- Email: [email protected]
Investigation on correlation of energy consumption of multi-buildings on campus area
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Energy Procedia 158 Energy Procedia 00(2019) (2017)3559–3564 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China The 15th International Symposium on District Heating and Cooling
Investigation on correlation of energy consumption of multiInvestigation on correlation of campus energy consumption of multibuildingsofon Assessing the feasibility using the area heat demand-outdoor buildings on campus areaheat demand forecast temperature function for a long-term district a a, a
Wei Wang , Jiayu Chen * a,b,c a Wei Wang a a, Jiayu Chen b a,* c c I. Andrić *, and A.Civil Pina , P. Ferrão , J. Fournier .,Y6621, B. Lacarrière , O.Kowloon, Le Corre Department of Architecture Engineering, City University of Hong Kong, AC1, Tat Chee Ave, Hong Kong
Department of Innovation, ArchitectureTechnology and Civil Engineering, City University of Hong Kong, Y6621,Av. AC1, Tat Chee Kowloon,Lisbon, Hong Kong IN+ Center for and Policy Research - Instituto Superior Técnico, Rovisco PaisAve, 1, 1049-001 Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
aa
Abstract Abstract Buildings Abstract occupy a large proportion of energy use and to analyze the building energy use is quite important for
understanding building energy patternofand energy methods. For multi building use analysis, Buildings occupy a large proportion energy useconservation and to analyze the building energy use isenergy quite important for District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the researchers realized the interimpact and -relationship between multi buildings by considering inter buildings effect understanding building energy pattern and energy conservation methods. For multi building energy use analysis, greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat and identifying reference buildings inand the-relationship group. Thisbetween study would like to investigate correlations between effect multi researchers the interimpact multipolicies, buildings considering buildings sales. Due realized to the changed climate conditions and building renovation heatbydemand in theinter future could decrease, buildings to identify the relationship and reference buildings. In the method, the social network technique method and identifying reference buildings in the group. This study would like to investigate correlations between multi prolonging the investment return period. was used to identify the reference buildings and correlation between them and total buildings energy use, nonThe main to scope of thisthe paper is to assess and the feasibility using the heat demand – outdoor temperature function for heatmethod demand buildings identify relationship referenceofbuildings. In the method, the social network technique reference buildings, Tobuildings validate proposed method, this Southeast University as a of case forecast. district respectively. of located in Lisbon was usedstudy asthem a selected case The district is consisted 665 was used The to identify theAlvalade, reference and (Portugal), correlation between andstudy. total buildings energy use, nonbuildings that in bothtypes construction periodincluding and typology. Three buildings weather scenarios (low,laboratory medium, high) and three study and buildings, two vary buildings were education and buildings group. In reference respectively. Totested, validate proposed method, this studygroup selected Southeast University as a district case renovation scenarios were buildings, developed (shallow, intermediate, deep). To estimate the error, obtainedbetween heat demand values were the results, for education there are three reference buildings with the correlations them and total study and with two results buildings were heat tested, including group and laboratory buildings group. In compared fromtypes a dynamic demand model,education previously buildings developed and validated by the authors. buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between buildings the results, for education buildings, there are three reference buildings with the correlations between themapplications and total The results showed that when only weather change is considered, the margin of error could be acceptablereference for some and buildings 0.814, 0.845, 0.741. For laboratory buildings, the correlations between (the non-reference error energy in annual wasare lower than 20% for and all and weather scenarios considered). However, after introducing renovation buildings usedemand intensities about 0.712, 0.983, 0.910. While the correlations between reference buildings scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between The value buildings of slope coefficient increased on average withinare the 0.632 range of 3.8% up torespectively, 8% per decade, that0.637 corresponds to the reference and two non-reference buildings and 0.613, and and 0.218, reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and respectively. This study a significant case study are for the interdisciplinary research on multi-buildings reference and can twobenon-reference buildings 0.632 and 0.613, 0.637 and energy 0.218, renovationbuildings scenarios considered). On the other hand, function intercept increased for respectively, 7.8-12.7% per and decade (depending on the use analysis studies. respectively. This