The problem study of district energy system in Shanghai, China

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Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, Applied Energy Symposium and Forum, Energy Integration REM 2017, 18–20 Renewable October 2017, Tianjin, China with Mini/Microgrids, REM 2017, 18–20 October 2017, Tianjin, China

The problem study of district energy system in Shanghai, China The 15th International Symposium on District Heating and Cooling China The problem study of district energy system in Shanghai, Shangyuan Huangaa, Zhiyuan Liubb*, Chunyan Zhangaa Shangyuan Huang , Zhiyuan Liu the *, Chunyan Zhang Assessing the feasibility of using heat demand-outdoor a

State Grid Shanghai Municipal Electric Power Company, Yanshen Road No.1122, 200122 Shanghai, China

State GridofShanghai Municipal Electric Power Company,Caoan Yanshen Road 200122 Shanghai, China forecast School Mechanical Engineering, University, Road No.No.1122, 4800,heat 201804 Shanghai, China temperature function for aTongji long-term district demand School of Mechanical Engineering, Tongji University, Caoan Road No. 4800, 201804 Shanghai, China a

b b

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & built Innovation, 291inAvenue Dreyfous 78520 Limay, France A number of district energy systems have been recently Yangtze RiverDaniel, Delta where is the most developed region in China. c Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France A number ofmany district energy systems have been built recently in Yangtze Deltastations where is the most developed region of in energy China. Meanwhile, problems around the systems are rising. Firstly, almost River all energy adopt the redundancy design Meanwhile, many problems around the systems are rising. Firstly, almost all energy stations adopt the redundancy design of chilled energy load, which leads to the inefficiency of energy during part-load operation, especially. Secondly, primary and secondary load, leads to the inefficiency of energy during part-load operation, especially. Secondly, primary and secondary chilled water which could be unbalanced due to redundancy design. Furthermore, low occupancy rate could lead over supplied of energy station, water could be unbalanced due to redundancy design. Furthermore, low occupancy rate could lead over supplied of energy station, and large water flow with narrow temperature difference in heating and cooling network could result in the low-energy efficiency Abstract and large water flowInwith narrow temperature difference in heating and cooling result in theinlow-energy poor economy. order to solve those problems, a model of district energynetwork system could was put forward this paper,efficiency based on and poor economy. Inload. orderOn to one solve thosetheproblems, a model delayed of district energy system was put forward inkey thisfactors paper,ofbased on prototypical building hand, model considers performance of energy system and district District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the prototypical building load. On one hand, theit model considers delayed performance ofthe energy system and key factors of district energy system planning. On the other hand, can make the energy supply matching to hourly loads of every user. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat energy system planning. On the other hand, it can make the energy supply matching to the hourly loads of every user. sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Copyright © the 2018 Elsevier Ltd. Allperiod. rights reserved. prolonging investment return Copyright © 2018 2018 The Authors. Published by Elsevier Ltd. Copyright © Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the of scientific of the Applied Energy Symposium The mainand scope of this paper is responsibility to assess the feasibility using thecommittee heat demand – outdoor temperature function forand heatForum, demand Selection peer-review under of the scientific committee of the Applied Energy Symposium and Forum, Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with REM 2017. Renewable Energy Integration with Mini/Microgrids, Mini/Microgrids, REM 2017 forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Renewable Energy Integration with Mini/Microgrids, REM 2017. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Keywords: district energy systems; several problems; user station; model; hourly loads renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: district energy systems; several problems; user station; model; hourly loads compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications 1.(the Introduction error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.scenarios, Introduction the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Asvalue urbanization economic growth in Chinese Yangtze River thedecade, consumption of fossil fuels The of slope accelerate coefficient and increased on average within the range of 3.8% up toDelta, 8% per that corresponds to the As urbanization accelerate anddevelopment economic growth in Chinese Yangtze Delta, the consumption of fuels isdecrease increasing rapidly. Sustainable has during attracted attention as a way toon address the problems of energy in the number of heating hours of 22-139h themuch heating seasonRiver (depending the combination of fossil weather and is increasing rapidly. Sustainable development has attracted much attention a way togained address the problems energy renovation scenarios considered). On the other hand, functiondistributed intercept increased for 7.8-12.7% permore decade on the shortages and environmental deterioration [1,2], therefore energyassystem is and(depending moreof attention shortages and environmental [1,2], distributed energy system is gained and more attention coupled scenarios). The valuesdeterioration suggested could betherefore used to modify the function parameters for themore scenarios considered, and and widely used. The majority of newly built city districts (we suppose the area of such district approximately several and widely Theofmajority of newly built city districts (we suppose the area of such district approximately several improve theused. accuracy heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +8615821611277. Cooling. * Corresponding author. Tel.: +8615821611277. E-mail address: [email protected].

