A multi-scale life-cycle energy and greenhouse-gas emissions analysis model for residential buildings

The building and transportation sectors account for approximately 75% of CO₂ emissions. Accurate forecasts of future energy usage are an important step towards reaching carbon mitigation commitments for city policymakers. Beyond identifying sources of emission intensity for a region, the forecast mechanism must be capable of compensating for gaps in available data and of accounting for the uncertainties behind the dynamics of an urban system. By considering a range of possible scenarios, the prediction model can identify recurring sources of high energy consumption and fine-tune areas of priority with incoming data. Although there are many studies dedicated to modeling techniques for predicting household building energy consumption, very few focus on household transportation energy consumption using household variables. Buildings connect different networks of transportation and influence transit patterns. Developing a robust and integrated residential, commercial, and transportation energy use model is useful for multiple planning purposes. This is crucial for future urban development; there is a critical need to analyze the integrated impacts of transportation infrastructure and building construction on the environment. Machine learning techniques in artificial intelligence (AI) predictive modeling have become popular in energy prediction due to their ability to capture nonlinear and complex relationships. Nevertheless, developing a comprehensive understanding of the inference mechanisms in AI models and ensuring trust in their predictions is challenging. This is because AI models are mostly of high complexity and low interpretability. There is a need to analyze the insights of energy models to interpret local and global features and to demonstrate how existing bottom-up approaches can augment scenario planning forecasts. This dissertation will address the abovementioned integration needs and interpretability challenges in the following four steps: (1) Examine four machine learning approaches for predicting household transportation energy consumption. These are decision trees, random forest, neural networks, and elastic net regularization analyses. These models will be compared in terms of both accuracy and interpretability. This step aims to determine the best ML application for transportation energy models. (2) Predict residential and commercial building energy demand by generating bottom-up models using datasets commonly available in the United States. (a) The residential model applies machine learning methods to match records in the Residential Energy Consumption Survey (RECS) with Public Use Microdata samples. This produces a synthetic household energy distribution at the neighborhood scale. (b) The commercial building energy model is generated by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Commercial building energy consumption is predicted at the building and household level in order to aggregate it with the residential (step 2a) and transportation models (step 1). (3) Evaluate model transparency and explainability for the residential, commercial, and transportation models produced in steps 1 and 2. The application of Local Interpretable Model-Agnostic Explanation (LIME) and SHAP (SHapley Additive exPlanations) will support advanced machine learning techniques in the transportation and building energy research. (4) Analyze the impact of alternative policy scenarios on urban energy consumption. Sustainability scenarios will be constructed from available projections of demographic and socioeconomic data for Philadelphia County. The goal of this step is to apply urban planning priorities to our models to inform our understanding of their predicted environmental outcomes. This project extends urban energy analysis by developing AI and XAI techniques for the three most energy intensive sectors of urban development. The integrated assessment of the transportation, residential and commercial sectors is critical to assessing and prioritizing urban planning scenarios for sustainable urban growth. These results are essential in decision-making among urban planners and building and transportation engineers.

Rapid population growth has led to significant demand for residential buildings around the world. Consequently, there is a growing energy demand associated with increased greenhouse gas (GHG) emissions. The residential building energy demand in arid countries such as Saudi Arabia is supplied with fossil fuel. The existing consumption pattern of fossil fuels in Saudi Arabia is less sustainable due to the depletion of fossil fuel resources and resulting environmental impacts. Buildings built in hot and arid climatic conditions demand high energy for creating habitable indoor environments. Enormous energy is required to maintain a cool temperature in hot regions. Moreover, climate change may have different impacts on hot climatic regions and affect building energy use differently. This means that different building interventions may be required to improve the performance of building energy performance in these geographical regions, thereby reducing the emissions of GHGs. In this study, this framework has been applied to Saudi Arabia, a hot and arid country. This research proposes a community–government partnership framework for developing low-carbon energy in residential buildings. This study focuses on both the operational energy demand and a cost-benefit analysis of energy use in the selected geographical regions for the next 30 years (i.e., 2050). The proposed framework primarily consists of four stages: (1) data collection on energy use (2020 to 2050); (2) setting a GHG emissions reduction target; (3) a building intervention approach by the community by considering cost, energy, and GHG emissions using the Technique for Order of Performance by Similarity to the Ideal Solution (TOPSIS) to select the best combinations in each geographical region conducting 180 simulations; and (4) a clean energy approach by the government using grey relational analysis (GRA) to select the best clean energy system on the grid. The clean energy approach selected six different renewable power generation systems (i.e., PV array, wind turbine, hybrid system) with two storage systems (i.e., battery bank and a combination of electrolyte, fuel cell, and hydrogen tank storage). This approach is designed to identify the best clean energy systems in five geographical regions with thirty scenario analyses to define renewable energy-economy benefits. This framework informs through many engineering tools such as residential building energy analysis, renewable energy analysis, multi-criteria decision analysis (MCDA) techniques, and cost-benefit analysis. Integration between these engineering tools with the set of energy policies and public initiatives is designed to achieve further directives in the effort to reach greater efficiency while downsizing residential energy demands. The results of this paper propose that a certain level of cooperation is required between the community and the government in terms of financial investments and the best combinations of retrofits and clean energy measures. Thus, retrofits and clean energy measures can help save carbon emissions (enhancing the energy performance of buildings) and decrease associated GHG emissions, which can help policy makers to achieve low-carbon emission communities.

