Matching Items (14)
Description
Schools all around the country are improving the performance of their buildings by adopting high performance design principles. Higher levels of energy efficiency can pave the way for K-12 Schools to achieve net zero energy (NZE) conditions, a state where the energy generated by on-site renewable sources are sufficient to meet the cumulative annual energy demands of the facility. A key capability for the proliferation of Net Zero Energy Buildings (NZEB) is the need for a design methodology that identifies the optimum mix of energy efficient design features to be incorporated into the building. The design methodology should take into account the interaction effects of various energy efficiency measures as well as their associated costs so that life cycle cost can be minimized for the entire life span of the building.
This research aims at developing such a methodology for generating cost effective net zero energy solutions for school buildings. The Department of Energy (DOE) prototype primary school, meant to serve as the starting baseline, was modeled in the building energy simulation software eQUEST and made compliant with the requirement of ASHRAE 90.1-2007. Commonly used efficiency measures, for which credible initial cost and maintenance data were available, were selected as the parametric design set. An initial sensitivity analysis was conducted by using the Morris Method to rank the efficiency measures in terms of their importance and interaction strengths. A sequential search technique was adopted to search the solution space and identify combinations that lie near the Pareto-optimal front; this allowed various minimum cost design solutions to be identified corresponding to different energy savings levels.
Based on the results of this study, it was found that the cost optimal combination of measures over the 30 year analysis span resulted in an annual energy cost reduction of 47%, while net zero site energy conditions were achieved by the addition of a 435 kW photovoltaic generation system that covered 73% of the roof area. The simple payback period for the additional technology required to achieve NZE conditions was calculated to be 26.3 years and carried a 37.4% premium over the initial building construction cost. The study identifies future work in how to automate this computationally conservative search technique so that it can provide practical feedback to the building designer during all stages of the design process.
This research aims at developing such a methodology for generating cost effective net zero energy solutions for school buildings. The Department of Energy (DOE) prototype primary school, meant to serve as the starting baseline, was modeled in the building energy simulation software eQUEST and made compliant with the requirement of ASHRAE 90.1-2007. Commonly used efficiency measures, for which credible initial cost and maintenance data were available, were selected as the parametric design set. An initial sensitivity analysis was conducted by using the Morris Method to rank the efficiency measures in terms of their importance and interaction strengths. A sequential search technique was adopted to search the solution space and identify combinations that lie near the Pareto-optimal front; this allowed various minimum cost design solutions to be identified corresponding to different energy savings levels.
Based on the results of this study, it was found that the cost optimal combination of measures over the 30 year analysis span resulted in an annual energy cost reduction of 47%, while net zero site energy conditions were achieved by the addition of a 435 kW photovoltaic generation system that covered 73% of the roof area. The simple payback period for the additional technology required to achieve NZE conditions was calculated to be 26.3 years and carried a 37.4% premium over the initial building construction cost. The study identifies future work in how to automate this computationally conservative search technique so that it can provide practical feedback to the building designer during all stages of the design process.
ContributorsIslam, Mohammad Moshfiqul (Author) / Reddy, T. Agami (Thesis advisor) / Bryan, Harvey J. (Committee member) / Addison, Marlin (Committee member) / Arizona State University (Publisher)
Created2016
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
With the increasing interest in energy efficient building design, whole building energy simulation programs are increasingly employed in the design process to help architects and engineers determine which design alternatives save energy and are cost effective. DOE-2 is one of the most popular programs used by the building energy simulation community. eQUEST is a powerful graphic user interface for the DOE-2 engine. EnergyPlus is the newest generation simulation program under development by the U.S. Department of Energy which adds new modeling features beyond the DOE-2's capability. The new modeling capabilities of EnergyPlus make it possible to model new and complex building technologies which cannot be modeled by other whole building energy simulation programs. On the other hand, EnergyPlus models, especially with a large number of zones, run much slower than those of eQUEST. Both eQUEST and EnergyPlus offer their own set of advantages and disadvantages. The choice of which building simulation program should be used might vary in each case. The purpose of this thesis is to investigate the potential of both the programs to do the whole building energy analysis and compare the results with the actual building energy performance. For this purpose the energy simulation of a fully functional building is done in eQUEST and EnergyPlus and the results were compared with utility data of the building to identify the degree of closeness with which simulation results match with the actual heat and energy flows in building. It was observed in this study that eQUEST is easy to use and quick in producing results that would especially help in the taking critical decisions during the design phase. On the other hand EnergyPlus aids in modeling complex systems, producing more accurate results, but consumes more time. The choice of simulation program might change depending on the usability and applicability of the program to our need in different phases of a building's lifecycle. Therefore, it makes sense if a common front end is designed for both these simulation programs thereby allowing the user to select either the DOE-2.2 engine or the EnergyPlus engine based upon the need in each particular case.
ContributorsRallapalli, Hema Sree (Author) / Bryan, Harvey (Thesis advisor) / Addison, Marlin (Committee member) / Reddy, Agami (Committee member) / Arizona State University (Publisher)
Created2010
Description
Due to extreme summer temperatures that regularly reach 122°F (50°C), cooling energy requirements have been responsible for 70% of peak demand and 45% of total electricity consumption in Kuwait. It is estimated that 50%-60% of electric power is consumed by the residential sector, mostly in detached villas. This study analyzes the potential impact of energy efficiency measures (EEM) and renewable energy (RE) measures on the electric energy requirements of an existing villa built in 2004. Using architectural plans, interview data, and the eQUEST building energy simulation tool, a building energy model (BEM) was developed for a villa calibrated with hourly energy use data for the year 2014. Although the modeled villa consumed less energy than an average Kuwaiti villa of the same size, 26% energy reductions were still possible under compliance with 2018 building codes. Compliance with 2010 and 2014 building codes, however, would have increased energy use by 19% and 3% respectively. Furthermore, survey data of 150 villas was used to generate statistics on rooftop solar area availability. Accordingly, it was found that 78% of the survey sample’s average total rooftop area was not suitable for rooftop solar systems due to shading and other obstacles. The integration of a solar canopy circumvents this issue and also functions as a shading device for outdoor activities and as a protective cover for AC units and water tanks. Combining the highest modeled EEMs and RE measures on the villa, the energy use intensity (EUI) would be reduced to 15 kWh/m2/year from a baseline value of 127 kWh/m2/year, close to net zero. Finally, it was determined that EEMs were able to reduce the entire demand profile whereas RE measures were most effective at reducing demand around mid-day hours. In future studies, more effort should be spent on collecting hourly data from multiple villas to assist in the development of a detailed hourly bottom-up residential energy modeling methodology.
ContributorsAlyakoob, Ali (Author) / Reddy, Agami T (Thesis advisor) / Addison, Marlin (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2020