Embodied Life Cycle Assessment (LCA) comparison of residential building retrofit measures in Atlanta

Shirazi, Arezoo; Ashuri, Baabak

doi:10.1016/j.buildenv.2020.106644

Cited by 56 publications

(24 citation statements)

References 25 publications

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“…The paper concludes that an "advanced refurbishment" would provide 17% embodied and 9% total greenhouse gas savings compared to new construction complying with contemporary standards. In contrast, [47] identified an increase of approximately 20% in EEIs of residential building renovations in USA. This increase was outweighed by only 1.6 to 5 years of resulting operational energy savings.…”

Section: Introductionmentioning

confidence: 84%

“…There are several other publications such as [44][45][46] dealing with this field, but this number is still negligible compared to the thousands of publications describing other building-related LCAs. Moreover, only a few of these publications analysed the efficiency of renovations in comparison to new construction: eight papers cited in [11], five in [31], and handful of others, such as [46,47]. Overall, these studies confirm that the renovation of existing (especially older) buildings can significantly reduce environmental impacts related to their life cycle.…”

Section: Introductionmentioning

confidence: 96%

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Life-Cycle Assessment of a Rural Terraced House: A Struggle with Sustainability of Building Renovations

Struhala

Ostrý

2021

Energies

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Contemporary research stresses the need to reduce mankind’s environmental impacts and achieve sustainability. One of the keys to this is the construction sector. New buildings have to comply with strict limits regarding resource consumption (energy, water use, etc.). However, they make up only a fraction of the existing building stock. Renovations of existing buildings are therefore essential for the reduction of the environmental impacts in the construction sector. This paper illustrates the situation using a case study of a rural terraced house in a village near Brno, Czech Republic. It compares the life-cycle assessment (LCA) of the original house and its proposed renovation as well as demolition followed by new construction. The LCA covers both the initial embodied environmental impacts (EEIs) and the 60-year operation of the house with several variants of energy sources. The results show that the proposed renovation would reduce overall environmental impacts (OEIs) of the house by up to 90% and the demolition and new construction by up to 93% depending on the selected energy sources. As such, the results confirm the importance of renovations and the installation of environmentally-friendly energy sources for achieving sustainability in the construction sector. They also show the desirability of the replacement of inefficient old buildings by new construction in specific cases.

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Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 96%

Life-Cycle Assessment of a Rural Terraced House: A Struggle with Sustainability of Building Renovations

Struhala

Ostrý

2021

Energies

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“…There are several approaches to reduce the energy consumption of buildings, which can be classified into two main groups: those based on “passive” strategies, understanding “passive” as those ones that do not require an active management of the building, as the improvement of building envelopes [ 11 ], the replacement of systems (HVAC, DHW, etc.) with more efficient ones [ 12 ], the use of renewable energies, etc. ; and those based on “active” strategies that basically try to achieve an optimal management of the building as a whole [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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“…Accurate models for energy consumption prediction are important in buildings energy planning and optimization, energy monitoring, efficient building operation, etc. [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Classification of retrofit measures for residential buildings according to the global cost

2021

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Data-driven black-box surrogate models are widely used in research related to buildings energy efficiency. They are based on machine learning techniques, learn from available data, and act as a replacement for or an addition to complex and computationally intensive knowledge-based models. Surrogate models can predict energy demand, indoor air temperature, or occupants behavior; explore search space in optimization problems; learn control rules; etc. This paper analyzes surrogate models that classify building retrofit measures directly according to the global cost. In addition, they quantify the importance of each variable for the classification process. The models are based on random forest classifiers, which are fast and powerful ensemble learners. They can be applied to effectively reduce search spaces when optimizing energy renovation measures or to rapidly identify projects that deserve financial support. This approach is applied to two residential buildings and three scenarios of price development. The training process uses a small share of retrofit options assessed with standard calculations of the heating and cooling demands, as well as the global cost. The results show very high classification performance, even when the models are trained with small and imbalanced training sets. The obtained recall, precision, and F-score values are mostly above 95%, except for extremely small training sets.

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Embodied Life Cycle Assessment (LCA) comparison of residential building retrofit measures in Atlanta

Cited by 56 publications

References 25 publications

Life-Cycle Assessment of a Rural Terraced House: A Struggle with Sustainability of Building Renovations

Life-Cycle Assessment of a Rural Terraced House: A Struggle with Sustainability of Building Renovations

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Classification of retrofit measures for residential buildings according to the global cost

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