight responsive artificial electric lighting demand of office blocks for different densities and different floor levels. DISCUSSION AND CONCLUSIONS Many aspects of the urban design from the layout of the roads to the building shape - significantly affect the energy performance of the buildings. On the other hand, the urban configuration has significant effects on the site energy performance. Especially the lighting electricity demand in commercial buildings with daytime operation can be decreased by the urban design strategy and daylight responsive artificial lighting system. Therefore first the possible solar gains for the site design need to be investigated and then building construction and envelope design have to be optimized by considering this analysis. This study examines the energy performance of office buildings in an urban context with different dimensional ratio in separated urban generic form. The cases deal with daylight responsive controlled electricity lighting consumption and thermal performance of buildings. The results from the simulation analysis indicate the energy performance under the impact of nearby obstructions. Within this work, the total annual energy performance of building under spacing distance to the frontal length ratios (L1/L2), building depth to the frontal length (D/L2) ratios and aspect ratios (H/W) effects were evaluated. The simulation results showed that between 4.5 % and 35 % of lighting electricity demand can be saved by the site design.

18 Combined detailed daylighting analysis and dynamic thermal simulations of high-rise office buildings show that in a moderate to cold climate, the heating energy demand increases with site coverage by about 12 % and the cooling demand drops by 36%. The daylight responsive electric lighting d emand rises from 5 kwh/m² in 30% of site coverage to 8 kwh/m² in 60% of site coverage. If no daylight responsive strategy is used, the electric lighting demand would be 36 kwh/m² which is 5 times as much in the worst case. For high rise office buildings the heating energy demand increases with the site density (from 30% to 60%) by 9% and cooling demand decreases by 36%. Lighting and thermal energy demand should be investigated together and the overall energy consumption should be optimized considering primary energy. If solar access is taken into account at the earliest planning stages, it is usually possible to ensure that the majority of buildings on a site are orientated to have good solar access for daylighting and have a reduced heating demand. For summer dominated climate conditions, the site design should focus on reducing the cooling load, but the daylighting illuminance level and the electric lighting loads should also be considered. REFERENCES Ashmore, J., Richens, Paul, Computer Simulation in Daylight Design: A Comparison, Architectural Science Review, 44, pp 33-44, (UK). Azza N., Mardaljevic J.,2006, Useful daylight illuminances: A replacement for daylight factors Energy and Buildings, Volume 38, Issue 7, July 2006, Pages Special Issue on Daylighting Buildings CEN, EN :2002, Light and lighting lighting of work places Part 1: indoor work places. Brussels, Belgium: Comite Europe en de Normalisation. Compagnon R., 2000, Assessment of the Potential for Solar Energy Applications in Urban Sites, Ecole d'ingénieurs et d'architectes de Fribourg Federal Ministry of Transport, Building and Urban Affairs, German Weather Service Givoni, B. 1998, Climate Considerations in Building and Urban Design, Van Nostrand Reinhold, United States of America Kesten,D., Fiedler, S., Thumm, F., Löffler, A., Eicker, U. Evaluation of daylight performance in scale models and a full-scale mock-up office Oxford Journals Mathematics & Physical Sciences International Journal of Low-Carbon Technologies Volume5, Issue3 Pp June 8, Littlefair P., 2001, Daylight, sunlight and solar gain in the urban environment,solar Energy,Volume 70, Issue 3,, Pages Mardaljevic, J., Verification of Program Accuracy for Illuminance Modeling: Assumptions, Methodology and an Examination of Conflicting Findings, Lighting Research and Technology, 36(3), pp , CIBSE (UK). Marta J.N. Oliveira Panãoa,,Helder J.P. Gonçalvesa and Paulo M.C. Ferrãob, Optimization of the urban building efficiency potential for mid-latitude climates using a genetic algorithm approach, Renewable Energy, Volume 33, Issue 5, May 2008, Pages Nannei E., Schenone C., 1999, Thermal transients in buildings: development and validation of a numerical model, Energy and Buildings 29, p Reinhart CF, 2006, Tutorial on the use of daysim

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