study can besuggested a significant study the interdisciplinary research energy coupled scenarios). The values couldcase be used to for modify the function parameters for on the multi-buildings scenarios considered, and Copyright © 2018 Elsevier Ltd.demand All rights reserved. the accuracy of heat estimations. use analysis studies. ©improve 2019 The Authors. Published by Elsevier Ltd. This is an open access article under CCreserved. BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Copyright © 2018 Elsevier Ltd. All the rights © 2017 Theunder Authors. Publishedof bythe Elsevier Ltd.committee of ICAE2018 – The 10th International Conference on Applied Energy. Peer-review responsibility scientific Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +852 3442 4696. Keywords: Heat demand; Forecast; Climate(Jiayu change E-mail address: [email protected] Chen) * Corresponding author. Tel.: +852 3442 4696. E-mail address: [email protected] (Jiayu Chen) 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and©peer-review under responsibility the scientific 1876-6102 2017 The Authors. Published byofElsevier Ltd. committee of the 10th International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.911
2
Author name / Energy Procedia 00 (2018) 000–000
Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied 3560 Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Energy (ICAE2018). Keywords: multi buildings, energy use intensity, correlation, social network technique;
1. Introduction Buildings occupy more than 40% of primary energy usage and become one of the main energy consumers [1], while in cities, buildings can consume up to 75% of total primary energy use [2]. Particularly, electricity contributes as one of the main energy use and the latest Electric Power Monthly with Data for January 2018 reported by Department of Energy (DOE), U.S., indicated the electricity consumption of both commercial and residential buildings consumes 77.5% of all the electricity produced in U.S [3]. The International Energy Agency (IEA)’s Energy in Buildings and Communities (EBC) Programme annexes also highlighted the importance of analyzing total energy use in buildings to reduce energy use and emissions in buildings and communities [4,5]. Besides the single building, more researchers started to realize the inter-impact and –relationship between building groups and investigate the inter-relationships between multi buildings. Further, the concept of the InterBuildings Effect (IBE) was introduced to understand the complex mutual impact within spatially proximal buildings [6–8]. For example, using the case in Perugia, Italy, Han et al explored the mutual shading and mutual reflection for IBEs on building energy performance with two realistic urban contexts [9] and further simulated the IBE on energy consumption form embedding phase change materials in building envelopes [10]. Recently, there are two main approaches applied for energy analysis of multi building groups and they are simulation and cluster method. Some software and web based interface applications, are the novel and creative approaches to analyzing and predicting energy use of multi-buildings in the distributed or urban areas. On one hand, for example, the City Building Energy Saver (CityBES), an Energyplus based web application, provides a visualization platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale efficiency programs [11–13], also predicting energy use for building retrofits measurements. Based on CityBES, Chen analyzed the impacts of building geometry modeling on urban building energy models to understand how a group of buildings will perform together [14]. On the other hand, for clustering method, Deb and Lee [15] studied on determining key variables influencing energy consumption in 56 office buildings through cluster analysis. The clustering approach focuses the investigation on small number of representative, reference buildings from a large buildings dataset [16]. Gaitani et al. [17] applied several variables, heating surface, building age, insulation of the buildings, number of classroom and students, operation hours, and age of heating system, in principal component and cluster analysis method to find the reference buildings. To expand the studies of IBE, this study would like to use the regression model and social network technique to investigate on correlation of energy consumption of multi-buildings and define effectively the reference buildings in multi buildings group. Also, this study provides the insights into the multi-building energy prediction based on the social network technique. 2. Case study This study chose four education buildings and four laboratory buildings, total eight buildings in Southeast University, located in Nanjing City, Jiangsu province, China. The location of eight buildings in Southeast University can be found in Fig. 1. The education buildings are in use for students to have class and study insides, while laboratory buildings are usually used for researchers to conduct their experiments in need. To apply social network
Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
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technique, this study selected monthly building energy use intensity dataset from year 2015 to year 2017 and the energy use intensities for different buildings were calculated by building electricity use data and building area.
