Available online at ScienceDirect. Procedia Engineering 198 (2017 )

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1 Available online at ScienceDirect Procedia Engineering 198 (2017 ) Urban Transitions Conference, Shanghai, September 2016 Assessment of the annual energy demand for cooling of buildings in their urban context in 26 cities in China Dirk Schwede*, Meiling Sheng Institute for lightweight Structures and Conceptual Design (ILEK), Stuttgart University, Pfaffenwaldring 14, Stuttgart, Germany Abstract Although microclimate effects of the urban fabric and the urban green are known, they are rarely considered in energy performance studies of buildings in their specific context. The microclimate in the urban environment is effective on human wellbeing and on the energy demand of buildings. Especially the temperatures surrounding buildings in summer and, in case these buildings are air conditioned, the resulting energy demand for cooling are effected. However microclimatic design strategies are evaluated (if at all) qualitatively or based on short periods of representative conditions, while thermal building simulation studies are usually performed with climate datasets from weather stations remote from the real urban context. This paper introduces and applies an algorithm to translate available EPW climate data into annual climate datasets containing urban microclimate effects of the specific location through multiple application of the microclimate simulation model ENVI-met. The resulting annual microclimate information is able to be applied in thermal simulation studies with TRNSYS to determine the course of the temperatures in a building over the year as well as the resulting annual cooling demand. 56 detailed 24h-simulation runs are performed to represent the climatic conditions each week of the year and four additional extreme climate situations. From the results of these ENVI-met calculations an 8760h-dataset is constructed as input for an annual thermal simulation study. The algorithm has been tested for 26 locations in China through comparison of the original dataset with a model representing a neutral site environment for calibration. It then has been applied for urban context models with green coverage. Differences in maximum temperatures as well as the impact of the urban context on the annual cooling demand are reported for the 26 locations. From these results also the CO 2 -emission reduction potentials are calculated The Authors. Published by Elsevier by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of the Urban Transitions Conference. Peer-review under responsibility of the organizing committee of the Urban Transitions Conference Keywords: microclimate simulation, urban green, cooling energy demand, China * Corresponding author. Tel.: ; fax: address: dirk.schwede@ilek.uni-stuttgart.de The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of the Urban Transitions Conference doi: /j.proeng

2 306 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Introduction The microclimate in the urban context has significant impact on human wellbeing in the cities as well as on the energy demand for building conditioning. Surface materials with high thermal absorption, high thermal capacity and low permeability will result in higher temperatures in summer, while urban green, water features and shading will reduce the temperatures effectively. Air exchange will provide relieve from thermal stress and moisture loads. All these effects influence the energy household of buildings and thereby the need for building conditioning in the urban context. Since energy demand is usually assessed in terms of annual energy demand figures, investigations of the urban microclimate and its impact on the cooling demand in buildings must be performed through long term (annual, or at least seasonal) simulation studies. Meteorological data from a local weather station, remote from the urban context, which is usually available as an annual dataset, is not representing the local urban micro climate. On the other hand, measured site-specific climate data is not available within the cost- and timeframe of a building project. Also, a dataset measured before a building is erected, would not represent the situation at the site after construction. There are simulation tools for urban microclimate simulation, which include models for the thermal and the moisture balance and which are also able to analyze the effects of urban green and other features of the urban fabric. Among these tools, the simulation model ENVI-met V3.1 [1] has been applied in a number of studies and has been assessed as applicable to investigate microclimate effects [2,3,4]. However, these tools are expensive in calculation capacity and time consuming in their application. Since an annual simulation run is not feasible, often only characteristic and extreme situations are simulated in these models. Such isolated investigations are able to show the effect, but they are not able to support the evaluation of the microclimate s contribution in the same way as the performance of technical measures, such as for example shading systems, are evaluated. In this research an algorithm has been developed and implemented in C# to connect the detailed microclimate simulation tool ENVI-met to the thermal simulation model TRNSYS and to utilize readily available EPW climate datasets [5] for the annual assessment of microclimatic effects in the urban context [6]. The procedure includes the following steps: identification of 52 days representing the climate conditions in course of the year from an EPW dataset identification of 4 days with extreme climate conditions in course of the year from an EPW dataset extreme warm day extreme cool day extreme humid day extreme dry day calculation of calibration factors for the 56 selected days, in order to adjust the ENVI-met model for the application of EPW-climate data under application of a neutral open field model performance of 56 24h-simulation runs in the urban context modeled with ENVI-met utilizing climatic conditions from the EPW-dataset and the calibration factors identified before construction of annual climate datasets: climate dataset constructed from original EPW data (CTD) climate dataset constructed from the calibration model results (CTC) climate dataset constructed from the urban context model results (CTS) In this study this process is performed for 26 climate dataset, representing the wide range of climate conditions in China. Following the CTD-datasets are compared with the CTC-datasets to evaluate the deviation in the calibration process and the quality of presentation of the ENVI-met model for a specific climate condition. The effect of the urban green on the temperature and moisture levels is then evaluated through comparison of the CTC and the CTS datasets. The energy savings between the CTC dataset and the urban context model (CTS) are reported in terms of cooling energy demand reduction [kwh c /m²a] and in terms of reduction of CO 2 -emissions [kg/m²a].

