Matching Items (34)
Description
City administrators and real-estate developers have been setting up rather aggressive energy efficiency targets. This, in turn, has led the building science research groups across the globe to focus on urban scale building performance studies and level of abstraction associated with the simulations of the same. The increasing maturity of the stakeholders towards energy efficiency and creating comfortable working environment has led researchers to develop methodologies and tools for addressing the policy driven interventions whether it’s urban level energy systems, buildings’ operational optimization or retrofit guidelines. Typically, these large-scale simulations are carried out by grouping buildings based on their design similarities i.e. standardization of the buildings. Such an approach does not necessarily lead to potential working inputs which can make decision-making effective. To address this, a novel approach is proposed in the present study.
The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation.
Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models.
The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits.
From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction.
The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation.
Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models.
The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits.
From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction.
ContributorsPathak, Maharshi P. (Author) / Reddy, T Agami (Thesis advisor) / Addison, Marlin (Committee member) / Bryan, Harvey (Committee member) / Arizona State University (Publisher)
Created2017
Description
Passive cooling techniques, specifically passive downdraft cooling (PDC), have proven to be a solution that can address issues associated with air conditioning (AC). Globally, over 100 buildings have integrated PDC in its different forms, most of which use direct evaporative cooling. Even though all surveyed buildings were energy efficient and cost-effective and most surveyed buildings were thermally comfortable, application of PDC remains limited. This study aims to advance performance of the single stage passive downdraft evaporative cooling tower (PDECT), and expand its applicability beyond the hot dry conditions where it is typically used, by designing and testing a multi-stage passive and hybrid downdraft cooling tower (PHDCT). Experimental evaluation on half-scale prototypes of these towers was conducted in Tempe, Arizona, during the hot dry and hot humid days of Summer, 2017. Ambient air dry-bulb temperatures ranged between 73.0°F with 82.9 percent coincident relative humidity, and 123.4°F with 7.8 percent coincident relative humidity. Cooling systems in both towers were operated simultaneously to evaluate performance under identical conditions.
Results indicated that the hybrid tower outperformed the single stage tower under all ambient conditions and that towers site water consumption was at least 2 times lower than source water required by electric powered AC. Under hot dry conditions, the single stage tower produced average temperature drops of 35°F (5°F higher than what was reported in the literature), average air velocities of 200 fpm, and average cooling capacities of 4 tons. Furthermore, the hybrid tower produced average temperature drops of 45°F (50°F in certain operation modes), average air velocities of 160 fpm, and average cooling capacities exceeding 4 tons. Under hot humid conditions, temperature drops from the single stage tower were limited to the ambient air wet-bulb temperatures whereas drops continued beyond the wet-bulb in the hybrid tower, resulting in 60 percent decline in the former’s cooling capacity while maintaining the capacity of the latter. The outcomes from this study will act as an incentive for designers to consider incorporating PDC into their designs as a viable replacement/supplement to AC; thus, reducing the impact of the built environment on the natural environment.
Results indicated that the hybrid tower outperformed the single stage tower under all ambient conditions and that towers site water consumption was at least 2 times lower than source water required by electric powered AC. Under hot dry conditions, the single stage tower produced average temperature drops of 35°F (5°F higher than what was reported in the literature), average air velocities of 200 fpm, and average cooling capacities of 4 tons. Furthermore, the hybrid tower produced average temperature drops of 45°F (50°F in certain operation modes), average air velocities of 160 fpm, and average cooling capacities exceeding 4 tons. Under hot humid conditions, temperature drops from the single stage tower were limited to the ambient air wet-bulb temperatures whereas drops continued beyond the wet-bulb in the hybrid tower, resulting in 60 percent decline in the former’s cooling capacity while maintaining the capacity of the latter. The outcomes from this study will act as an incentive for designers to consider incorporating PDC into their designs as a viable replacement/supplement to AC; thus, reducing the impact of the built environment on the natural environment.
ContributorsAl-Hassawi, Omar Dhia Sadulah (Author) / Bryan, Harvey (Thesis advisor) / Reddy, T Agami (Committee member) / Chalfoun, Nader (Committee member) / Arizona State University (Publisher)
Created2017
Description
In 2018, building energy use accounted for over 40% of total primary energy consumption in the United States; moreover, buildings account for ~40% of national CO2 emissions. One method for curbing energy use in buildings is to apply Demand Side Management (DSM) strategies, which focus on reducing the energy demand through various technological and operational approaches in different building sectors.