Purpose This study aimed to examine the ways of minimizing embodied energy in residential buildings in Sri Lanka. Accordingly, the study identified current practices and barriers to reducing embodied energy in residential buildings in Sri Lanka. Then, effective measures were identified to overcome the barriers to reducing embodied energy in residential buildings in Sri Lanka. Design/methodology/approach The qualitative research approach was adopted in the study. Semi-structured interviews were conducted as the data collection. Sixteen experts relating to the construction industry were selected using a purposive sampling technique. The collected data were analyzed using manual content analysis. Findings The study identified practices and barriers to reducing embodied energy in residential buildings in Sri Lanka, along with effective measures to mitigate the identified barriers. Findings hold significant value for industry practitioners to design low-embodied energy residential buildings in Sri Lanka. Originality/value While several studies have separately investigated the reduction of operational energy, the novel contribution of this study lies in unique exploration of the reduction of embodied energy especially in residential buildings in Sri Lanka. Despite existing literature, there has been a noticeable gap in investigating how to reduce embodied energy in buildings. Therefore, the findings of this study offer innovative approaches to designing low-embodied energy buildings.

The increase of the energy efficiency in buildings and greenhouses is important for reducing the use of fossil fuels and the emissions of greenhouse gases. Energy efficiency evaluation requires the consideration of both the embodied and the operational energy. Many estimations regarding the embodied and the operational energy in various types of buildings have been reported so far. However, studies regarding the embodied energy in agricultural greenhouses are rare while there are many estimations regarding their operational energy. The goal of our study is the comparison of the embodied and the operational energy in residential buildings and in agricultural greenhouses. The embodied and operational energies are compared in greenhouses as well as in low-energy and in conventional residential buildings. Our results indicate that the ratio of embodied energy to life-cycle energy in low-energy residential buildings and in nearly-zero energy buildings varies in the range at 36% to 83% that is significantly higher than the ratio in conventional residential buildings which is in the range of 6% to 20%. The ratio of embodied energy to life-cycle energy in agricultural greenhouses, at 0.86% - 70.41%, varies significantly depending on many parameters. The importance of carbon emissions related to embodied energy in low-energy buildings, in net-zero energy buildings and in agricultural greenhouses is highlighted. Our work could be useful to policy makers who are willing to accelerate the green transition to a low carbon economy in the coming decades.

The energy efficiency of urban infrastructure is in large part influenced by the efficient utilization of transportation and building systems. Opportunities for more efficient utilization of transportation and building systems are available in the context of commercial office building/site selection. Office location decision makers have an opportunity to select buildings and locations that potentially minimize building and transportation energy consumption within a given urban context. The objective of the research presented in this paper was to apply a calculation framework for estimating the potential transportation energy and building energy consumption of commercial office building/site alternatives. The calculation framework was applied to case studies of commercial office buildings/sites that represent typical developments found in the transportation and land-use context of the Atlanta, Georgia, metropolitan region. The framework leverages building energy simulation models and regional travel demand model data to estimate energy performance under uncertainty. Importantly, the calculation results indicate that transportation is a major determinant of commercial office building/site energy performance. Incorporation of the framework into sustainable development policy or market transformation tools (e.g., green building rating systems) could accommodate more performance-based planning of efficient infrastructure utilization.

Towards a comprehensive life cycle energy analysis framework for residential buildings

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Energy use and related greenhouse gas emissions of buildings have a significant effect on the environment. To reduce energy consumption in buildings, it is important to understand energy use occurring across the building life cycle. While previous studies have shown the significance of both the energy required for building operation as well as the energy embodied in initial building construction, an understanding of the total energy embodied in replacement materials over a building's life is not as well developed. One of the key factors affecting this ‘recurring’ embodied energy is the service life of materials. The aim of this study was to investigate the relationship between the service life of materials and the life cycle energy demand associated with contemporary residential buildings in Australia. The initial embodied energy, operational energy and recurrent embodied energy of a detached residential building were calculated with material service life values based on average figures obtained from the literature. These values were then varied to reflect the extent of service life variability likely for a selection of the main building materials and recurring embodied energy recalculated for each scenario. Selected materials of the building were then replaced with commonly used alternatives and the building's initial and recurrent embodied energy recalculated for a range of materials service life scenarios. The results from this initial study indicate that the service life of materials can have a considerable effect on total energy demand associated with a building over its life. This demonstrates the need for further clarity around the service life of materials and the importance of considering the durability of materials when designing and managing buildings for improved energy efficiency. Results from this study also suggest the importance of including the recurrent embodied energy of buildings in building life cycle energy analyses, which in this case represented between 19 and 31% of the life cycle energy of the building as built and 21 and 34% with the use of alternative materials.

