POTROŠNJA ENERGIJE U STAMBENOM FONDU BEOGRADA PART 1: MODELING AND SIMULATION OF BUILDINGS AND HOUSES FROM REPRESENTATIVE SAMPLE

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1 POTROŠNJA ENERGIJE U STAMBENOM FONDU BEOGRADA PART 1: MODELING AND SIMULATION OF BUILDINGS AND HOUSES FROM REPRESENTATIVE SAMPLE ENERGY CONSUMPTION IN BELGRADE HOUSEHOLDS SECTOR PRVI DEO: MODELIRANJE I SIMULACIJA ZGRADA I KUĆA IZ REPREZENTATIVNOG UZORKA O. Ećim-Đurić*, M. Kavgić***, V. Turanjanin**, B. Vučićević**, Ž. Stevanović** and M. Jovanović** University of Belgrade, Faculty of Agriculture, Nemanjina 6, Belgrade * Institute of Nuclear Sciences VINČA, Laboratory for Thermal Engineering and Energy, PO Box 522, Belgrade ** Partner Inženjering, Belgrade, Ilije Garašanina 26 28, Belgrade *** Abstract: In this paper is presented heat consumption optimization of Belgrade residential buildings. Whole residential building fund was divided into eight groups according to the year of construction and type of building. By numerical simulation it was investigated heat consumption of building zero model which represented buildings current state. Numerical simulations were performed for typical metrological year for given location for 8760 hours during the year. In calculation it was taken in account building surroundings, outside heat transfer coefficient which is depended of local weather conditions and inside heat transfer coefficient according to the type of walls. Based on building zero model, advanced building models were developed where was investigated influence of insulation and windows on heat consumption. Model was validated with data for every building type. Aim of this study is establishing of real heat consumption database and indication on possible energy consumption reduce and improvement of building energy efficiency. Keywords: energy consumption, numerical simulation, optimization energy efficiency 1. INTRODUCTION Excessive use of fossil fuels for heating in residential and commercial building sector leads to its irreversibly spending. Main consequence of this is greenhouse gases emissions which strongly affects on climate change, and on the other hand, even more energy consumption in building sector, both for heating and air-conditioning. Optimization of energy consumption is complex problem and it takes in account many parameters that in ultimate goal do not have only reduction of energy consumption but also the environmental impact. Optimization can also lead to replacement of nonrenewable fuels with renewable for heating purpose such as biomass, geothermal energy, solar energy etc. When thermal comfort is being analyzed, inside temperature is main parameter for rating. Many parameters influences of inside temperature forming. Some of them are building position and orientation, local metrological data, building envelope, room position and orientation inside the building, lighting, appliances, sensible and latent heat of people present in occupied space etc. All of these parameters are strongly connected, and they have to be taken in account in heat load calculating [1]. Standard heat load calculation is determined by the maximum heating load

2 according to the outside temperature minimum for given location which in practice, over most heating period gives system oversize. Method is based on heat load calculation for set up inside temperature assuming that main factor for inside temperature formation is heat transfer trough the walls. It is assumed in this method that same amount of heat has to be distributed to the building in every design hour during the heating season. For buildings connected to the district heating this is unfortunately true, but due to many other reasons in many buildings designed temperature (20 C) is not archived. Some of these reasons lies directly in building such are old windows and doors or totally absence of insulation material. In houses with their own heating system it is very difficult to determine inside temperature because this data is set up mainly by the subject feeling of inhabitants. In this situation heating system, what ever it is electrical energy, gas or coal, is set up to desire temperature which is more than previously mentioned, or in some other cases less due the same reasons. As opposed to this method, numerical simulation of building thermal behavior gives more precisely heat, and if it is necessary also cooling load, based on real outside weather conditions and parameters mentioned above [6]. Calculating thermal load for hourly time step during the year, heat accumulation is also included unlike classical design. This kind of calculation, treat building as a complex system and takes in account also some parameters that are not included in classical heat calculation, but there are present in cooling load calculation. Examples for this are internal load like lighting, electrical appliances present in house, presence of people. From the other side, by this method it can be detected periods (days or hours) where distributed heat energy is not enough, and consequence of this is excessive use of electrical energy for space heating. All these problems lead to main question: Is it possible to design heating system in real time, regardless to heat source, and is it possible to follow this system during the heating system in such a way of energy efficiency improvement and decreasing of greenhouse gases emission? Answer on this question is very complicated, but according to previous studies where numerical simulation was base for building thermal behavior it is show that by this way is possible to get more precise building energy consumption data and thermal comfort data in the same time. Scope of this investigation is properly determination of present heat energy needs in residential sector. Improved building models easily can be obtained starting from that point and energy consumption could be decrease. Even in second steps as are improved building models, numerical simulation is very comfort tool for energy consumption and thermal comfort predicting in early building design stage, or as a scenario in old building renovation. 2. SETTING UP BUILDING MODEL In this paper is analyzed Belgrade residential building sector, and numerical simulations were performed in purpose to determine heat consumption during the winter period. Residential building sector was divided into 9 groups, and total area of every group is given in Table 1 according to official statistic from Table 1. Residential building sector in Belgrade by year of construction Area [m 2 ] Year of construction Multi apartment buildings Single family house to