3 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Algorithm The algorithm has been developed and implemented in C# to calibrate the ENVI-met model for the climate conditions at a specific location and to construct an annual climate dataset from 56 detailed 24h-microclimate simulation results. In order to reduce the simulation expense, the procedure is performed for 52 days, taken to represent each week of the year, and for 4 days, selected to cover the extreme situations extreme hot, extreme cold, extreme humid and extreme dry. Thereby the extend of the simulation study is reduced from 365 days of the entire year to 56 selected days. For each of these days, two calibration factors are determined in together 9 simulation runs of a neutral ENVI-met model, which is intended to resemble the situation at the original site of the weather station (assumed to be located on an open field or airport, see Figure 1). The objective of the calibration is to adjust the model parameter in a way that the average temperature and the average humidity of the specific day match the respective values of the original EPW dataset. Thereby the f SH calibration factor (specific humidity adjustment) is effective on the humidity balance and the f SR calibration factor (shortwave radiation adjustment) is effective on the thermal balance and thereby the resulting temperatures in the model. The calibration is performed starting with pairs of calibration factors representing cool-humid, warm-humid, cool-dry and warm-dry conditions, respectively. Between the values of these four cases the real climate conditions are located and the calibration factors are determined. The calibration is refined in five further calibration steps to approach the solution for f SR and f SH which best fits the condition in the original dataset. The calibration factors, determined for each of the 56 days, are then applied to an ENVI-met model of the urban context to be investigated (see Figure 1). Here again 56 simulations are performed and an annual microclimate dataset is constructed for a following thermal building simulation with TRNSYS. The algorithm has been tested by comparison of the original climate dataset (CTD) and the constructed climate dataset after calibration (CTC). Deviations between these two datasets can originate from the ENVI-model used for the calibration, when the match between the original data and the simulated data has not been achieved in the systematic calibration process, or through an insufficient representation of the entire year by the 56 selected days. To reduce the impact of such deviations resulting from the calibration process and the data selection the urban context is evaluated through comparison of the climate dataset after calibration CTC and the microclimate dataset in the urban context CTS. The difference between the CTC-dataset and CTS-dataset does then only originate from the urban context model. In these two cases (CTC and CTS-model) the setting in the ENVI-met model are the same for each of the 56 days. 3. Investigation Fig. 1. left: sources of climate data; right: urban environment model The urban context investigated in this study is a typical residential neighborhood as they were built in the 1970 th in all larger cities in China (see Figure 2 left, example from Shanghai). With the increasing affluence of the residents today the public space in these neighborhoods is transformed into sealed driveways and car parking. Thereby permeable surfaces and the urban green is competing with the space for cars. At the same time air conditioning is being operated more extensively to cool the building interior to satisfy the increased demand for comfort.

4 308 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) The investigation is performed to study the impact of urban green on the air temperatures and air humidity in the open space and the resulting cooling energy demand in the adjacent buildings. The urban context is modeled as an area of 96m width, 90m depth and 36m height with a cubic cell size of 2m. The dimension of a building block is 40m wide, 10m deep and 14m high; the distance between buildings in one row is 8m, and the distance between building rows is 20m (see Figure 1 right). Fig. 2. left: Urban context investigation case, example in Shanghai Yangpu District; right: Geographical locations and climate zones of the 26 selected climate data files The urban context is modeled in ENVI-met with cyclic boundary conditions, meaning that the outflow on the one side of the model is used as inflow on the other side. This resembles the urban context influence beyond the model boundaries. The results are read out through a receptor element located 1.8m above the ground in the middle of the model. The buildings thermal behavior is simulated with Type 56 of the thermal building simulation program TRNSYS 17. The constructed climate datasets, CTC and CTS respectively, are read into the model to include the urban context. The buildings are modelled for all locations as 6 story building blocks and with the same parameters for structure, occupancy and operation. Only the U-values of the opaque outside walls are set according to the energy standard at the location of investigation. The simulations are conducted for 26 locations distributed in all climate zones in China (see Figure 2 right). Climate I(B) represents locations with severe cold conditions, such as Harbin (HAR). Climate I(C) includes Shenyang (SHN), Hohhot (HOH), Xining (XIN) and Urumqi (URU). Climate II(A) includes cold locations, such as Lanzhou (LAN), Leting (LET), Dalian (DAL), Taiyuan (TAI), Lhasa (LHA) and Yining (YIN) and climate II(B) locations like Beijing (BEI), Zhengzhou (ZHE), Xian (XIA) and Tianjin (TIA). Location with hot summer and cold winter are located in climate zone III, such as Chongqing (CHO), Wuhan (WUH), Changsha (CHA), Nanjing (NAJ), Shanghai (SHA) und Chengdu (CHE). Climate zone IV stands for hot summer and warm winter climate in Xiamen (XIA), Guangzhou (GUA), Shenzhen (SHE) and Nanning (NAN). While the mild climate in zone V is represented by Kunming (KUN). The climate conditions for the specific locations are given in Table 1 in the CTD columns, as well as through the dry bulb temperature and the absolute humidity of the CTD dataset depicted in Figures 4 and 5. The CO 2 -emission savings through the reduction of cooling energy demand are calculated under the assumption that AC-units with an average efficiency (energy class 2, SEER 5.0) are installed and that room temperatures are cooled to temperatures lower than 26 C and that the relative humidity should not exceed values of 60% in summer. The CO 2 -emissions per kwh e are then taken in reference to the energy consumption in each province in China [7]. These values consider CO 2 -emissions of energy produced locally and energy imports from other provinces. Thereby the CO 2 -savings are the result of the urban green as well the CO 2 -factor of the local energy supply mix.