E-mail address: [email protected]. Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum, Renewable Energy Selection and peer-review under responsibility the scientific Selection peer-review under responsibility Integrationand with Mini/Microgrids, REM 2017. of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2017. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2017 10.1016/j.egypro.2018.04.079

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square kilometers, which means a small-scale area with mixed land use) usually adopt central energy-supply system to provide cooling, heating and power by one or several energy stations. Considering the energy efficiency of multiconsumer and multi-producer, the energy system can implement that the collaboration of centralization and decentralization, the coexistence of renewable energy and fossil energy. And it also improves the synthetical efficiency for energy utilization by optimal design [3]. It is worth noting that the good top-level design, systematic structure optimization and installation is very important. Nomenclature 𝑄𝑄𝑡𝑡′ 𝑞𝑞𝑗𝑗𝑗𝑗 𝑆𝑆𝑗𝑗 𝑛𝑛 𝛼𝛼1 𝛼𝛼2 𝛼𝛼3 𝑇𝑇𝑝𝑝 (𝑥𝑥, 𝑡𝑡) 𝑥𝑥 𝜆𝜆𝑊𝑊 𝜌𝜌 𝐶𝐶𝑝𝑝 𝑑𝑑 𝑘𝑘(𝑥𝑥) 𝑇𝑇𝑆𝑆 (𝑥𝑥) 𝐶𝐶𝑜𝑜 𝐶𝐶𝑖𝑖𝑖𝑖 𝐶𝐶𝑚𝑚 𝜀𝜀 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑃𝑃𝑡𝑡 𝜔𝜔 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝜂𝜂𝑒𝑒 𝑞𝑞 ∆𝑡𝑡 𝑃𝑃𝑃𝑃𝑡𝑡𝑜𝑜𝑜𝑜 𝐿𝐿𝐿𝐿𝑡𝑡 𝛾𝛾𝑒𝑒−𝑖𝑖 𝑃𝑃𝑡𝑡𝑖𝑖

the hourly load of district energy system, W the hourly load of j’s building per square meter, W/m2 the overall floorage of j’s building, m2 the sum of building types the correction factor of district energy load result from architectural form and microclimate the correction factor of district energy load taking accounts of the attenuation and delay the correction factor of district energy load result from other elements the distribution of temperature in pipeline, K the delivery distance of pipeline, m the heat conductivity coefficient of water, W/(m·K) the density of water, kg/m³ the specific heat capacity of water, J/(kg·K) piping diameter, m the equivalent heat transfer coefficient, W/(m2·K) the temperature of external environment, K the annual operative cost, ￥ the uniform annual value of initial cost, ￥ the equipment maintenance cost, ￥ the carbon tax, ￥/t the grid’s supply power of time t, kW the carbon emission factor of power grid, tCO2/kWh the generating efficiency of CHP the lower calorific value of natural gas, kJ/Nm3 the time interval, h the electric power consumption of other equipment on the time t, kW the cooling load of time t, kW the equipment i’s state of start-up and shut-down, which’s value is 0 or 1 the output of equipment i in time t, kW

There are many researches and studies about district energy system. In district energy system planning, Hang Y et al. [4] present the main conception of community energy planning and its status and relationship in city master plan, city detailed plan and architectural design. The core problem of energy planning is to resolve the contradiction between the current supply and current demand, as well as current supply and future demand, which are involved in energy security issues [5,6]. In load prediction of district energy system, Papakostas et al. [7] proposed a new procedure for the calculation of equivalent full-load hours of operation for heating and cooling system, from hourly temperature data. Equivalent full hours used for a rough estimation of annual heating and cooling energy requirements in buildings. Seo et al. [8] developed a nine-node-based Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design, which can be useful for an architect or a construction manager in the early design phase. In the mathematical model of district energy system, a partial differential equation that is the linear transport equation corresponding to mass density is used in modeling specific total energy of the fluid

