Times series forecasting for urban building energy consumption based on graph convolutional network
•Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results. The wo...
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| Vydáno v: | Applied energy Ročník 307; s. 118231 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.02.2022
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Abstract | •Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results.
The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction. |
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| AbstractList | •Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results.
The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction. The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction. |
| ArticleNumber | 118231 |
| Author | Hu, Yuqing Cheng, Xiaoyuan Chen, Jianli Zhao, Tianxiang Dai, Enyan Wang, Suhang |
| Author_xml | – sequence: 1 givenname: Yuqing surname: Hu fullname: Hu, Yuqing organization: Department of Architectural Engineering, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 2 givenname: Xiaoyuan surname: Cheng fullname: Cheng, Xiaoyuan organization: Department of Architectural Engineering, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 3 givenname: Suhang surname: Wang fullname: Wang, Suhang organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 4 givenname: Jianli surname: Chen fullname: Chen, Jianli email: jianli.chen@utah.edu organization: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA – sequence: 5 givenname: Tianxiang surname: Zhao fullname: Zhao, Tianxiang organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 6 givenname: Enyan surname: Dai fullname: Dai, Enyan organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA |
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| Cites_doi | 10.1016/j.eiar.2020.106487 10.1016/j.apenergy.2020.115584 10.14778/3430915.3430924 10.3390/buildings8030037 10.1109/TNN.2008.2005605 10.1016/j.aiopen.2021.01.001 10.1016/j.apenergy.2016.08.079 10.1016/j.enbuild.2008.06.013 10.1016/j.rser.2020.110591 10.1016/j.enbuild.2019.109580 10.1016/j.apenergy.2021.116452 10.1016/j.autcon.2018.05.002 10.1016/j.buildenv.2019.106508 10.1109/INTLEC.2018.8612398 10.1016/j.rser.2020.109902 10.1016/j.scs.2020.102408 10.1016/j.autcon.2020.103231 10.1016/j.apenergy.2020.115834 10.1016/j.apenergy.2018.05.023 10.1016/j.autcon.2017.12.017 10.1016/j.autcon.2010.07.006 10.1016/j.apenergy.2020.115656 10.1016/j.energy.2017.11.071 10.24963/ijcai.2018/505 10.1016/j.buildenv.2015.12.001 10.1016/j.enbuild.2021.110786 10.1016/j.ijproman.2016.08.001 10.1038/s41598-018-24271-9 10.3390/en13092382 10.3390/en12030433 10.1016/j.enbuild.2012.03.010 10.1115/1.4036545 10.1016/j.enbuild.2017.08.029 10.1016/j.enbuild.2004.09.009 10.1016/j.enbuild.2018.09.034 10.1177/0008125616683703 10.1109/YAC.2016.7804912 10.1016/j.apenergy.2014.02.057 10.1016/j.enbuild.2020.110590 |
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| References | Zhang (b0205) 2021; 285 Ahmed, Ortiz, González (b0125) 2017; 139 Mutani G, Casalengo M, Ramassotto MA. The effect of roof-integrated solar technologies on the energy performance of public buildings: The case study of the City of Turin (IT). In: 2018 IEEE international telecommunications energy conference (INTELEC). IEEE; 2018. p. 1–8. Kim, Stumpf, Kim (b0180) 2011; 20 Erell, Pearlmutter, Williamson (b0255) 2012 Huo, Ma, Cai, Liu, Mu (b0005) 2021; 232 Johari, Peronato, Sadeghian, Zhao, Widén (b0055) 2020; 128 Jin, Ma, Liu, Tang, Wang, Tang (b0260) 2020 Huang, Zheng (b0290) 2018; 8 Fu R, Zhang Z, Li L. Using LSTM and GRU neural network methods for traffic flow prediction. In Huo, Ma, Yu, Cai, Liu, Ren (b0110) 2021; 86 Wang, Hong, Li, Piette (b0080) 2021 Cerezo, Sokol, AlKhaled, Reinhart, Al-Mumin, Hajiah (b0120) 2017; 