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Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model's performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model's behavior and optimize the model's performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study's findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale. [ABSTRACT FROM AUTHOR]

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption). [ABSTRACT FROM AUTHOR]

Building energy retrofits can reduce emissions and increase cost savings. Some retrofits that can deliver higher emissions savings are not popular due to a lack of economic justifications. Financial incentives can be used to change buyer perception around such retrofits. This study proposes a framework to identify the best-performing retrofit strategies for a given building cluster and the optimal incentive amounts to promote the chosen strategies, accounting for uncertainties, stakeholder priorities, and budget constraints. The proposed framework was demonstrated using a case study complemented with policy insights. Life cycle cost savings and capital cost significantly impact retrofit purchase decisions. Case study results showed that retrofitting houses heated with electricity can produce significant cost savings. However, adopting energy-conscious behaviours in houses heated with natural gas and injecting renewable natural gas into the gas supply can produce two times more emissions savings achieved by any retrofit strategy applied to an electrically heated house. This indicates the need for adopting performance-based incentives over the prescriptive approach to reward occupant efforts in addition to asset performance. Despite potential life cycle cost savings, incentives must be complemented with low-interest loans to promote retrofit strategies carrying higher capital costs. [ABSTRACT FROM AUTHOR]

Eco-certification schemes are usually launched with various incentives provided by local governments to facilitate green building development and building energy retrofits. A number of barriers to building energy retrofitting have been identified in previous literature, while the barriers to the eco-certification of existing buildings are under-researched. Drawing on a set of building data retrievable from the BEAM Society and other sources, we carried out an analysis and found the building energy retrofitting, as well as the certification process, were unwelcomed in multi-owned residential buildings. The identified shortfall is put forward from the perspectives of transaction cost theory and agency theory. The findings reveal that high transaction costs incurred during negotiations and coordination among a large number of co-owners within a typical apartment building can outweigh the benefits of retrofitting and eco-certification. Besides, the remuneration structure of third-party property management agents discourages agents from facilitating co-owners to initiate retrofitting. This study provides significant implications for policymakers to understand the concerns of building owners and managers over the decisions and the processes of both the building energy retrofits and eco-certification. The problems and barriers unveiled in this study will facilitate the refining of current energy efficiency policies and related incentives designs. [ABSTRACT FROM AUTHOR]