Fig. 1 The layout of eight buildings in Southeast University. Commonly there are the two main approaches to build the connections of individual building in social network technique, they are distance method (e.g. Euclidean distance), which is usually used to calculate difference between two objects, and correlation method (e.g. Pearson correlation coefficient), which is usually used to find the similarity between each. This study used the Pearson correlation coefficient method to calculate the connections between buildings. Then, two steps are taken to extract the networks. The first step is to identify the reference buildings. The second step can build networks between all buildings and exclude weak networks by setting one threshold. 3. Results 3.1 Results on building energy use intensity This subsection shows the energy use intensity results of education buildings and laboratory buildings, which are indicated in Fig. 2 and 3. In Southeast University, those education buildings are relatively newly built around year 1980 to 1990. For education buildings, the EUIs are usually smaller around two summer break months around July and August and one winter break month around February. The EUI varies from 1.06 to 3.11 kWh/m2, from 0.97 to 4.86 kWh/m2, from 0.79 to 3.22 kWh/m2, respectively for education building 1, 2, and 4. While the education building 2 was the biggest energy consumer, the EUI of which varies from 3.63 to 14.34 kWh/m2.
Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 2. The building EUI results of education buildings group (year 2015: left, year 2016: middle, year 2017: right).
Fig. 3. The building EUI results of laboratory buildings group (year 2015: left, year 2016: middle, year 2017: right).
The laboratory buildings energy use intensities for three years generally show unclear trend. The average EUI for L4 is quite smaller than EUIs for others and its EUI varies from 0.04 to 6.01 kWh/m2 and average EUI is about 0.89 kWh/m2. Although L3 has the similar EUI trend to L4, varying from 0.16 to 7.27 kWh/m2, the average EUI of L3 is around 4.75, which is far higher than it of l4. The L2 consumes the biggest energy and also has the largest building area. Its EUI varies from 6.36 to 11.4 kWh/m2 and average EUI is about 8.48 kWh/m2. While for L1, its EUI is from 0.72 to 10.87 kWh/m2 with average EUI of 3.58 kWh/m2. 3.2 Results on building networks This subsection discusses the applications and results of the social network analysis. Fig. 4 and 5 include the networks between buildings for two buildings groups by calculating the correlation of total building EUI in year 2015 to 2017. In the figures, blue color circle represents the energy use intensity of non-reference building, yellow color circle represents the energy use intensity of reference building, and the green color circle represents the energy use intensity of total buildings. For education buildings group, only building E4 is the non-reference buildings. The building with most relevant trend to total buildings EUI trend is the building E2 with highly correlation of 0.983. The building E3 is also highly correlated to the total building EUI trend and its correlation is 0.91. While considering the networks in laboratory buildings, building L1 and L2 are the reference buildings with correlations of 0.722 and 0.918, respectively. For non-reference buildings, the building L4 is negative relevant to both buildings L1 and l2, showing the opposite EUI trends between L4 and L1, L2, respectively. Meanwhile the building L3 also shows the much low relation of EUI trend to reference building L1 and L2.
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Fig. 4 The results of networks between education building group.
Fig. 5 The results of networks between laboratory building group.
4. Discussion and conclusion This study proposed a methodology by investigating correlations between multi buildings. In the method, the social network technique method was used to identify the reference buildings and correlation between them and total buildings energy use, non-reference buildings, respectively. To validate proposed method, this study selected Southeast University as a case study and two buildings types were tested, including education buildings group and laboratory buildings group. In the results, for education buildings, there are three reference buildings with the correlations between them and total buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between reference buildings and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two reference buildings and two non-reference buildings are 0.632 and 0.613, respectively, and 0.637 and 0.218, respectively. This study can be a significant case study for the interdisciplinary research on multi-buildings energy use analysis studies. The reference buildings play a very important role in analyzing multi buildings group by providing the insights for, (1) identifying the buildings with key contributions to total building energy use intensity; (2) representing non-reference buildings when analyzing multi-building energy use with less building information; (3) integrating networks between reference buildings, total building energy use and non-reference buildings, respectively into other disciplines, for example, future study can apply the reference buildings to predict total building energy use.