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in district heating systems [9]. A further partial differential equation resulted from this approach is solved along the characteristic path determined by the fluid particle motion. The paper is organized in the following way: Section 2 presents the four main problems of district energy system in Chinese Yangtze River Delta by investigate and survey. In section 3, we analyze the challenges which we face in three levels including plan, design and operations management, simply. Section 4 contains the model of district energy system which considering the delaying effect and heat loss). Final conclusions and future research proposals are given in Section 5. 2. Several problems 2.1. System redundancy Architecture design institutions usually adopt the method of indicator per unit area to calculate the building designing load, or the method of simulating a kind of building type for PPT. And then, they put those load data to multiply by diversity factor to obtain the design load of district energy system (see Fig.1). There exist three issues in the process: (1) The method of indicator per unit area magnifies the single building load, firstly. (2) Where the diversity factor comes from is fuzzy, and architecture design institutions usually select the bigger value when they unable to judge which one is more accurate. (3) All of above data must be multiplied by safety factor for security. Therefore, the design load of district energy system has been rather redundant. However, this is not the end. The owner or architecture design institution ensure each piece of equipment meeting the demands, result in the system redundancy getting bigger in the basis of design load, when choosing systematic equipment and pipe. In fact, the problem of installed capacity redundancy can be solved in phases, but the redundancy of pipe system results transmission energy consumption sharply increasingly. Single building load

Diversity factor

Safety factor Δ2

Δ1

actual curve

Δ3

design curve

Fig. 1. The superposition of system redundancy

2.2. Ambiguous functionality (user station) Only one-third of the investigating district energy systems adopt the direct supply of refrigerating medium, and the most of systems make use of the heat exchanger to separate district energy main system from block energy subsystem for accomplishing independent control. However, the primary water and secondary water of user station’s control is very bad, actually. It is also appeared that the consumer demand, the heat exchanger’s efficiency of user station and energy station supply are mismatching. In fact, the regulation’ objective of user station is not uniformly distributing load in all of user station, but satisfying the demand of consumer. This is very important. And Fig.2 can implement excellent control.

Author name / Energy Procedia 00 (2018) 000–000 Shangyuan Huang et al. / Energy Procedia 145 (2018) 542–548

4

b

545

E.M

Secondary water

a

Primary water Signal acquisition

Control signal

Fig.2. The control method of user station

2.3. Foamy demand We must make clear that the government has amazing abilities in China, firstly. There are many policies to support the sustainable and low-carbon development, especially in the newly-developed urban area or district area. Generally, the newly-developed district area is located in distant suburb, and the price of block or work place is very cheap because of policy factor in initial stage of development. So many companies or individualities may purchase it and let it in idle, resulting in plenty of buildings are zero load or a little. But architecture design institution or the owner of energy system must do staging design according to the data of land grant and sales. This is the reason of generated foamy demand. We can avoid or reduce the foamy demand by assessing the influence of government policy. 2.4. Large water flow with narrow temperature difference What’s the means of large water flow with narrow temperature difference? This phenomenon often appears in district energy system, and it mainly means that the temperature difference between return water and supply water from energy station is less than 3℃. This phenomenon exists in all of investigating projects. There are two projects that the temperature difference varies from supply water in 5.1℃ to return water in 6.0℃, and its demand of cooling is not low. Apparently, this mode of supply energy consumes more energy than big temperature difference with small water flow rate. We minutely analyse the mechanism of large water flow with narrow temperature difference on account of energy conservation. And in the end, we consider that the efficiently running of energy system in the control of secondary water of user station indirectly. So it appears to be of essence to ensure the temperature difference of supply and return water in user station or to pinpoint the control privileges of consumer energy subsystem. 3. The challenge of facing 3.1. Planning level The present situation and development of research on energy demand forecasting, renewable energy resource assessments and whole community energy system optimization, which are three highly important components of

