Comparison of energy-efficiency benchmarking methodologies for residential buildings Gyanesh Gupta a,⇑ , Sanjay Mathur a , Jyotirmay Mathur a,⇑ , Bibhu Kalyan Nayak b a Centre for Energy and Environment, Malaviya National Institute of Technology, Jaipur 302017, Rajasthan, India b Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India article info Article history: Received 5 January 2023 Revised 6 February 2023 Accepted 17 February 2023 Available online 23 February 2023 Keywords: Energy-efficiency benchmarking Composite indicator Energy benchmarking methodologies Comparative performance abstract Benchmarking with defined metrics is an excellent tool to track progress and obtain the desired goals. Benchmarking end-use energy in the residential sector has been a topic of great interest among research- ers in recent times. This research analyses the buildings’ energy performance based on a devised bench- marking procedure for residential buildings in the Indian city of Jaipur. Creation of benchmarking system involves collection, analysis and verification of the results obtained from a similar set of data into consid- eration. This study explores the relationship between the Energy Performance Index (EPI) and with other influencing factors like; Area, end-use appliances (Fridge, AC, Cooler, etc.) to ensure the contribution of each variable towards the buildings’ end-use energy consumption. In this study, information has been collected and analysed from over 2700 houses of Jaipur City. The independent and dependent variables have been identified, and a five-star benchmarking framework has been developed. There are very few studies available investigating the effectiveness of various benchmarking techniques, the domain still lacks a comparative performance study of black-box and gray-box benchmarking techniques. This study has selected two black box approaches and one gray box approach to design a benchmarking model and has compared the energy performance of sample buildings. According to the Pearson and Spearman cor- relation coefficients, Multiple Linear Regression (MLR) and Bayesian ranking scores are the most consis- tent for energy benchmarking of the residential building sector. This study proposes a novel implementation of the composite indicator (C.I.) as a platform for designing energy benchmarking tables. Finally, the study concludes with recommendations for future work to employ numerous or hybrid com- binations of benchmarking methodologies that are expected to provide a more accurate depiction of the energy performance of buildings. Ó 2023 Elsevier B.V. All rights reserved. 1. Introduction There has been a consistent increase in energy consumption in buildings worldwide. The India Energy Outlook 2021 report stated that India is expected to develop more than double its built fabric over the next two decades. The building sector demand for energy has risen to 40% since 2000. Increase in the standard of living is one of the key contributors to this increase in energy demand. The elec- tricity consumption by domestic appliances has nearly tripled in the last two decades, growing faster than total energy demand [1]. Looking at the current global emission and protocols signing in, every country is devising measures for energy conservation to decrease the total carbon emission of the respective country. Build- ings, being the most energy-intensive sector, calls for immediate attention. Building energy benchmarking and development of per- formance indicator systems is an effective way to increase build- ing’s energy efficiency. Fig. 1 shows the three effective techniques of building certification that is used for energy perfor- mance benchmarking. India is a country with vast territory, diverse range of geograph- ical, climatic characteristics and levels of economic development. India has been predominantly divided into five climatic zones that https://doi.org/10.1016/j.enbuild.2023.112920 0378-7788/Ó 2023 Elsevier B.V. All rights reserved. Abbreviations: EPI, Energy Performance Index; SFA, Stochastic Frontier Analysis; MLR, Multiple Linear Regression; DT, Decision Tree; C.I, Composite Indicator; ANN, Artificial Neural Network; NBC, National Building Council; PCA, Principal Compo- nent Analysis; BHK, Bedroom, Hall, and Kitchen; EWS, Economic Weaker Section; EUI, Energy Use Intensity; LIG, Lower Income Group; CBECS, Commercial Buildings Energy Consumption Survey; MIG, Middle Income Group; HVAC, Heating and Ventilation and Air Conditioning; HIG, High Income Group; SVM, Support Vector Machine; BR, Bayesian Regression; DEA, Data Envelopment Analysis. ⇑ Corresponding authors. E-mail addresses: gyaneshgupta121291@gmail.com (G. Gupta), jyotirmay- mathur@gmail.com (J. Mathur). Energy & Buildings 285 (2023) 112920 Contents lists available at ScienceDirect Energy & Buildings journal homepage: www.elsevier.com/locate/enb