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Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
Acknowledgement The work described in this paper was sponsored by the project JCYJ20150518163139952 of the Shenzhen Science and Technology Funding Programs and the National Natural Science Foundation of China (NSFC #51508487). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the Science Technology and Innovation Committee of Shenzhen and NSFC. References [1] Pérez-Lombard L, Ortiz J, Pout C. A review on buildings energy consumption information. Energy and Buildings 2008;40:394–8. doi:10.1016/j.enbuild.2007.03.007. [2] City Energy Project | A Joint Project of NRDC + IMT n.d. http://www.cityenergyproject.org/ (accessed May 3, 2018). [3] U.S. Energy Information Administration (EIA). Electric Power Monthly with data for January 2018. Washington, DC: 2018. [4] Hong T. IEA EBC annexes advance technologies and strategies to reduce energy use and GHG emissions in buildings and communities. Energy and Buildings 2018;158:147–9. doi:10.1016/J.ENBUILD.2017.10.028. [5] Yoshino H, Hong T, Nord N. IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods. Energy and Buildings 2017;152:124–36. doi:10.1016/J.ENBUILD.2017.07.038. [6] Han Y, Taylor JE, Pisello AL. Toward mitigating urban heat island effects: Investigating the thermal-energy impact of bio-inspired retro-reflective building envelopes in dense urban settings. Energy and Buildings 2015;102:380–9. doi:10.1016/J.ENBUILD.2015.05.040. [7] Pisello AL, Castaldo VL, Taylor JE, Cotana F. Expanding Inter-Building Effect modeling to examine primary energy for lighting. Energy and Buildings 2014;76:513–23. doi:10.1016/J.ENBUILD.2014.02.081. [8] Pisello AL, Taylor JE, Xu X, Cotana F. Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions. Building and Environment 2012;58:37–45. doi:10.1016/J.BUILDENV.2012.06.017. [9] Han Y, Taylor JE, Pisello AL. Exploring mutual shading and mutual reflection inter-building effects on building energy performance. Applied Energy 2017;185:1556–64. doi:10.1016/J.APENERGY.2015.10.170. [10] Han Y, Taylor JE. Simulating the Inter-Building Effect on energy consumption from embedding phase change materials in building envelopes. Sustainable Cities and Society 2016;27:287–95. doi:10.1016/J.SCS.2016.03.001. [11] Building Technology and Urban Systems Division at Lawrence Berkeley National Laboratory; City Building Energy Saver n.d. https://citybes.lbl.gov/. [12] Chen Y, Hong T. Creating Building Datasets for CityBES 2017. [13] Chen Y, Hong T, Piette MA. Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Applied Energy 2017;205:323–35. doi:10.1016/J.APENERGY.2017.07.128. [14] Chen Y, Hong T. Impacts of building geometry modeling methods on the simulation results of urban building energy models. Applied Energy 2018;215:717–35. doi:10.1016/j.apenergy.2018.02.073. [15] Deb C, Lee SE. Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data. Energy and Buildings 2018;159:228–45. doi:10.1016/j.enbuild.2017.11.007. [16] European Commission. Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings by establishing a comparative methodology framework for calculating. 2012. [17] Gaitani N, Lehmann C, Santamouris M, Mihalakakou G, Patargias P. Using principal component and cluster analysis in the heating evaluation of the school building sector. Applied Energy 2010;87:2079–86. doi:10.1016/J.APENERGY.2009.12.007.
