Year |
Citation |
Score |
2020 |
Sonta AJ, Jain RK. Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference Ieee Embedded Systems Letters. 12: 41-44. DOI: 10.1109/Les.2019.2937316 |
0.344 |
|
2020 |
Roth J, Lim B, Jain RK, Grueneich D. Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective Energy Policy. 139: 111327. DOI: 10.1016/J.Enpol.2020.111327 |
0.399 |
|
2020 |
Azar E, O'Brien W, Carlucci S, Hong T, Sonta A, Kim J, Andargie MS, Abuimara T, Asmar ME, Jain RK, Ouf MM, Tahmasebi F, Zhou J. Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications Energy and Buildings. 224: 110292. DOI: 10.1016/J.Enbuild.2020.110292 |
0.312 |
|
2020 |
Roth J, Brown HA, Jain RK. Harnessing smart meter data for a Multitiered Energy Management Performance Indicators (MEMPI) framework: A facility manager informed approach Applied Energy. 276: 115435. DOI: 10.1016/J.Apenergy.2020.115435 |
0.376 |
|
2019 |
Roth J, Bailey A, Choudhary S, Jain RK. Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data Computing in Civil Engineering. 459-467. DOI: 10.1061/9780784482445.059 |
0.323 |
|
2019 |
Yang Z, Gupta K, Jain RK. DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems Energy Procedia. 158: 6478-6483. DOI: 10.1016/J.Egypro.2019.01.114 |
0.4 |
|
2019 |
Srivastava C, Yang Z, Jain RK. Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey Building and Environment. 157: 139-164. DOI: 10.1016/J.Buildenv.2019.04.016 |
0.352 |
|
2019 |
Jin M, Jain R, Spanos C, Jia Q, Norford LK, Kjærgaard M, Yan J. Energy-cyber-physical systems Applied Energy. 256: 113939. DOI: 10.1016/J.Apenergy.2019.113939 |
0.311 |
|
2018 |
Yang Z, Roth J, Jain RK. DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis Energy and Buildings. 163: 58-69. DOI: 10.1016/J.Enbuild.2017.12.040 |
0.402 |
|
2018 |
Nutkiewicz A, Jain RK, Bardhan R. Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India Applied Energy. 231: 433-445. DOI: 10.1016/J.Apenergy.2018.09.002 |
0.337 |
|
2018 |
Nutkiewicz A, Yang Z, Jain RK. 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. 225: 1176-1189. DOI: 10.1016/J.Apenergy.2018.05.023 |
0.403 |
|
2018 |
Khosrowpour A, Jain RK, Taylor JE, Peschiera G, Chen J, Gulbinas R. A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation Applied Energy. 218: 304-316. DOI: 10.1016/J.Apenergy.2018.02.148 |
0.495 |
|
2018 |
Sonta AJ, Simmons PE, Jain RK. Understanding building occupant activities at scale: An integrated knowledge-based and data-driven approach Advanced Engineering Informatics. 37: 1-13. DOI: 10.1016/J.Aei.2018.04.009 |
0.338 |
|
2017 |
Sonta AJ, Jain RK, Gulbinas R, Moura JMF, Taylor JE. OESPG: Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings Journal of Computing in Civil Engineering. 31: 4017017. DOI: 10.1061/(Asce)Cp.1943-5487.0000663 |
0.363 |
|
2017 |
Jain RK, Qin J, Rajagopal R. Data-driven planning of distributed energy resources amidst socio-technical complexities Nature Energy. 2. DOI: 10.1038/Nenergy.2017.112 |
0.304 |
|
2017 |
Nutkiewicz A, Yang Z, Jain RK. Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow Energy Procedia. 142: 2114-2119. DOI: 10.1016/J.Egypro.2017.12.614 |
0.394 |
|
2015 |
Kontokosta CE, Jain RK. Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy Sustainable Cities and Society. 18: 44-55. DOI: 10.1016/J.Scs.2015.05.007 |
0.313 |
|
2014 |
Jain RK, Moura JMF, Kontokosta CE. Big data + big cities: Graph signals of urban air pollution [Exploratory SP] Ieee Signal Processing Magazine. 31: 130-136. DOI: 10.1109/Msp.2014.2330357 |
0.306 |
|
2014 |
Gulbinas R, Jain RK, Taylor JE, Peschiera G, Golparvar-Fard M. Network ecoinformatics: Development of a social ecofeedback system to drive energy efficiency in residential buildings Journal of Computing in Civil Engineering. 28: 89-98. DOI: 10.1061/(Asce)Cp.1943-5487.0000319 |
0.382 |
|
2014 |
Jeong SH, Gulbinas R, Jain RK, Taylor JE. The impact of combined water and energy consumption eco-feedback on conservation Energy and Buildings. 80: 114-119. DOI: 10.1016/J.Enbuild.2014.05.013 |
0.305 |
|
2014 |
Gulbinas R, Jain RK, Taylor JE. BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy Applied Energy. 136: 1076-1084. DOI: 10.1016/J.Apenergy.2014.07.034 |
0.401 |
|
2014 |
Jain RK, Smith KM, Culligan PJ, Taylor JE. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy Applied Energy. 123: 168-178. DOI: 10.1016/J.Apenergy.2014.02.057 |
0.603 |
|
2013 |
Jain RK, Gulbinas R, Taylor JE, Culligan PJ. Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback Energy and Buildings. 66: 119-127. DOI: 10.1016/J.Enbuild.2013.06.029 |
0.576 |
|
2013 |
Jain RK, Taylor JE, Culligan PJ. Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings Energy and Buildings. 64: 408-414. DOI: 10.1016/J.Enbuild.2013.05.011 |
0.599 |
|
2013 |
Chen J, Jain RK, Taylor JE. Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption Applied Energy. 105: 358-368. DOI: 10.1016/J.Apenergy.2012.12.036 |
0.493 |
|
2012 |
Jain RK, Taylor JE, Peschiera G. Assessing Eco-Feedback Interface Usage and Design to Drive Energy Efficiency in Buildings Energy and Buildings. 48: 8-17. DOI: 10.1016/J.Enbuild.2011.12.033 |
0.343 |
|
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