154 Scarselli, Gori, Tsoi, Hagenbuchner, Monfardini (b0210) 2008; 20 Somu, Mr, Ramamritham (b0200) 2021; 137 Wu, Tang, Zhu, Wang, Xie, Tan (b0230) 2019; 33 Government C. Koskela T, Lehtokangas M, Saarinen J, Kaski K. Time series prediction with multilayer perceptron, FIR and Elman neural networks. In Che, Purushotham, Cho, Sontag, Liu (b0285) 2018; 8 Ferrando, Causone, Hong, Chen (b0100) 2020 Wu, Lian, Xu, Wu, Chen (b0220) 2020; 34 [accessed April 4, 2021]. Grindvoll, Vermesan, Crosbie, Bahr, Dawood, Revel (b0170) 2012; 17 Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875; 2017. . Zhou (b0215) 2020; 1 Jain, Smith, Culligan, Taylor (b0085) 2014; 123 Chao MA, Tian Y, Kulkarni C, Goebel K, Fink O. Real-time model calibration with deep reinforcement learning. arXiv preprint arXiv:2006.04001; 2020. Neto, Fiorelli (b0195) 2008; 40 U. S. D. O. Energy. In Dong, Cao, Lee (b0190) 2005; 37 2018. International Building Performance Simulation Association; 2018. Chen, Deng, Hong (b0165) 2020; 277 Ying, He, Chen, Eksombatchai, Hamilton, Leskovec (b0235) 2018 Luo, Tang, Hong (b0090) 2020 Montgomery, Peck, Vining (b0265) 2021 [accessed January 20, 2021]. Ma, Cheng (b0075) 2016; 183 Liu, Han, Fu, Zhou, Lu, Xiong (b0225) 2020; 14 Pareja (b0240) 2020; 34 Hong, Chen, Luo, Luo, Lee (b0025) 2020; 168 Quan, Li, Augenbroe, Brown, Yang (b0050) 2015 Dino (b0155) 2020; 116 Nutkiewicz, Yang, Jain (b0040) 2018; 225 Wu (b0250) 2020; 278 Edwards, New, Parker (b0185) 2012; 49 Li (b0045) 2017; 141 Zhuravchak, Pedrero, del Granado, Nord, Brattebø (b0105) 2021; 238 Berrone, Ricart, Carrasco (b0175) 2016; 59 Amir Roth PP, Sawyer Karma, Lee David. Emerging Technologies Research and Development: DRAFT Research and Development Opportunities for Building Energy Modeling. U.S. Department of Energy; 2019. [Online]. Available Nageler, Koch, Mauthner, Leusbrock, Mach, Hochenauer (b0140) 2018; 179 Citeseer; 1996. p. 491–6. Ali U, Shamsi MH, Hoare C, O'Donnell J. GIS-based residential building energy modeling at district scale. In IEEE; 2016. p. 324–8. He, Wang, Luo, Shi, Xie, Meng (b0070) 2017; 35 Martín-Garín, Millán-García, Baïri, Millán-Medel, Sala-Lizarraga (b0160) 2018; 87 Kim, Song, Sung, Seo (b0145) 2019; 12 Reinhart, Cerezo Davila (b0095) 2016; 97 Ali (b0135) 2020; 279 Luo, Hong, Tang (b0035) 2020; 13 Wang, Shi, Lyu, Deng (b0275) 2017 2015, ch. Chapter 5: Increasing Efficiency of Building Systems and Technologies September 2015; 2015. Rakha, Gorodetsky (b0150) 2018; 93 Center A. Huang, Jones, Zhang, Peng, Li, Chan (b0030) 2020; 207 Nutkiewicz (10.1016/j.apenergy.2021.118231_b0040) 2018; 225 10.1016/j.apenergy.2021.118231_b0060 Martín-Garín (10.1016/j.apenergy.2021.118231_b0160) 2018; 87 Luo (10.1016/j.apenergy.2021.118231_b0035) 2020; 13 Wu (10.1016/j.apenergy.2021.118231_b0250) 2020; 278 Ma (10.1016/j.apenergy.2021.118231_b0075) 2016; 183 Dino (10.1016/j.apenergy.2021.118231_b0155) 2020; 116 Montgomery (10.1016/j.apenergy.2021.118231_b0265) 2021 Huang (10.1016/j.apenergy.2021.118231_b0290) 2018; 8 Jain (10.1016/j.apenergy.2021.118231_b0085) 2014; 123 10.1016/j.apenergy.2021.118231_b0020 Huang (10.1016/j.apenergy.2021.118231_b0030) 2020; 207 Dong (10.1016/j.apenergy.2021.118231_b0190) 2005; 37 Neto (10.1016/j.apenergy.2021.118231_b0195) 2008; 40 10.1016/j.apenergy.2021.118231_b0065 Kim (10.1016/j.apenergy.2021.118231_b0180) 2011; 20 He (10.1016/j.apenergy.2021.118231_b0070) 2017; 35 Edwards (10.1016/j.apenergy.2021.118231_b0185) 2012; 49 Pareja (10.1016/j.apenergy.2021.118231_b0240) 2020; 34 Wu (10.1016/j.apenergy.2021.118231_b0220) 2020; 34 Ali (10.1016/j.apenergy.2021.118231_b0135) 2020; 279 Reinhart (10.1016/j.apenergy.2021.118231_b0095) 2016; 97 Wang (10.1016/j.apenergy.2021.118231_b0275) 2017 10.1016/j.apenergy.2021.118231_b0115 Nageler (10.1016/j.apenergy.2021.118231_b0140) 2018; 179 Zhuravchak (10.1016/j.apenergy.2021.118231_b0105) 2021; 238 Li (10.1016/j.apenergy.2021.118231_b0045) 2017; 141 Kim (10.1016/j.apenergy.2021.118231_b0145) 2019; 12 Chen (10.1016/j.apenergy.2021.118231_b0165) 2020; 277 Ferrando (10.1016/j.apenergy.2021.118231_b0100) 2020 Hong (10.1016/j.apenergy.2021.118231_b0025) 2020; 168 Luo (10.1016/j.apenergy.2021.118231_b0090) 2020 10.1016/j.apenergy.2021.118231_b0270 Che (10.1016/j.apenergy.2021.118231_b0285) 2018; 8 Wang (10.1016/j.apenergy.2021.118231_b0080) 2021 10.1016/j.apenergy.2021.118231_b0280 10.1016/j.apenergy.2021.118231_b0245 Huo (10.1016/j.apenergy.2021.118231_b0110) 2021; 86 Jin (10.1016/j.apenergy.2021.118231_b0260) 2020 Ahmed (10.1016/j.apenergy.2021.118231_b0125) 2017; 139 Zhou (10.1016/j.apenergy.2021.118231_b0215) 2020; 1 Rakha (10.1016/j.apenergy.2021.118231_b0150) 2018; 93 Liu (10.1016/j.apenergy.2021.118231_b0225) 2020; 14 Wu (10.1016/j.apenergy.2021.118231_b0230) 2019; 33 Grindvoll (10.1016/j.apenergy.2021.118231_b0170) 2012; 17 Erell (10.1016/j.apenergy.2021.118231_b0255) 2012 Quan (10.1016/j.apenergy.2021.118231_b0050) 2015 Somu (10.1016/j.apenergy.2021.118231_b0200) 2021; 137 Cerezo (10.1016/j.apenergy.2021.118231_b0120) 2017; 154 10.1016/j.apenergy.2021.118231_b0015 Zhang (10.1016/j.apenergy.2021.118231_b0205) 2021; 285 Berrone (10.1016/j.apenergy.2021.118231_b0175) 2016; 59 Ying (10.1016/j.apenergy.2021.118231_b0235) 2018 Scarselli (10.1016/j.apenergy.2021.118231_b0210) 2008; 20 10.1016/j.apenergy.2021.118231_b0130 Johari (10.1016/j.apenergy.2021.118231_b0055) 2020; 128 Huo (10.1016/j.apenergy.2021.118231_b0005) 2021; 232 10.1016/j.apenergy.2021.118231_b0010 |
| References_xml | – volume: 285 year: 2021 ident: b0205 article-title: A review of machine learning in building load prediction publication-title: Appl Energy – volume: 183 start-page: 182 year: 2016 end-page: 192 ident: b0075 article-title: Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology publication-title: Appl Energy – volume: 35 start-page: 670 year: 2017 end-page: 685 ident: b0070 article-title: Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis publication-title: Int J Project Manage – volume: 20 start-page: 37 year: 2011 end-page: 43 ident: b0180 article-title: Analysis of an energy efficient building design through data mining approach publication-title: Autom Constr – volume: 93 start-page: 252 year: 2018 end-page: 264 ident: b0150 article-title: Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones publication-title: Autom Constr – reference: Center A. – reference: Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875; 2017. – reference: U. S. D. O. Energy. In: – volume: 141 start-page: 2445 year: 2017 end-page: 2457 ident: b0045 article-title: Modeling urban building energy use: A review of modeling approaches and procedures publication-title: Energy – volume: 123 start-page: 168 year: 2014 end-page: 178 ident: b0085 article-title: Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy publication-title: Appl Energy – start-page: 447 year: 2015 end-page: 469 ident: b0050 article-title: Urban data and building energy modeling: A GIS-based urban building energy modeling system using the urban-EPC engine publication-title: Planning support systems and smart cities – volume: 40 start-page: 2169 year: 2008 end-page: 2176 ident: b0195 article-title: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption publication-title: Energy Build – reference: Government C. – reference: Mutani G, Casalengo M, Ramassotto MA. The effect of roof-integrated solar technologies on the energy performance of public buildings: The case study of the City of Turin (IT). In: 2018 IEEE international telecommunications energy conference (INTELEC). IEEE; 2018. p. 1–8. – volume: 97 start-page: 196 year: 2016 end-page: 202 ident: b0095 article-title: Urban building energy