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Energy Procedia 158 Energy Procedia 00(2019) (2017)3559–3564 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China The 15th International Symposium on District Heating and Cooling
Investigation on correlation of energy consumption of multiInvestigation on correlation of campus energy consumption of multibuildingsofon Assessing the feasibility using the area heat demand-outdoor buildings on campus areaheat demand forecast temperature function for a long-term district a a, a
Wei Wang , Jiayu Chen * a,b,c a Wei Wang a a, Jiayu Chen b a,* c c I. Andrić *, and A.Civil Pina , P. Ferrão , J. Fournier .,Y6621, B. Lacarrière , O.Kowloon, Le Corre Department of Architecture Engineering, City University of Hong Kong, AC1, Tat Chee Ave, Hong Kong
Department of Innovation, ArchitectureTechnology and Civil Engineering, City University of Hong Kong, Y6621,Av. AC1, Tat Chee Kowloon,Lisbon, Hong Kong IN+ Center for and Policy Research - Instituto Superior Técnico, Rovisco PaisAve, 1, 1049-001 Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
aa
Abstract Abstract Buildings Abstract occupy a large proportion of energy use and to analyze the building energy use is quite important for
understanding building energy patternofand energy methods. For multi building use analysis, Buildings occupy a large proportion energy useconservation and to analyze the building energy use isenergy quite important for District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the researchers realized the interimpact and -relationship between multi buildings by considering inter buildings effect understanding building energy pattern and energy conservation methods. For multi building energy use analysis, greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat and identifying reference buildings inand the-relationship group. Thisbetween study would like to investigate correlations between effect multi researchers the interimpact multipolicies, buildings considering buildings sales. Due realized to the changed climate conditions and building renovation heatbydemand in theinter future could decrease, buildings to identify the relationship and reference buildings. In the method, the social network technique method and identifying reference buildings in the group. This study would like to investigate correlations between multi prolonging the investment return period. was used to identify the reference buildings and correlation between them and total buildings energy use, nonThe main to scope of thisthe paper is to assess and the feasibility using the heat demand – outdoor temperature function for heatmethod demand buildings identify relationship referenceofbuildings. In the method, the social network technique reference buildings, Tobuildings validate proposed method, this Southeast University as a of case forecast. district respectively. of located in Lisbon was usedstudy asthem a selected case The district is consisted 665 was used The to identify theAlvalade, reference and (Portugal), correlation between andstudy. total buildings energy use, nonbuildings that in bothtypes construction periodincluding and typology. Three buildings weather scenarios (low,laboratory medium, high) and three study and buildings, two vary buildings were education and buildings group. In reference respectively. Totested, validate proposed method, this studygroup selected Southeast University as a district case renovation scenarios were buildings, developed (shallow, intermediate, deep). To estimate the error, obtainedbetween heat demand values were the results, for education there are three reference buildings with the correlations them and total study and with two results buildings were heat tested, including group and laboratory buildings group. In compared fromtypes a dynamic demand model,education previously buildings developed and validated by the authors. buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between buildings the results, for education buildings, there are three reference buildings with the correlations between themapplications and total The results showed that when only weather change is considered, the margin of error could be acceptablereference for some and buildings 0.814, 0.845, 0.741. For laboratory buildings, the correlations between (the non-reference error energy in annual wasare lower than 20% for and all and weather scenarios considered). However, after introducing renovation buildings usedemand intensities about 0.712, 0.983, 0.910. While the correlations between reference buildings scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between The value buildings of slope coefficient increased on average withinare the 0.632 range of 3.8% up torespectively, 8% per decade, that0.637 corresponds to the reference and two non-reference buildings and 0.613, and and 0.218, reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and respectively. This study a significant case study are for the interdisciplinary research on multi-buildings reference and can twobenon-reference buildings 0.632 and 0.613, 0.637 and energy 0.218, renovationbuildings scenarios considered). On the other hand, function intercept increased for respectively, 7.8-12.7% per and decade (depending on the use analysis studies. respectively. This study can besuggested a