5 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Results Although some deviations between the original dataset (CTD) and the results of the neutral calibration model (CTC) exist for some of the investigated climates, the calibration algorithms and the construction of annual climate datasets from a subset of simulated days (56 days out of 365 days) result in an acceptable representation of the climatic conditions. Table 1. comparison of dry bulb temperature [ C] and absolute humidity [g/kg dryair] in summer conditions (90%-percentile) and the comparison of the resulting annual cooling demand [kwh c/m²a] between the original climate data (CTD), the data generated in the calibrated neutral model (CTC) and the simulated climate in the urban context model [CTS]. Changes in the climatic conditions and energy demand between the neutral model and the urban context model. CO 2-emission for electricity use at the location [g/kwh e] and resulting CO 2-emission reduction [kg/m²a]. air temperature 90%-percentile T [ C] absolute humidity 90%-percentile H [g/kg dry air] cooling demand annual value Q [kwh c/m²a] CTC-CTS CTD CTC CTS CTD CTC CTS CTD CTC CTS ΔT ΔH ΔQ CO 2 [g/kwh e] ΔCO 2 [kg/m²a] HAR Harbin SHN Shenyang HOH Hohhot XIN Xining URU Urumqi YIN Yining LAN Lanzhou LET Leting DAL Dalian TAI Taiyuan LHA Lhasa BEI Beijing ZHE Zhengzhou XIA Xian TIA Tianjin CHO Chongqing WUH Wuhan CHA Changsha NAJ Nanjing SHA Shanghai CHE Chengdu XIM Xiamen GUA Guangzhou SHE Shenzhen NAN Nanning KUN Kunming

6 310 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Further the urban green model (CTS) shows significant reductions of the temperatures and the cooling energy demand compared with the calibration model (CTC) in the investigated locations. For comparison of the temperature [ C] the 90%-percentile is utilized in Table 1, since these values are more representative for the temperatures levels in summer than the absolute maximum values. The humidity in the model is compared in terms of absolute humidity; also here the 90%-percentile values are taken as reference to compare the three climate datasets. Figures 4 and 5 show the frequency lines of the dry bulb temperature and absolute humidity respectively for all three investigated cases CTD, CTC and CTS. Fig. 4. Comparison of CTD, CTC and CTS climate datasets, dry bulb temperature [ C], 26 locations in China Fig. 5. Comparison of CTD, CTC and CTS climate datasets, absolute humidity [g/kg dry air], 26 locations in China