modeling – a review of a nascent field publication-title: Build Environ – volume: 179 start-page: 333 year: 2018 end-page: 343 ident: b0140 article-title: Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach publication-title: Energy Build – reference: Amir Roth PP, Sawyer Karma, Lee David. Emerging Technologies Research and Development: DRAFT Research and Development Opportunities for Building Energy Modeling. U.S. Department of Energy; 2019. [Online]. Available: – year: 2012 ident: b0255 article-title: Urban microclimate: designing the spaces between buildings – volume: 34 start-page: 5363 year: 2020 end-page: 5370 ident: b0240 article-title: Evolvegcn: Evolving graph convolutional networks for dynamic graphs publication-title: Proc AAAI Conf Artificial Intel – volume: 20 start-page: 61 year: 2008 end-page: 80 ident: b0210 article-title: The graph neural network model publication-title: IEEE Trans Neural Networks – start-page: 66 year: 2020 end-page: 74 ident: b0260 article-title: Graph structure learning for robust graph neural networks publication-title: In: – volume: 13 start-page: 2382 year: 2020 ident: b0035 article-title: Modeling thermal interactions between buildings in an urban context publication-title: Energies – volume: 232 year: 2021 ident: b0005 article-title: Will the urbanization process influence the peak of carbon emissions in the building sector? A dynamic scenario simulation publication-title: Energy Build – volume: 207 year: 2020 ident: b0030 article-title: Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun, Hong Kong publication-title: Energy Build – reference: Fu R, Zhang Z, Li L. Using LSTM and GRU neural network methods for traffic flow prediction. In: – reference: . IEEE; 2016. p. 324–8. – year: 2021 ident: b0080 article-title: Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models publication-title: Adv Appl Energy – volume: 59 start-page: 39 year: 2016 end-page: 70 ident: b0175 article-title: The open kimono: Toward a general framework for open data initiatives in cities publication-title: California Manage Rev – volume: 128 year: 2020 ident: b0055 article-title: Urban building energy modeling: State of the art and future prospects publication-title: Renew Sustain Energy Rev – reference: [accessed April 4, 2021]. – reference: , 2015, ch. Chapter 5: Increasing Efficiency of Building Systems and Technologies September 2015; 2015. – volume: 34 start-page: 1054 year: 2020 end-page: 1061 ident: b0220 article-title: Graph convolutional networks with markov random field reasoning for social spammer detection publication-title: Proc AAAI Conf Artificial Intell – reference: . Citeseer; 1996. p. 491–6. – year: 2021 ident: b0265 article-title: Introduction to linear regression analysis – reference: , 2018. International Building Performance Simulation Association; 2018. – volume: 225 start-page: 1176 year: 2018 end-page: 1189 ident: b0040 article-title: Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow publication-title: Appl Energy – volume: 87 start-page: 201 year: 2018 end-page: 214 ident: b0160 article-title: Environmental monitoring system based on an Open Source Platform and the Internet of Things for a building energy retrofit publication-title: Autom Constr – year: 2020 ident: b0100 article-title: Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches publication-title: Sustainable Cities Soc – volume: 8 start-page: 37 year: 2018 ident: b0290 article-title: Energy and economic performance of solar cooling systems in the hot-summer and cold-winter zone publication-title: Buildings – volume: 17 start-page: 43 year: 2012 end-page: 61 ident: b0170 article-title: A wireless sensor network for intelligent building energy management based on multi communication standards-A case study publication-title: J Inform Technol Construct – volume: 