significant study the interdisciplinary research energy coupled scenarios). The values couldcase be used to for modify the function parameters for on the multi-buildings scenarios considered, and Copyright © 2018 Elsevier Ltd.demand All rights reserved. the accuracy of heat estimations. use analysis studies. ©improve 2019 The Authors. Published by Elsevier Ltd. This is an open access article under CCreserved. BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Copyright © 2018 Elsevier Ltd. All the rights © 2017 Theunder Authors. Publishedof bythe Elsevier Ltd.committee of ICAE2018 – The 10th International Conference on Applied Energy. Peer-review responsibility scientific Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +852 3442 4696. Keywords: Heat demand; Forecast; Climate(Jiayu change E-mail address: [email protected] Chen) * Corresponding author. Tel.: +852 3442 4696. E-mail address: [email protected] (Jiayu Chen) 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and©peer-review under responsibility the scientific 1876-6102 2017 The Authors. Published byofElsevier Ltd. committee of the 10th International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.911
2
Author name / Energy Procedia 00 (2018) 000–000
Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied 3560 Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Energy (ICAE2018). Keywords: multi buildings, energy use intensity, correlation, social network technique;
1. Introduction Buildings occupy more than 40% of primary energy usage and become one of the main energy consumers [1], while in cities, buildings can consume up to 75% of total primary energy use [2]. Particularly, electricity contributes as one of the main energy use and the latest Electric Power Monthly with Data for January 2018 reported by Department of Energy (DOE), U.S., indicated the electricity consumption of both commercial and residential buildings consumes 77.5% of all the electricity produced in U.S [3]. The International Energy Agency (IEA)’s Energy in Buildings and Communities (EBC) Programme annexes also highlighted the importance of analyzing total energy use in buildings to reduce energy use and emissions in buildings and communities [4,5]. Besides the single building, more researchers started to realize the inter-impact and –relationship between building groups and investigate the inter-relationships between multi buildings. Further, the concept of the InterBuildings Effect (IBE) was introduced to understand the complex mutual impact within spatially proximal buildings [6–8]. For example, using the case in Perugia, Italy, Han et al explored the mutual shading and mutual reflection for IBEs on building energy performance with two realistic urban contexts [9] and further simulated the IBE on energy consumption form embedding phase change materials in building envelopes [10]. Recently, there are two main approaches applied for energy analysis of multi building groups and they are simulation and cluster method. Some software and web based interface applications, are the novel and creative approaches to analyzing and predicting energy use of multi-buildings in the distributed or urban areas. On one hand, for example, the City Building Energy Saver (CityBES), an Energyplus based web application, provides a visualization platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale efficiency programs [11–13], also predicting energy use for building retrofits measurements. Based on CityBES, Chen analyzed the impacts of building geometry modeling on urban building energy models to understand how a group of buildings will perform together [14]. On the other hand, for clustering method, Deb and Lee [15] studied on determining key variables influencing energy consumption in 56 office buildings through cluster analysis. The clustering approach focuses the investigation on small number of representative, reference buildings from a large buildings dataset [16]. Gaitani et al. [17] applied several variables, heating surface, building age, insulation of the buildings, number of classroom and students, operation hours, and age of heating system, in principal component and cluster analysis method to find the reference buildings. To expand the studies of IBE, this study would like to use the regression model and social network technique to investigate on correlation of energy consumption of multi-buildings and define effectively the reference buildings in multi buildings group. Also, this study provides the insights into the multi-building energy prediction based on the social network technique. 2. Case study This study chose four education buildings and four laboratory buildings, total eight buildings in Southeast University, located in Nanjing City, Jiangsu province, China. The location of eight buildings in Southeast University can be found in Fig. 1. The education buildings are in use for students to have class and study insides, while laboratory buildings are usually used for researchers to conduct their experiments in need. To apply social network
Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
3561 3
technique, this study selected monthly building energy use intensity dataset from year 2015 to year 2017 and the energy use intensities for different buildings were calculated by building electricity use data and building area.
