| A review on time series forecasting techniques for building energy consumption C Deb, F Zhang, J Yang, SE Lee, KW Shah Renewable and Sustainable Energy Reviews 74, 902-924, 2017 | 742 | 2017 |
| Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique F Zhang, C Deb, SE Lee, J Yang, KW Shah Energy and Buildings 126, 94-103, 2016 | 263 | 2016 |
| Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks C Deb, LS Eang, J Yang, M Santamouris Energy and Buildings 121, 284-297, 2016 | 263 | 2016 |
| k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement J Yang, C Ning, C Deb, F Zhang, D Cheong, SE Lee, C Sekhar, KW Tham Energy and Buildings 146, 27-37, 2017 | 146 | 2017 |
| Determining key variables influencing energy consumption in office buildings through cluster analysis of pre-and post-retrofit building data C Deb, SE Lee Energy and Buildings 159, 228-245, 2018 | 105 | 2018 |
| Evaluation of thermal comfort in a rail terminal location in India C Deb, A Ramachandraiah Building and Environment 45 (11), 2571-2580, 2010 | 98 | 2010 |
| PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings EM Saber, SE Lee, S Manthapuri, W Yi, C Deb Energy 71, 588-595, 2014 | 94 | 2014 |
| The significance of physiological equivalent temperature (PET) in outdoor thermal comfort studies C Deb, A Ramachandraiah International Journal of Engineering Science and Technology 2 (7), 2825-2828, 2010 | 76 | 2010 |
| Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings C Deb, SE Lee, M Santamouris Solar Energy 163, 32-44, 2018 | 69 | 2018 |
| Review of data-driven energy modelling techniques for building retrofit C Deb, A Schlueter Renewable and Sustainable Energy Reviews 144, 110990, 2021 | 63 | 2021 |
| Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data P Westermann, C Deb, A Schlueter, R Evins Applied Energy 264, 114715, 2020 | 60 | 2020 |
| Energy performance model development and occupancy number identification of institutional buildings J Yang, M Santamouris, SE Lee, C Deb Energy and Buildings 123, 192-204, 2016 | 60 | 2016 |
| Do energy performance certificates allow reliable predictions of actual energy consumption and savings? Learning from the Swiss national database S Cozza, J Chambers, C Deb, JL Scartezzini, A Schlüter, MK Patel Energy and Buildings 224, 110235, 2020 | 51 | 2020 |
| Forecasting energy consumption of institutional buildings in Singapore C Deb, LS Eang, J Yang, M Santamouris Procedia Engineering 121, 1734-1740, 2015 | 48 | 2015 |
| A simplified tool for building layout design based on thermal comfort simulations P Anand, C Deb, R Alur Frontiers of Architectural Research 6 (2), 218-230, 2017 | 41 | 2017 |
| Wireless sensor network for estimating building performance M Frei, C Deb, R Stadler, Z Nagy, A Schlueter Automation in Construction 111, 103043, 2020 | 40 | 2020 |
| A simple technique to classify urban locations with respect to human thermal comfort: Proposing the HXG scale C Deb, A Ramachandraiah Building and environment 46 (6), 1321-1328, 2011 | 31 | 2011 |
| Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings P Anand, C Deb, K Yan, J Yang, D Cheong, C Sekhar Energy and Buildings 252, 111478, 2021 | 25 | 2021 |
| Automated load disaggregation for residences with electrical resistance heating C Deb, M Frei, J Hofer, A Schlueter Energy and Buildings 182, 61-74, 2019 | 22 | 2019 |
| A machine learning-based framework for cost-optimal building retrofit C Deb, Z Dai, A Schlueter Applied Energy 294, 116990, 2021 | 17 | 2021 |
Junjing YangNational University of SingaporeVerified email at nus.edu.sg
