A Simplified and Automated Building Energy Retrofit Analysis Approach
Year: 2026
Type: article
Abstract: Retrofitting existing buildings is widely recognized as a critical strategy for achieving global decarbonization goals. As a part of this effort, several tools have been developed for building retrofit analysis, each offering distinct advantages and limitations. However, the current approaches and tools still lack the capability to generate well-calibrated detailed building energy models that can evaluate both individual and combined energy efficiency measures. Moreover, no existing analysis tool can identify the most cost-optimal combination of retrofit measures through a comprehensive optimization search using different objectives. To address these shortcomings, this paper describes a new Simplified and Automated Building Energy Retrofit (SABER) analysis approach and tool. The SABER tool is a Python-based interactive platform designed to assist users by automatically creating detailed energy models of existing buildings. It incorporates a novel automatic calibration algorithm that adjusts operational schedules using building energy signature characteristics, ensuring accurate model performance. In addition, SABER can assess various building energy efficiency measures using a sequential search technique to determine the most cost-effective retrofit packages. This paper describes the key functionalities of SABER and demonstrates its capabilities through two residential building case studies. By integrating several key features into a unified framework, SABER represents a significant step toward the next generation of building energy retrofit analysis tools that can effectively assist the industry’s transition to a sustainable future.
Source: Energies
DOI: 10.3390/en19081907
Institutions: University of Colorado Boulder, Virginia Tech
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: National Science Foundation
OpenAlex ID: W7154420747
