IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v197y2017icp303-317.html
   My bibliography  Save this item

A data-driven predictive model of city-scale energy use in buildings

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Lee, Ruda & Kim, Dongsu & Yoon, Jongho & Kang, Eunho & Cho, Heejin & Kim, Jinhwi, 2024. "Development and calibration of apartment building energy model based on architectural and energy consumption characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
  2. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  3. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
  4. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
  5. Evelien Verellen & Karen Allacker, 2022. "Developing a Building Stock Model to Enable Clustered Renovation—The City of Leuven as Case Study," Sustainability, MDPI, vol. 14(10), pages 1-25, May.
  6. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
  7. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
  8. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
  9. (Giancarlo) Falcocchio, John C. & Malik, Awais & Kontokosta, Constantine E., 2018. "A data-driven methodology for equitable value-capture financing of public transit operations and maintenance," Transport Policy, Elsevier, vol. 66(C), pages 107-115.
  10. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
  11. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
  12. Amoako, Samuel & Andoh, Francis Kwaw & Asmah, Emmanuel Ekow, 2023. "Technological advancement, sectoral growth, and electricity consumption in Ghana," Energy, Elsevier, vol. 263(PB).
  13. Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
  14. Wang, Chao & Du, Yuyan & Li, Hailong & Wallin, Fredrik & Min, Geyong, 2019. "New methods for clustering district heating users based on consumption patterns," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  15. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  16. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
  17. Meibodi, Saleh S. & Loveridge, Fleur, 2022. "The future role of energy geostructures in fifth generation district heating and cooling networks," Energy, Elsevier, vol. 240(C).
  18. Dai, Shuang & Eames, Matt & Vinai, Raffaele & Sucala, Voicu Ion, 2025. "EnergyNet: A modality-aware attention fusion network for building energy efficiency classification," Applied Energy, Elsevier, vol. 379(C).
  19. Maher Selim & Ryan Zhou & Wenying Feng & Peter Quinsey, 2021. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design," Energies, MDPI, vol. 14(1), pages 1-15, January.
  20. Mine Isik & P. Ozge Kaplan, 2020. "Understanding Technology, Fuel, Market and Policy Drivers for New York State’s Power Sector Transformation," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
  21. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "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," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
  22. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
  23. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
  24. Qin, Chao & Yan, Qingyou & He, Gang, 2019. "Integrated energy systems planning with electricity, heat and gas using particle swarm optimization," Energy, Elsevier, vol. 188(C).
  25. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
  26. Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
  27. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  28. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
  29. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
  30. Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
  31. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  32. Wang, Wei & Liu, Ke & Zhang, Muxing & Shen, Yuchi & Jing, Rui & Xu, Xiaodong, 2021. "From simulation to data-driven approach: A framework of integrating urban morphology to low-energy urban design," Renewable Energy, Elsevier, vol. 179(C), pages 2016-2035.
  33. Won-Jun Park & Hye-Mi Kim, 2018. "Study on the Improvement of Expected Energy Savings and Actual Energy Savings in Apartments," Sustainability, MDPI, vol. 10(4), pages 1-15, April.
  34. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
  35. Johansson, Tim & Olofsson, Thomas & Mangold, Mikael, 2017. "Development of an energy atlas for renovation of the multifamily building stock in Sweden," Applied Energy, Elsevier, vol. 203(C), pages 723-736.
  36. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
  37. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
  38. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
  39. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
  40. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  41. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
  42. Tahir Mahmood & Muhammad Asif, 2024. "Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms," Energies, MDPI, vol. 17(19), pages 1-17, October.
  43. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
  44. Sina Narimani Abar & Martin Schulwitz & Martin Faulstich, 2023. "The Impact of Urban Form and Density on Residential Energy Use: A Systematic Review," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
  45. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
  46. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
  47. Zinzi, Michele & Carnielo, Emiliano & Mattoni, Benedetta, 2018. "On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy," Applied Energy, Elsevier, vol. 221(C), pages 148-160.
  48. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
  49. Hanif, Sarmad & Mukherjee, Monish & Poudel, Shiva & Yu, Min Gyung & Jinsiwale, Rohit A. & Hardy, Trevor D. & Reeve, Hayden M., 2023. "Analyzing at-scale distribution grid response to extreme temperatures," Applied Energy, Elsevier, vol. 337(C).
  50. Jeffrey D. Spitler & Signhild Gehlin, 2019. "Measured Performance of a Mixed-Use Commercial-Building Ground Source Heat Pump System in Sweden," Energies, MDPI, vol. 12(10), pages 1-34, May.
  51. Yu, Xinran & Zou, Zhengbo & Ergan, Semiha, 2023. "Extracting principal building variables from automatically collected urban scale façade images for energy conservation through deep transfer learning," Applied Energy, Elsevier, vol. 344(C).
  52. Frankel, Matthew & Xing, Lu & Chewning, Connor & Sela, Lina, 2021. "Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings," Applied Energy, Elsevier, vol. 281(C).
  53. Di Leo, Senatro & Pietrapertosa, Filomena & Salvia, Monica & Cosmi, Carmelina, 2021. "Contribution of the Basilicata region to decarbonisation of the energy system: results of a scenario analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  54. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  55. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
  56. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
  57. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
  58. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
  59. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
  60. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
  61. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
  62. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.