This PhD research examines the integration of DSM strategies in existing residential and commercial buildings in the Phoenix, Arizona metropolitan area, a hot-arid climate. The author proposes three different case studies to evaluate the effectiveness of one DSM strategy in buildings, namely the integration of Phase Change Materials (PCMs). PCMs store energy in the freezing process and use that stored energy in the melting process to reduce the energy demand. The goal of these case studies is to analyze the potential of each strategy to reduce peak load and overall energy consumption in existing buildings.
First, this dissertation discusses the efficacy of coupling PCMs with precooling strategies in residential buildings to reduce peak demand. The author took a case study approach and simulated two precooling strategies, with and without PCM integration, in two sample single-family homes to assess the impact of the DSM strategies (i.e., precooling and PCM integration) on load shifting and load shedding in each home.
Second, this research addresses the feasibility of using PCMs as sensible and latent heat storage in commercial buildings. The author documents the process of choosing buildings for PCM installation, as well as the selection of PCMs for retrofitting purposes. Commercial building case studies compare experimental and simulation results, focusing on the impact of the PCMs on reducing the total annual energy demand and energy cost.
Finally, this research proposes a novel process for selecting PCMs as energy efficiency measures for building retrofits. This process facilitates the selection of a building and PCM that are complementary. Implementation of this process has not yet been tested; however, the process was developed based on experimental and simulation results from prior studies, and it would alleviate many of the PCM performance issues documented in those studies.
This PhD research examines the integration of DSM strategies in existing residential and commercial buildings in the Phoenix, Arizona metropolitan area, a hot-arid climate. The author proposes three different case studies to evaluate the effectiveness of one DSM strategy in buildings, namely the integration of Phase Change Materials (PCMs). PCMs store energy in the freezing process and use that stored energy in the melting process to reduce the energy demand. The goal of these case studies is to analyze the potential of each strategy to reduce peak load and overall energy consumption in existing buildings.
First, this dissertation discusses the efficacy of coupling PCMs with precooling strategies in residential buildings to reduce peak demand. The author took a case study approach and simulated two precooling strategies, with and without PCM integration, in two sample single-family homes to assess the impact of the DSM strategies (i.e., precooling and PCM integration) on load shifting and load shedding in each home.
Second, this research addresses the feasibility of using PCMs as sensible and latent heat storage in commercial buildings. The author documents the process of choosing buildings for PCM installation, as well as the selection of PCMs for retrofitting purposes. Commercial building case studies compare experimental and simulation results, focusing on the impact of the PCMs on reducing the total annual energy demand and energy cost.
Finally, this research proposes a novel process for selecting PCMs as energy efficiency measures for building retrofits. This process facilitates the selection of a building and PCM that are complementary. Implementation of this process has not yet been tested; however, the process was developed based on experimental and simulation results from prior studies, and it would alleviate many of the PCM performance issues documented in those studies.
ContributorsAskari Tari, Neda (Author) / Parrish, Kristen (Thesis advisor) / Bryan, Harvey (Committee member) / Reddy, T. Agami (Committee member) / Arizona State University (Publisher)
Created2020
Description
Building-integrated carbon-capture (BICC) is an envisioned mechanism capable of absorbing carbon dioxide (CO2) from the air to be stored and then converted into useful carbon-based materials without negatively impacting the environment. This dissertation builds on the authors' previous work, in which building façades were treated as artificial leaves capable of providing shade to lower solar heat gain, while simultaneously capturing CO2 through the air filters attached to the building façades by attempting a different approach capable of capturing CO2 within buildings. This dissertation presents the author’s work on BICC, where buildings are envisioned as CO2 reservoirs or vacuums, into which mechanical systems introduce fresh air, and through human activities, the air within the building becomes enriched with CO2 before being pushed out back to the outer environment. The design of a carbon-capture mechanism will take advantage of the ventilation side of existing HVAC systems, through which BICC captures CO2 from the exhaust-enriched CO2 air. BICC will utilize existing opportunities and components within buildings represented in the high CO2 concentration in buildings, ventilation guidelines, mechanical equipment represented in air handling unit and air duct network, in addition to natural gas grid connectivity. BICC will capture CO2 through buildings' mechanical system, and the captured CO2 would then be converted into renewable methane to be injected into the existing natural gas pipeline network. This dissertation will investigate the potential of BICC to offset carbon emissions from multiple commercial building types and will present a utilization strategy for the captured carbon.
ContributorsBen Salamah, Fahad (Author) / Bryan, Harvey (Thesis advisor) / Lackner, Klaus (Committee member) / Reddy, T Agami (Committee member) / Arizona State University (Publisher)
Created2021