3 From every group several buildings were selected for analysis. The only group that has not been taken in consideration in this analysis was group of building before 1946., because in this group it was difficult to identify building as multi apartment or singly family house due to way of construction in this period. In every group it was considered type of heating system. Based on collected data several heating type systems were identified: - district heating system, that covers most of multi apartment building in Belgrade, especially in modern suburbs - gas heating system, that covers most of single family houses. This type of heating type in last few years replaced some other heating type in this sector in last few years - heating by electrical energy that can be found in multi apartment building and single family house both - coal and oil heating that covers single family houses, mostly from older period In analysis are also taken in consideration other parameters which were important for building envelope such are wall structure, window and door types, roof structure. These parameters are related also to the year of construction. Main problem which appeared later in simulation setting was heat transfer coefficient determination. In early years there were not standards for walls and roofs heat transfer coefficient, and it was only possible to assume range of this value by material types that were used in building envelope. Based on an analysis of all data it was concluded that buildings from period until were most interesting for heat consumption simulation. Multi apartment buildings from this period are specific in totally absence of insulation material, and walls are mainly constructed from concrete blocks. These buildings are specific for most parts of New Belgrade as it shown in Picture 1. Picture 1. Aerial view of New Belgrade part It was also noticed that in period of 30 years, building envelope was truncated due to weather conditions and no building maintenance. Windows are also in bad conditions; originally they are double glazing windows with wooden frame, which after of long use have lost their original characteristics. All of these buildings are connected to district heating system. Single family houses are constructed mainly from clay blocks and it is also specific for this period absence of insulation materials. Houses are detached, formed of ground floor and upper floor which ends by roof directly. Windows are also double glazing with wooden frame. These houses are heated by electrical energy mainly, and some of them, depending of town area are now connected to the gas network. For building numerical simulation TRNSYS software package was chosen. TRNSYS ([3],[4]) calculate thermal load based on weather data from a typical meteorological year (TMY).The

4 advantage of this software package in comparison to other similar programs is that allows the user analysis of multiple parameters on a relatively simple way. It is also possible to design windows and doors or to design internal and external heat transfer coefficients that are taken as constants in standard heat load calculation. One of advantages of TRNSYS is possibility for building zonal modeling. Defining the zone is left to the user selection. The concept of the program is such that as the output data the user gives the average value of temperature in the zones, heat gain, solar gain, heat transfer coefficients etc. For this simulation external heat transfer coefficient was based on model that uses wind speed and wind direction on a façade surface to calculate local velocity in vicinity of the surface. For surfaces that are windward [2]: For surfaces that are leeward: v 0.25v v l l 0.5 v 2m s (1) v 2m s v l v (2) The external heat transfer coefficient is modeled as: (3) sp v l Internal heat transfer coefficient is modeled as [5]: where are coefficients a,b,p,q and m are given in Table 2. Table 2. Coefficent for internal heat transfer Surface a b p Q m Vertical /4 1/3 6 Horisontal /4 1/ RESULTS AND DISCUSSION 1 m m p T q m a b T (4) d Analysis was conducted by numerical simulations and measurements in observed objects. Both buildings were divided into zones according to the positions of measurement equipment set. Model developed by TRNSYS software was validated by temperature inside two characteristic zones (rooms) in the building. Considering occupation during the day and people presence in observed apartments, living room and bedroom was taken into account for measurement and model validation. For multi apartment building results of simulations and measurements for few days in winter period are given in Picture 2 and Picture 3.

5 Picture 2. Calculated and temperatures in living room of multi apartment building for few winter days Picture 3. Calculated and temperatures in bedroom of multi apartment building for few winter days For single family house results of simulations and measurements for few days in winter period are given in Picture 4 and Picture 5.

6 Picture 4. Calculated and 700 temperatures in 750 living room of single 800 family house for few 850 winter days Picture 5. Calculated and temperatures in bedroom of single family house for few winter days Zonal modeling was applied in both buildings. In single family house considering less number of rooms, every room was taken as single zone. In case of multi apartment building, observed apartment was zoned in detail, and rest of building was divided in few zones taking into account internal temperature, building orientation and type of rooms use. Simulation was performed in way that temperature in zone is output value. Present heating systems in both objects were designed as internal heat gain. For the time interval show at pictures it was selected few days during the last winter with very low external temperature, which was good indicator for model validation. As it is shown in pictures, and temperature were very close in all selected zones. Small differences that appear between and data can be explained by differences in metrological data. For numerical simulation typical meteorological year for Belgrade location was used, where mean data are are given, and data corresponds to the real meteorological data. Even with these differences, it can be concluded that model for numerical simulation was set up correct and that can be used in other simulations and further model improvements. This fact is good indicator that described model can be used in simulation of thermal behavior; where for set up internal zone temperature would be obtained real building thermal load.

7 REFERENCES [1] Clarke J.A: Energy simulation in building design, Second edition, Butterworth-Heinemann, 2001 [2] Kimura, K Scientific Basis of Air Conditioning, Applied Science Publisher LTD, London [3] TRNSYS 15 Manual, University of Wisconsin Madison Solar Energy Lab and the University of Colorado Solar Energy Applications Lab., 2003 [4] Donnely John, Flynn Jim, Monagham Paul F.: Integration of energy simulation & ventilation design tools via an object oriented data model, Renewable Energy, Vol. 5, pp , 1994 [5] Beausoleil-Morrison, I: The adaptive coupling of heat and air modeling within dynamic whole-building simulation, Ph.D. thesis, University of Strathclyde, Glasgow, 2000 [6] Bojić Milorad, Kostić Saša: Application of COMIS software for ventilation study in a typical building in Serbia, Energy and Buildings, Vol. 41., pp , 2006 ACKNOWLEDGEMENT This paper is result of the project TR18004A: "Development and application of complementary methods for assessing the efficiency and quality indicators of indoor housing facilities in the Belgrade area" funded by Ministry of Science and Technological Republic of Serbia.