7 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Calibration The comparison between CTD and CTC indicates how well the ENVI-met calibration model can resemble the original climate dataset. The match between the dry bulb temperature [ C] (90%-percentile) is shown in Figure 6 on the left and for the absolute humidity [g/kg dryair ] (90%-percentile) in the middle. The correlations between the CTD and the CTC data indicates a good functioning of the calibration process (dry bulb temperature R²=0.8326, absolute humidity R²=0.9775). The frequency lines of the dry bulb temperature in Figure 4 show that in time of cold outside temperatures the CTC dataset has the tendency to be warmer than the original dataset and that also in times with high temperatures in some cases the CTC dataset shows higher values. This is especially true for the locations Urumqi (URU), Chengdu (CHE), Guangzhou (GUA), Chongqing (CHO) and Shenyang (SHE). The frequency lines of the absolute humidity in Figure 5 show significant deviation only for the climate dataset of Urumqi. A detailed analysis of these deviations shall not be performed here Urban Green Fig. 6. Comparison of CTD and CTC - left: dry bulb temperature [ C], middle: absolute humidity [g/kg dry air] right: calculated impact of the urban green (CTS-CTC) - on the dry bulb temperature [ C] and absolute humidity [g/kg dry air], 26 locations in China, 90%-percentile values The urban green model (CTS) results in dry bulb temperature values about 4K lower and in values for the absolute humidity slightly increased compared to the calibration climate dataset (CTC) (see Figure 6 right). Large deviations are observed for Urumqi (URU) with a temperature reduction of -10.7K and a humidity increase of 3.7g/kg dryair. Shenyang (SHE) and Dalian (DAL) are the only location where the absolute humidity in the urban context model is decreased, while all other locations show an increase of the humidity levels, as expected, if plants are present in the model. Looking at the CTS model (green lines in Figure 4) it is obvious that ENVI-met does in some cases not perform well for temperatures below 0 C (the winter case). This might also result in an incorrect assessment of the summer situation, if the winter case is significant and if average or percentile values of the annual dataset are used for the comparison. The climates in the warm and hot climate regions show results more consistent with the expectations. The cooling energy demand is in compliance with the reduced temperature levels significantly decreased in all of the 26 locations (see Figure 7 right). While in warm and hot locations the temperature reductions are translated into energy savings for building conditioning in summer mostly between 10 and 25%, in the cool and cold climate regions the urban green show higher saving percentages and might result in outdoor and indoor temperatures perceived to be comfortable without mechanical cooling. In these cases, urban green could prevent conditioning equipment from being installed. This would further reduce investment cost, resource demand for the AC-unit production, maintenance demand, noise and not at least the refrigerant loads on the environment during operation. In all cases the reduced energy demand for cooling results in significant reduction of CO 2 -emissions and an improvement of the outdoor comfort conditions in the public space.

8 312 Dirk Schwede and Meiling Sheng / Procedia Engineering 198 ( 2017 ) Fig. 7. left: energy demand for cooling [kwh/m²a] from thermal building simulation with various data sources, 26 locations in China right: cooling energy demand saving [%] in correlation with the outside dry bulb temperature [ C], 90%-percentile (CTD: original EPW climate data, CTC: climate data after model calibration, CTS: climate data with urban context) Conclusion The study presented in this paper shows that significant energy savings for cooling and in consequence CO 2 - emission reduction can be achieved through plants and green in the urban context. In warm and hot climates savings are as high as 10-25%, while in cooler and moderate climates higher saving percentages can be achieved. In these cases, the remaining cooling demand and the noticeably reduced temperature levels might prevent cooling equipment from being installed at all. Which would have significant environmental benefits, such saving of materials for AC-units, noise reduction and emission of refrigerants to the environment. Also the architectural appearance would be improved, if AC-units do not need to be integrated into the building envelope. Furthermore, the research presented in this paper provides an algorithm able to connect the detailed microclimate simulation model ENVI-met with the thermal simulation program TRNSYS (or any other thermal simulation model able to read a comma separated climate dataset) The algorithm is able to translate available climate datasets from locations remote to the urban context into microclimate datasets relevant for the inner city situation of a specific project. It thereby enables to calculate the cooling energy demand of buildings in their urban context. Acknowledgements The investigation has been conducted in the context of the Robert Bosch Juniorprofessur Nachhaltiges Bauen at the Institute for Lightweight Structure and Conceptual Design (ILEK, chair: o. Prof. Dr.-Ing. Dr.-Ing. E.h. Dr. h.c. Werner Sobek) at the University of Stuttgart and has been supported by the Robert Bosch Foundation. References [1] M. Bruse, ENVI-met 3. (last access on ) [2] Orehouning, Kristina, Kristina Kiesel, and Ardeshir Mahdavi Derivation of locally adjusted high-resolution weather information for building performance simulation. In: Proceedings of BauSim2012, Berlin, Germany, September [3] Ali-Toudert, Fazia Dependance of Outdoor Thermal Comfort on Street Design in Hot and Dry Climate, Berichte des Meteologischen Institutes der Universität Freiburg, Freiburg, November 2005, ISSN X. [4] Huttner, Sebastian Further development and application of the 3D microclimate simulation ENVI-met, Dissertation at University of Mainz, [5] US DOE, EnergyPlus Energy Simulation Software Weather Data, (last access on ) [6] D. Schwede. Simulation des Jahres-Gebäudeenergiebedarfs unter Einfluss des Urbanen Mikroklimas. In: Proceedings of BauSim2014, Aachen, Germany, September [7] C.-M. Ma, Q.-S. Ge, Method for Calculation CO 2 Emissions from the Power Sector at the Provincial Level in China, Advances in Climate Research 5(2): 92-99, 2014.