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1. Construction Cost Decomposition of Residential Building Energy Retrofit

   https://doi.org/10.3390/buildings13061363  

   Zhao, Dong; Mo, Yunjeong (June 2023, Buildings)

   Buildings are responsible for significant energy consumption and carbon emissions. Green buildings, which incorporate advanced building technologies, offer a solution to reducing energy use. However, high costs associated with green building development present a barrier to widespread adoption. Retrofit projects, involving remodeling, renovation, and redevelopment of existing buildings, offer a viable solution. While prior studies have examined the cost analysis of green and non-green buildings, there is a lack of evidence comparing new and retrofit projects. This study aims to address this gap by providing empirical evidence for the cost decomposition and benefits of new and retrofit projects. Data on energy use, building technology, and costs from 235 certified green homes in the United States were collected, and cost benefits were evaluated. Results show that retrofit projects cost, on average, $1270.5/m2 ($118.0/ft2), which is 30% less than new projects. Land acquisition and development account for 35% of retrofit costs, six times greater than new projects. Excluding land costs, retrofit projects cost, on average, $733.88/m2 ($68.2/ft2), 49% less than new projects. Retrofit projects use similar building technologies as new projects and produce larger energy savings. The cost-benefit values generated by retrofit projects are 86% greater than new projects when considering land costs and 142% greater without considering land costs. These findings contribute to cost management for complex building projects and energy policy for sustainable development. Retrofitting offers great potential to promote the green building movement and suggests effective subsidy programs as a public policy implication. 

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2. Recurrent Mobility: Urban Conduits for Diffusion of Energy Efficiency

   https://doi.org/10.1038/s41598-019-56372-4  

   Mohammadi, Neda; Taylor, John E. (December 2019, Scientific Reports)

   Abstract Recent advances in energy technologies, policies, and practices have accelerated the global rate of improvements in energy efficiency, bringing the energy targets identified in the 2030 United Nations (UN) Sustainable Development Agenda within reach. However, Target 7.3 requires this rate to double by 2030, demanding a more substantial response to energy interventions. At present, energy interventions are failing to reach optimal levels of adoption in buildings, which are the largest urban energy consumers. This is due to a combination of direct and indirect effects generally referred to as the energy efficiency gap. Here, we compare over 18.8 million positional records of individuals against Greater London’s buildings energy consumption records over the course of one year. We demonstrate that indirect (i.e., spillover) effects, arising fromrecurrent mobility, govern the diffusion of urban buildings’ energy efficiency, far outpacing direct effects. This has been understood as a consequence of underlying spatiotemporal dependencies at the intersection of energy use and social interactions. We add to this the critical role of recurrent mobility (i.e., the mobility of those urban populations who repeatedly visit certain locations, such as home and work) as a diffusion conduit. These findings suggest that in order to improve the current levels of adoption, interventions must target times and locations that function as dense hubs of energy consumption and social interactions. Recurrent mobility thus provides a viable complement to existing targeted intervention approaches aimed at improving energy efficiency, supporting efforts to meet the UN’s 2030 energy targets. 

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3. Combined design and control optimization of heat pumps in residential buildings

   https://doi.org/10.1016/j.energy.2025.136317  

   Vadali, Phani Arvind; Krarti, Moncef (July 2025, Energy)

   Elsevier (Ed.)

   Electrification of buildings through deployment of heat pumps requires innovative design and control strategies to reduce their energy demands on the grid. Instead of the sequential approach of optimizing the design specifications and control strategies, this paper considers the benefits of the combined and simultaneous optimization of design capacities and control settings for heat pumps when specified for US residential buildings. A Genetic Algorithm optimizer is used to simultaneously adjust the main and supplementary coil capacities for the heat pump as well as the indoor temperature setpoints to minimize annual heating and cooling energy needs as well as occupant thermal discomfort levels. In comparison to design and control baselines, it is found that simultaneous optimization can achieve 21% and 7% reductions in heating and cooling annual energy consumption for the cases of variable speed and single speed heat pumps. Moreover, the analysis results indicate that these reductions are nearly double the savings obtained when design only and control only based optimizations are considered. The presented combined design and control optimization approach could potentially provide an effective paradigm shift in specifying heat pump systems for residential buildings. 

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4. Analyzing the Impact of Decarbonizing Residential Heating on the Electric Distribution Grid

   https://doi.org/10.1145/3607114.3607119  

   Wamburu, John; Bashir, Noman; Irwin, David; Shenoy, Prashant (June 2023, ACM SIGEnergy Energy Informatics Review)

   Heating buildings using fossil fuels such as natural gas, propane and oil makes up a significant proportion of the aggregate carbon emissions every year. Because of this, there is a strong interest in decarbonizing residential heating systems using new technologies such as electric heat pumps. In this paper, we conduct a data-driven optimization study to analyze the potential of replacing gas heating with electric heat pumps to reduce CO 2 emission in a city-wide distribution grid. We conduct an in-depth analysis of gas consumption in the city and the resulting carbon emissions. We then present a flexible multi-objective optimization (MOO) framework that optimizes carbon emission reduction while also maximizing other aspects of the energy transition such as carbon-efficiency, and minimizing energy inefficiency in buildings. Our results show that replacing gas with electric heat pumps has the potential to cut carbon emissions by up to 81%. We also show that optimizing for other aspects such as carbon-efficiency and energy inefficiency introduces tradeoffs with carbon emission reduction that must be considered during transition. Finally, we present a detailed analysis of the implication of proposed transition strategies on the household energy consumption and utility bills, electric grid upgrades, and decarbonization policies. We compute the additional energy demand from electric heat pumps at the household as well as the transformer level and discuss how our results can inform decarbonization policies at city scale. 

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5. Non-Intrusive Air Leakage Detection in Residential Homes

   https://doi.org/10.1145/3154273.3154345  

   Pathak, Nilavra; Lachut, David; Roy, Nirmalya; Banerjee, Nilanjan; Robucci, Ryan (January 2018, ICDCN '18 Proceedings of the 19th International Conference on Distributed Computing and Networking)

   Air leakages pose a major problem in both residential and commercial buildings. They increase the utility bill and result in excessive usage of Heating Ventilation and Air Conditioning (HVAC) systems, which impacts the environment and causes discomfort to residents. Repairing air leakages in a building is an expensive and time intensive task. Even detecting the leakages can require extensive professional testing. In this paper, we propose a method to identify the leaky homes from a set, provided their energy consumption data is accessible from residential smart meters. In the first phase, we employ a Non-Intrusive Load Monitoring (NILM) technique to disaggregate the HVAC data from total power consumption for several homes. We propose a recurrent neural network and a denoising autoencoder based approach to identify the 'ON' and 'OFF' cycles of the HVACs and their overall usages. We categorize the typical HVAC consumption of about 200 homes and any probable insulation and leakage problems using the Air Changes per Hour at 50 Pa (ACH50) metric in the Dataport datasets. We perform our proposed NILM analysis on different granularities of smart meter data such as 1 min, 15 mins, and 1 hour to observe its effect on classifying the leaky homes. Our results show that disaggregation can be used to identify the residential air-conditioning, at 1 min granularity which in turn helps us to identify the leaky potential homes, with an accuracy of 86%. 

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