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bottom-up models. There is still a shortage of available tools for determining energy-related indictors at the district Regulatory Plan stage. District energy plan includes not only the community energy system and facility design but also all energy-related issues in a community, such as a setting energy consumption target, selecting energy resources, and energy conversion technologies evaluation. However, there seriously lacks energy structure indicators, aggregate indicators and energy transformation efficiency indicators in urban energy planning and district master plan. This information can meet the demand of district energy planning and can also be helpful in the design of district energy systems at the district site plan and architectural design stages. 3.2. Design level As all we known, one of the current challenges is predicting district energy demand in energy system design level, because of the prediction of district energy including cooling, heating and power lacks theories to support and don’t embody essential characteristics on the form and scale of district energy system. We still have a long way to go in the way which convert prototypical building load into district level. There are two main variables: (1) The relationship between real time summation of all isolated buildings and output of energy station is not one to one correspondence. In other words, the data of demand-side monitoring can’t be used for the control of energy centre directly. (2) How to describe the coupling relationship in the middle of single building? It should include the coupling relationship of return water mixture, microclimate and levelling load. 3.3. Operations management level The biggest challenge in operations management level is how to accomplish the interaction of heterogeneous data which include changes of the outside air temperature, the wind intensity, the solar radiation, the personal favour, or system start-ups and shut-downs for adjusting in energy efficient manner the loads of energy station and pump stations with dynamic consumption needs. 4. The model of district energy system 4.1. Model construction We propose the new calculation formula which is to solve the extension of energy load from individual building to district buildings based on the present literatures: (1) 𝑄𝑄𝑡𝑡′ = 𝛼𝛼1 · 𝛼𝛼2 · 𝛼𝛼3 · ∑𝑛𝑛𝑗𝑗=1 𝑞𝑞𝑗𝑗𝑗𝑗 𝑆𝑆𝑗𝑗 (t=1，2，3…，8760)

Where 𝛼𝛼1 = 𝑎𝑎 ∗ 𝑏𝑏 , 𝑎𝑎 is the correction factor of district energy load result from architectural form, 𝑏𝑏 is the correction factor of district energy load result from microclimate. And these two parameters can be consulted in references. Where 𝛼𝛼2 can be calculated by the heat-transfer model of pipes taking accounts of the attenuation and delay for district energy system [10]: ∂𝑇𝑇𝑝𝑝 (𝑥𝑥,𝑡𝑡) ∂𝑡𝑡

=

𝜆𝜆𝑊𝑊 𝜕𝜕2 𝑇𝑇𝑝𝑝 (𝑥𝑥,𝑡𝑡)

𝜌𝜌𝐶𝐶𝑝𝑝

𝜕𝜕𝑥𝑥 2

+

𝜋𝜋𝜋𝜋𝜋𝜋(𝑥𝑥)[𝑇𝑇𝑃𝑃 (𝑥𝑥,𝑡𝑡)−𝑇𝑇𝑆𝑆 (𝑥𝑥)]

(2)

𝜌𝜌𝐶𝐶𝑝𝑝

Where the physical meaning of those parameters can be find in Nomenclature. And 𝛼𝛼3 = 1 in this paper. We built a model of district energy system based on this essential research: The objective function of economic is shown in equation (3): 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔

min ∑ （𝐶𝐶𝑖𝑖𝑖𝑖 + 𝐶𝐶𝑜𝑜 + 𝐶𝐶𝑚𝑚 + ∑8760 𝑡𝑡=1 𝜀𝜀（𝑃𝑃𝑡𝑡

And the constraint conditions are:

𝑃𝑃𝑡𝑡𝐶𝐶𝐶𝐶𝐶𝐶

𝜔𝜔 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 + (

𝜂𝜂𝑒𝑒 𝑞𝑞

+

𝑃𝑃𝑡𝑡𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏

𝜂𝜂𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑞𝑞

) 𝜔𝜔 𝑔𝑔𝑔𝑔𝑔𝑔 ） ∆𝑡𝑡）

(3)

6

Shangyuan Huang et al. / Energy Procedia 145 (2018) 542–548 Author name / Energy Procedia 00 (2018) 000–000

{

𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔

𝑃𝑃𝑡𝑡𝐶𝐶𝐶𝐶𝐶𝐶 + 𝑃𝑃𝑡𝑡

𝑙𝑙𝑙𝑙𝑙𝑙ℎ𝑡𝑡

= 𝑃𝑃𝑃𝑃𝑡𝑡𝑜𝑜𝑜𝑜 + 𝑃𝑃𝑃𝑃𝑡𝑡

547

+ 𝑃𝑃𝑃𝑃𝑡𝑡𝑃𝑃𝑃𝑃 + 𝑃𝑃𝑃𝑃𝑡𝑡𝑐𝑐ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓

(4)