Fig. 1 The layout of eight buildings in Southeast University. Commonly there are the two main approaches to build the connections of individual building in social network technique, they are distance method (e.g. Euclidean distance), which is usually used to calculate difference between two objects, and correlation method (e.g. Pearson correlation coefficient), which is usually used to find the similarity between each. This study used the Pearson correlation coefficient method to calculate the connections between buildings. Then, two steps are taken to extract the networks. The first step is to identify the reference buildings. The second step can build networks between all buildings and exclude weak networks by setting one threshold. 3. Results 3.1 Results on building energy use intensity This subsection shows the energy use intensity results of education buildings and laboratory buildings, which are indicated in Fig. 2 and 3. In Southeast University, those education buildings are relatively newly built around year 1980 to 1990. For education buildings, the EUIs are usually smaller around two summer break months around July and August and one winter break month around February. The EUI varies from 1.06 to 3.11 kWh/m2, from 0.97 to 4.86 kWh/m2, from 0.79 to 3.22 kWh/m2, respectively for education building 1, 2, and 4. While the education building 2 was the biggest energy consumer, the EUI of which varies from 3.63 to 14.34 kWh/m2.
Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 2. The building EUI results of education buildings group (year 2015: left, year 2016: middle, year 2017: right).
Fig. 3. The building EUI results of laboratory buildings group (year 2015: left, year 2016: middle, year 2017: right).
The laboratory buildings energy use intensities for three years generally show unclear trend. The average EUI for L4 is quite smaller than EUIs for others and its EUI varies from 0.04 to 6.01 kWh/m2 and average EUI is about 0.89 kWh/m2. Although L3 has the similar EUI trend to L4, varying from 0.16 to 7.27 kWh/m2, the average EUI of L3 is around 4.75, which is far higher than it of l4. The L2 consumes the biggest energy and also has the largest building area. Its EUI varies from 6.36 to 11.4 kWh/m2 and average EUI is about 8.48 kWh/m2. While for L1, its EUI is from 0.72 to 10.87 kWh/m2 with average EUI of 3.58 kWh/m2. 3.2 Results on building networks This subsection discusses the applications and results of the social network analysis. Fig. 4 and 5 include the networks between buildings for two buildings groups by calculating the correlation of total building EUI in year 2015 to 2017. In the figures, blue color circle represents the energy use intensity of non-reference building, yellow color circle represents the energy use intensity of reference building, and the green color circle represents the energy use intensity of total buildings. For education buildings group, only building E4 is the non-reference buildings. The building with most relevant trend to total buildings EUI trend is the building E2 with highly correlation of 0.983. The building E3 is also highly correlated to the total building EUI trend and its correlation is 0.91. While considering the networks in laboratory buildings, building L1 and L2 are the reference buildings with correlations of 0.722 and 0.918, respectively. For non-reference buildings, the building L4 is negative relevant to both buildings L1 and l2, showing the opposite EUI trends between L4 and L1, L2, respectively. Meanwhile the building L3 also shows the much low relation of EUI trend to reference building L1 and L2.
Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 4 The results of networks between education building group.
Fig. 5 The results of networks between laboratory building group.
4. Discussion and conclusion This study proposed a methodology by investigating correlations between multi buildings. In the method, the social network technique method was used to identify the reference buildings and correlation between them and total buildings energy use, non-reference buildings, respectively. To validate proposed method, this study selected Southeast University as a case study and two buildings types were tested, including education buildings group and laboratory buildings group. In the results, for education buildings, there are three reference buildings with the correlations between them and total buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between reference buildings and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two reference buildings and two non-reference buildings are 0.632 and 0.613, respectively, and 0.637 and 0.218, respectively. This study can be a significant case study for the interdisciplinary research on multi-buildings energy use analysis studies. The reference buildings play a very important role in analyzing multi buildings group by providing the insights for, (1) identifying the buildings with key contributions to total building energy use intensity; (2) representing non-reference buildings when analyzing multi-building energy use with less building information; (3) integrating networks between reference buildings, total building energy use and non-reference buildings, respectively into other disciplines, for example, future study can apply the reference buildings to predict total building energy use.
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Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 Author name / Energy Procedia 00 (2018) 000–000
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