A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits

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.