𝑃𝑃𝑡𝑡𝑐𝑐ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑃𝑃𝑡𝑡𝐶𝐶𝐶𝐶𝐶𝐶−𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 + 𝜂𝜂𝑐𝑐 𝑃𝑃𝑡𝑡𝐸𝐸𝐸𝐸 + 𝑃𝑃𝑡𝑡 = 𝐿𝐿𝐿𝐿𝑡𝑡 𝑖𝑖 𝑖𝑖 𝛾𝛾𝑒𝑒−𝑖𝑖 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 ≤ 𝑃𝑃𝑡𝑡𝑖𝑖 ≤ 𝛾𝛾𝑒𝑒−𝑖𝑖 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚

Where the physical meaning of those parameters also can be find in Nomenclature. 4.2. Results

We chose three typical district energy systems and calculated this model, which’s three important parameters are showed in table 1. Table 1. The results of three important parameters in three typical district energy systems Typical case summer

𝑎𝑎

𝛼𝛼1 winter

summer

𝑏𝑏

𝛼𝛼2

winter

𝛼𝛼3

𝛼𝛼1 · 𝛼𝛼2 · 𝛼𝛼3

summer

winter

Case 1

0.975

1.123

1.15

0.95

0.96

1

1.08

1.02

Case 2

1.126

0.832

1.04

0.99

0.94

1

1.10

0.77

Case 3

1.025

0.956

1.04

0.98

0.92

1

0.98

0.86

We can find those three parameters magnify the energy load in case 1, 2 in summer and in case 1 in winter. In addition, 𝛼𝛼1 · 𝛼𝛼2 · 𝛼𝛼3 can decrease the energy load in case 3 in summer and incase 2, 3 in winter. Then we propose two scenarios which respectively are A (without energy storage) and B (with energy storage) for case 1. And the results of two scenarios are showed in the table 2. Table 2. The optimal results of all energy conversion technologies in two scenarios Energy conversion technology

Scenario A

Scenario B

Gas Generator/kW

20274

14873

Electric Chiller/kW

28466

32024

Absorption Chiller/kW

22707

15174

Free Cooling/kW

—

—

10343

11842

—

2828

Power Grid/kW Energy Storage/m

3

From table 2, we can find that the energy storage effectively increases the diversity of energy conversion technologies and reduces energy costs. 5. Conclusion In the present paper, four main problems have been introduced in Chinese Yangtze River Delta, and we can solve the capacity redundancy by establishing new model of load forecasting, solve the ambiguous functionality by new control logic, solve foamy demand by assessing the influence of government policy and solve large water flow with narrow temperature difference by ensuring the temperature difference of supply and return water in user station or pinpointing the control privileges of consumer energy subsystem. Also, the paper comes up with some challenges which we facing in current district energy system. The model which considering the delaying effect is presented.

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Acknowledgements This work was financially supported by the Science and Technology Project of SGCC State Grid Corporation of China (52094016000k) “Research and application of the key technologies on energy interconnection among multiscale distributed low-carbon energy stations”. References [1] The People's Republic of China National Report on Sustainable Development, 〈http://www.chinaun.org/chn/zt/zzsdr2012/P020120608809687043092.pdf〉 [2] Budzianowski W. Target for national carbon intensity of energy by 2050: a case study of Poland's energy system. Energy 2012, 46: 57–81 [3] Wei-ding Long etc. Urban demand side energy planning and energy micro-network technology [M]. Beijing: China Construction Industry Press, 2016 [4] Zishuo Huang, Hang Yu et al. Methods and tools for community energy planning: A review. Renewable and Sustainable Energy Reviews, 2015, 42, 1335–1348 [5] Qiu D. Energy planning and systems analysis. Beijing: Tsinghua University Press, 1995, ISBN: 730201812X [6] Swisher Joel N, Martino Jannuzzi Gilberto de, Redlinger Robert Y. Tools and methods for integrated resource planning. Grafisk Service, Riso National Laboratory, 1997, ISBN: 8755023320 [7] Papakostas KT, Michopoulos AK, Kyriakis NA. Equivalent full-load hours for estimating heating and cooling energy requirements in buildings: Greece case study. Applied Energy, 2009, 86:747-761 [8] Seo DY, Koo C, Hong TA. Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design. Applied Energy, 2015, 142: 66-79 [9] V.D. Stevanovic, B. Zivkovic, S. Prica, B. Maslovaric, V. Karamarkovic, V. Trkulja. Prediction of thermal transients in district heating systems. Energy Convers Manage, 2009, 50: 2167–2173 [10] Z. L, H. Y, Z. H. A prediction approach of energy station in community energy system based on the attenuation and delay of pipes. Journal of HV&AC, 2017, 47(04): 19-22