Summary Community‐wide greenhouse gas (GHG) emissions accounting is confounded by the relatively small spatial size of cities compared to nations—due to which, energy use in essential infrastructures serving cities, such as commuter and airline transport, energy supply, water supply, wastewater infrastructures, and others, often occurs outside the boundaries of the cities using them. The trans‐boundary infrastructure supply chain footprint (TBIF) GHG emissions accounting method, tested in eight U.S. cities, incorporates supply chain aspects of these trans‐boundary infrastructures serving cities, and is akin to an expanded geographic GHG emissions inventory. This article shows the results from applying the TBIF method in the rapidly developing city of Delhi, India. The objectives of this research are to (1) describe the data availability for implementing the TBIF method within a rapidly industrializing country, using the case of Delhi, India; (2) identify methodological differences in implementation of the TBIF method between Indian versus U.S. cities; and (3) compare broad energy use metrics between Delhi and U.S. cities, demonstrated by Denver, Colorado, USA, whose energy use characteristics and TBIF GHG emissions have previously been shown to be similar to U.S. per capita averages. This article concludes that most data required to implement the TBIF method in Delhi are readily available, and the methodology could be translated from U.S. to Indian cities. Delhi's 2009 community‐wide GHG emissions totaled 40.3 million metric tonnes of carbon dioxide equivalents (t CO 2 ‐eq), which are normalized to yield 2.3 t CO 2 ‐eq per capita; nationally, India reports its average per capita GHG emissions at 1.5 t CO 2 ‐eq. In‐boundary GHG emissions contributed to 68% of Delhi's total, where end use (including electricity) energy in residential buildings, commercial and industrial usage, and fuel used in surface transportation contributed 24%, 19%, and 21%, respectively. The remaining 4% of the in‐boundary GHG emissions were from waste disposal, water and wastewater treatment, and cattle. Trans‐boundary infrastructures were estimated to equal 32% of Delhi's TBIF GHG emissions, with 5% attributed to fuel processing, 3% to air travel, 10% to cement, and 14% to food production outside the city.

We analyze 100 case studies, which were conducted in 23 countries, and contrast their data on embodied and operational energy in residential and commercial buildings. The case studies include conventional, retrofit, low-energy, passive, and net-zero energy buildings. The buildings have different lifetimes varying from 25 to 100 years. We calculate the estimated total Life Cycle Energy (LCE) as the sum of Embodied Energy (EE) and Operational Energy (OE). The LCE in the 100 case studies ranges from 50.8 to 1840 MJ/m2 per year. Our results show that operational energy significantly dominates the life cycle energy of the buildings by an average of 419 MJ/m2 per year and an average share of 72%. The share of embodied energy increases with decreasing operational energy. However, the overall LCE decreases significantly when the operational energy decreases. Naturally, the assumptions on the lifetime of the buildings have a great impact on the LCE. We conclude that operational energy should be primarily reduced in order to decrease greenhouse gas emissions from the existing building stock because most of the buildings are already built and changes in the embodied energy are often obtained only through new construction or deep retrofit strategies. Depending on the strategy to decrease OE, the share of EE was found to show wide fluctuations within the case studies, ranging from 4% up to 100%. In addition, most of the operational energy consumption has been reported by using energy simulation tools. Only about 14% of the case studies had metered operational energy data. In order to create more accurate data, metering of buildings should be considered in future case studies.

Analyzing factors necessitating conservation of energy in residential buildings of Indian subcontinent: A DEMATEL approach

Life cycle energy analysis (LCEA) is used to assign energy values to product flows in each phase of an activity's life cycle. In the case of a residential building, this usually comprises energy embodied in the manufacture of building materials, energy used in the building's operation, and in periodic maintenance. In order to place these amounts of energy in a national context, the energy embodied in other goods and services consumed by householders also needs to be considered. This paper uses LCEA to demonstrate the need for considering not only the life cycle energy of the building but also the life cycle energy attributable to activities being undertaken by actual users of the building. The life cycle energy of an Australian residential building as well as common activities of households are analysed and simulated over a 30 year period using a worked example of a two bedroom, brick-veneer, semi-detached unit. The importance of considering the energy embodied in the initial construction of a residential building as well as the consumption of goods and services by householders is demonstrated as having long-term implications. In order to encourage sustainable living practices it is suggested that architects more closely consider the activities of householders when designing residential buildings, especially in temperate climates. The paper concludes by identifying future areas of research for LCEA in the residential sector. Les études de cycle de vie antérieures à: la construction ont tendance à omettre les phases situées après la démolition. Si le recyclage n'a pas été prévu, il n'est donc pas possible d'en évaluer les bénéfices. Une étude paramétrique portant sur une maison individuelle fait le point sur les économies d'énergie potentielles après la démolition rendues possibles par la réutilisation des divers matériaux de construction. Les résultats indiquent qu'il est peut être plus important de concevoir un bâtiment en vue de son recyclage que d'employer des matériaux exigeant peu d'énergie lors de la fabrication, ce qui fait que la mise au point d'un recyclage efficace dépend de sa prise en compte et de son intégration lors de la phase de conception; de cette façon la réutilisation et l'adaptation des éléments de base existants sont des composantes importantes de ce recyclage.

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