Citation: Alrobaie, A.; Krarti, M. A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits. Energies 2022, 15, 7824. https:// doi.org/10.3390/en15217824 Academic Editor: Francesco Minichiello Received: 28 September 2022 Accepted: 18 October 2022 Published: 22 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Review A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits Abdurahman Alrobaie and Moncef Krarti * Building Systems Program, University of Colorado Boulder, Boulder, CO 80309, USA * Correspondence: moncef.krarti@colorado.edu; Tel.: +1-303-492-3389 Abstract: Although the energy and cost benefits for retrofitting existing buildings are promising, several challenges remain for accurate measurement and verification (M&V) analysis to estimate these benefits. Due to the rapid development in advanced metering infrastructure (AMI), data- driven approaches are becoming more effective than deterministic methods in developing baseline energy models for existing buildings using historical energy consumption data. The literature review presented in this paper provides an extensive summary of data-driven approaches suitable for building energy consumption prediction needed for M&V applications. The presented literature review describes commonly used data-driven modeling approaches including linear regressions, decision trees, ensemble methods, support vector machine, deep learning, and kernel regressions. The advantages and limitations of each data-driven modeling approach and its variants are discussed, including their cited applications. Additionally, feature engineering methods used in building energy data-driven modeling are outlined and described based on reported case studies to outline commonly used building features as well as selection and processing techniques of the most relevant features. This review highlights the gap between the listed existing frameworks and recently reported case studies using data-driven models. As a conclusion, this review demonstrates the need for a flexible M&V analysis framework to identify the best data-driven methods and their associated features depending on the building type and retrofit measures. Keywords: baseline models; data-driven modeling; energy conservation measures; measurement and verification; retrofitted buildings 1. Introduction Buildings are the largest energy-consuming sector, with a global share of 35% of energy consumption, exceeding industry and transportation [1]. It is estimated that 85% of the building energy consumption is attributed to heating, ventilation, and air conditioning (HVAC), lighting, and plug loads. Moreover, residential buildings account for approx- imately 63% [1] of the total energy used by the building sector. In terms of electricity consumption, buildings take 50% of the world’s electricity consumption [1]. According to the U.S. Energy Information Administration (EIA), projections show that the residential and commercial buildings will increase by 1.3% per year from 2018 to 2050 for countries in the Organization for Economic Cooperation and Development (OECD), while non-OECD countries will experience an average of 2% growth annually [2]. Several studies have ana- lyzed the historical and current status of energy consumed by buildings and have projected future increases in building-related energy use globally [3] or in specific regions such as China [4], the European Union [5], and Gulf Cooperation Council countries [6]. The high energy consumption by the built environment has significant detrimental effects on the environment and the climate. Several governmental agencies and global organizations are adopting initiatives and programs that target the reduction of energy consumption in the building sector. For instance, the U.S. Department of Energy has set a 2030 goal of tripling Energies 2022, 15, 7824. https://doi.org/10.3390/en15217824 https://www.mdpi.com/journal/energies