137 year: 2021 ident: b0200 article-title: A deep learning framework for building energy consumption forecast publication-title: Renew Sustainable Energy Rev – volume: 14 start-page: 342 year: 2020 end-page: 350 ident: b0225 article-title: Multi-modal transportation recommendation with unified route representation learning publication-title: Proc VLDB Endowment – volume: 238 year: 2021 ident: b0105 article-title: Top-down spatially-explicit probabilistic estimation of building energy performance at a scale publication-title: Energy Build – volume: 279 year: 2020 ident: b0135 article-title: A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making publication-title: Appl Energy – volume: 116 year: 2020 ident: b0155 article-title: Image-based construction of building energy models using computer vision publication-title: Autom Constr – start-page: 974 year: 2018 end-page: 983 ident: b0235 article-title: Graph convolutional neural networks for web-scale recommender systems publication-title: In: – reference: [accessed January 20, 2021]. – volume: 8 start-page: 1 year: 2018 end-page: 12 ident: b0285 article-title: Recurrent neural networks for multivariate time series with missing values publication-title: Sci Rep – volume: 49 start-page: 591 year: 2012 end-page: 603 ident: b0185 article-title: Predicting future hourly residential electrical consumption: A machine learning case study publication-title: Energy Build – volume: 278 year: 2020 ident: b0250 article-title: A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption publication-title: Appl Energy – reference: Chao MA, Tian Y, Kulkarni C, Goebel K, Fink O. Real-time model calibration with deep reinforcement learning. arXiv preprint arXiv:2006.04001; 2020. – volume: 86 year: 2021 ident: b0110 article-title: Decoupling and decomposition analysis of residential building carbon emissions from residential income: Evidence from the provincial level in China publication-title: Environ Impact Assess Rev – reference: Ali U, Shamsi MH, Hoare C, O'Donnell J. GIS-based residential building energy modeling at district scale. In: – volume: 33 start-page: 346 year: 2019 end-page: 353 ident: b0230 article-title: Session-based recommendation with graph neural networks publication-title: Proc AAAI Conf Artificial Intell – volume: 37 start-page: 545 year: 2005 end-page: 553 ident: b0190 article-title: Applying support vector machines to predict building energy consumption in tropical region publication-title: Energy Build – volume: 139 year: 2017 ident: b0125 article-title: On the spatio-temporal end-user energy demands of a dense urban environment publication-title: J Sol Energy Eng – volume: 12 start-page: 433 year: 2019 ident: b0145 article-title: Development of a consecutive occupancy estimation framework for improving the energy demand prediction performance of building energy modeling tools publication-title: Energies – start-page: 238 year: 2020 end-page: 243 ident: b0090 article-title: Efficient computation of surface sunlit fractions in urban-scale building modeling using ray-tracing techniques publication-title: ASHRAE topical conference proceedings – reference: . – year: 2017 ident: b0275 article-title: Electricity consumption prediction using XGBoost based on discrete wavelet transform publication-title: DEStech Trans Comput Sci Eng – volume: 168 year: 2020 ident: b0025 article-title: Ten questions on urban building energy modeling publication-title: Build Environ – volume: 1 start-page: 57 year: 2020 end-page: 81 ident: b0215 article-title: Graph neural networks: A review of methods and applications publication-title: AI Open – volume: 154 start-page: 321 year: 2017 end-page: 334 ident: b0120 article-title: Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City publication-title: Energy Build – volume: 277 year: 2020 ident: b0165 article-title: Automatic and rapid calibration of urban building energy models by learning from energy performance database publication-title: Appl Energy – reference: Koskela T, Lehtokangas M, Saarinen J, Kaski K. Time series prediction with multilayer perceptron, FIR and Elman neural networks. In: – ident: 10.1016/j.apenergy.2021.118231_b0060 – volume: 86 year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0110 article-title: Decoupling and decomposition analysis of residential building carbon emissions from residential income: Evidence from the provincial level in China publication-title: Environ Impact Assess Rev doi: 10.1016/j.eiar.2020.106487 – ident: 10.1016/j.apenergy.2021.118231_b0115 – volume: 277 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0165 article-title: Automatic and rapid calibration of urban building energy models by learning from energy performance database publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115584 – volume: 14 start-page: 342 issue: 3 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0225 article-title: Multi-modal transportation recommendation with unified route representation learning publication-title: Proc VLDB Endowment doi: 10.14778/3430915.3430924 – volume: 8 start-page: 37 issue: 3 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0290 article-title: Energy and economic performance of solar cooling systems in the hot-summer and cold-winter zone publication-title: Buildings doi: 10.3390/buildings8030037 – volume: 20 start-page: 61 issue: 1 year: 2008 ident: 10.1016/j.apenergy.2021.118231_b0210 article-title: The graph neural network model publication-title: IEEE Trans Neural Networks doi: 10.1109/TNN.2008.2005605 – volume: 1 start-page: 57 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0215 article-title: Graph neural networks: A review of methods and applications publication-title: AI Open doi: 10.1016/j.aiopen.2021.01.001 – volume: 183 start-page: 182 year: 2016 ident: 10.1016/j.apenergy.2021.118231_b0075 article-title: Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology publication-title: Appl Energy doi: 10.1016/j.apenergy.2016.08.079 – volume: 40 start-page: 2169 issue: 12 year: 2008 ident: 10.1016/j.apenergy.2021.118231_b0195 article-title: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption publication-title: Energy Build doi: 10.1016/j.enbuild.2008.06.013 – volume: 137 year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0200 article-title: A deep learning framework for building energy consumption forecast publication-title: Renew Sustainable Energy Rev doi: 10.1016/j.rser.2020.110591 – volume: 207 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0030 article-title: Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun, Hong Kong publication-title: Energy Build doi: 10.1016/j.enbuild.2019.109580 – year: 2012 ident: 10.1016/j.apenergy.2021.118231_b0255 – volume: 285 year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0205 article-title: A review of machine learning in building load prediction publication-title: Appl Energy doi: 10.1016/j.apenergy.2021.116452 – start-page: 447 year: 2015 ident: 10.1016/j.apenergy.2021.118231_b0050 article-title: Urban data and building energy modeling: A GIS-based urban building energy modeling system using the urban-EPC engine – volume: 93 start-page: 252 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0150 article-title: Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones publication-title: Autom Constr doi: 10.1016/j.autcon.2018.05.002 – volume: 168 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0025 article-title: Ten questions on urban building energy modeling publication-title: Build Environ doi: 10.1016/j.buildenv.2019.106508 – start-page: 238 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0090 article-title: Efficient computation of surface sunlit fractions in urban-scale building modeling using ray-tracing techniques – year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0080 article-title: Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models publication-title: Adv Appl Energy – ident: 10.1016/j.apenergy.2021.118231_b0130 doi: 10.1109/INTLEC.2018.8612398 – start-page: 66 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0260 article-title: Graph structure learning for robust graph neural networks – volume: 128 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0055 article-title: Urban building energy modeling: State of the art and future prospects publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2020.109902 – year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0100 article-title: Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches publication-title: Sustainable Cities Soc doi: 10.1016/j.scs.2020.102408 – volume: 116 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0155 article-title: Image-based construction of building energy models using computer vision publication-title: Autom Constr doi: 10.1016/j.autcon.2020.103231 – volume: 17 start-page: 43 year: 2012 ident: 10.1016/j.apenergy.2021.118231_b0170 article-title: A wireless sensor network for intelligent building energy management based on multi communication standards-A case study publication-title: J Inform Technol Construct – volume: 279 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0135 article-title: A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115834 – volume: 33 start-page: 346 issue: 01 year: 2019 ident: 10.1016/j.apenergy.2021.118231_b0230 article-title: Session-based recommendation with graph neural networks publication-title: Proc AAAI Conf Artificial Intell – volume: 225 start-page: 1176 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0040 article-title: Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.05.023 – start-page: 974 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0235 article-title: Graph convolutional neural networks for web-scale recommender systems – volume: 87 start-page: 201 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0160 article-title: Environmental monitoring system based on an Open Source Platform and the Internet of Things for a building energy retrofit publication-title: Autom Constr doi: 10.1016/j.autcon.2017.12.017 – volume: 20 start-page: 37 issue: 1 year: 2011 ident: 10.1016/j.apenergy.2021.118231_b0180 article-title: Analysis of an energy efficient building design through data mining approach publication-title: Autom Constr doi: 10.1016/j.autcon.2010.07.006 – ident: 10.1016/j.apenergy.2021.118231_b0020 – ident: 10.1016/j.apenergy.2021.118231_b0010 – volume: 278 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0250 article-title: A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115656 – volume: 141 start-page: 2445 year: 2017 ident: 10.1016/j.apenergy.2021.118231_b0045 article-title: Modeling urban building energy use: A review of modeling approaches and procedures publication-title: Energy doi: 10.1016/j.energy.2017.11.071 – ident: 10.1016/j.apenergy.2021.118231_b0270 – ident: 10.1016/j.apenergy.2021.118231_b0245 doi: 10.24963/ijcai.2018/505 – volume: 97 start-page: 196 year: 2016 ident: 10.1016/j.apenergy.2021.118231_b0095 article-title: Urban building energy modeling – a review of a nascent field publication-title: Build Environ doi: 10.1016/j.buildenv.2015.12.001 – volume: 238 year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0105 article-title: Top-down spatially-explicit probabilistic estimation of building energy performance at a scale publication-title: Energy Build doi: 10.1016/j.enbuild.2021.110786 – volume: 35 start-page: 670 issue: 4 year: 2017 ident: 10.1016/j.apenergy.2021.118231_b0070 article-title: Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis publication-title: Int J Project Manage doi: 10.1016/j.ijproman.2016.08.001 – volume: 34 start-page: 1054 issue: 01 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0220 article-title: Graph convolutional networks with markov random field reasoning for social spammer detection publication-title: Proc AAAI Conf Artificial Intell – volume: 8 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0285 article-title: Recurrent neural networks for multivariate time series with missing values publication-title: Sci Rep doi: 10.1038/s41598-018-24271-9 – volume: 13 start-page: 2382 issue: 9 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0035 article-title: Modeling thermal interactions between buildings in an urban context publication-title: Energies doi: 10.3390/en13092382 – volume: 12 start-page: 433 issue: 3 year: 2019 ident: 10.1016/j.apenergy.2021.118231_b0145 article-title: Development of a consecutive occupancy estimation framework for improving the energy demand prediction performance of building energy modeling tools publication-title: Energies doi: 10.3390/en12030433 – ident: 10.1016/j.apenergy.2021.118231_b0065 – volume: 49 start-page: 591 year: 2012 ident: 10.1016/j.apenergy.2021.118231_b0185 article-title: Predicting future hourly residential electrical consumption: A machine learning case study publication-title: Energy Build doi: 10.1016/j.enbuild.2012.03.010 – volume: 139 issue: 4 year: 2017 ident: 10.1016/j.apenergy.2021.118231_b0125 article-title: On the spatio-temporal end-user energy demands of a dense urban environment publication-title: J Sol Energy Eng doi: 10.1115/1.4036545 – volume: 154 start-page: 321 year: 2017 ident: 10.1016/j.apenergy.2021.118231_b0120 article-title: Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City publication-title: Energy Build doi: 10.1016/j.enbuild.2017.08.029 – volume: 37 start-page: 545 issue: 5 year: 2005 ident: 10.1016/j.apenergy.2021.118231_b0190 article-title: Applying support vector machines to predict building energy consumption in tropical region publication-title: Energy Build doi: 10.1016/j.enbuild.2004.09.009 – year: 2017 ident: 10.1016/j.apenergy.2021.118231_b0275 article-title: Electricity consumption prediction using XGBoost based on discrete wavelet transform publication-title: DEStech Trans Comput Sci Eng – volume: 179 start-page: 333 year: 2018 ident: 10.1016/j.apenergy.2021.118231_b0140 article-title: Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach publication-title: Energy Build doi: 10.1016/j.enbuild.2018.09.034 – volume: 59 start-page: 39 issue: 1 year: 2016 ident: 10.1016/j.apenergy.2021.118231_b0175 article-title: The open kimono: Toward a general framework for open data initiatives in cities publication-title: California Manage Rev doi: 10.1177/0008125616683703 – ident: 10.1016/j.apenergy.2021.118231_b0280 doi: 10.1109/YAC.2016.7804912 – ident: 10.1016/j.apenergy.2021.118231_b0015 – year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0265 – volume: 123 start-page: 168 year: 2014 ident: 10.1016/j.apenergy.2021.118231_b0085 article-title: Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy publication-title: Appl Energy doi: 10.1016/j.apenergy.2014.02.057 – volume: 34 start-page: 5363 issue: 04 year: 2020 ident: 10.1016/j.apenergy.2021.118231_b0240 article-title: Evolvegcn: Evolving graph convolutional networks for dynamic graphs publication-title: Proc AAAI Conf Artificial Intel – volume: 232 year: 2021 ident: 10.1016/j.apenergy.2021.118231_b0005 article-title: Will the urbanization process influence the peak of carbon emissions in the building sector? A dynamic scenario simulation publication-title: Energy Build doi: 10.1016/j.enbuild.2020.110590 |
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