Matching Items (14)
Description
The objective of this thesis is to investigate the various types of energy end-uses to be expected in future high efficiency single family residences. For this purpose, this study has analyzed monitored data from 14 houses in the 2013 Solar Decathlon competition, and segregates the energy consumption patterns in various residential end-uses (such as lights, refrigerators, washing machines, ...). The analysis was not straight-forward since these homes were operated according to schedules previously determined by the contest rules. The analysis approach allowed the isolation of the comfort energy use by the Heating, Venting and Cooling (HVAC) systems. HVAC are the biggest contributors to energy consumption during operation of a building, and therefore are a prime concern for energy performance during the building design and the operation. Both steady state and dynamic models of comfort energy use which take into account variations in indoor and outdoor temperatures, solar radiation and thermal mass of the building were explicitly considered. Steady State Inverse Models are frequently used for thermal analysis to evaluate HVAC energy performance. These are fast, accurate, offer great flexibility for mathematical modifications and can be applied to a variety of buildings. The results are presented as a horizontal study that compares energy consumption across homes to arrive at a generic rather than unique model - to be used in future discussions in the context of ultra efficient homes. It is suggested that similar analyses of the energy-use data that compare the performance of variety of ultra efficient technologies be conducted to provide more accurate indications of the consumption by end use for future single family residences. These can be used alongside the Residential Energy Consumption Survey (RECS) and the Leading Indicator for Remodeling Activity (LIRA) indices to assist in planning and policy making related to residential energy sector.
ContributorsGarkhail, Rahul (Author) / Reddy, T Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Addison, Marlin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Improving the conditions of schools in many parts of the world is gradually acquiring importance. The Green School movement is an integral part of this effort since it aims at improving indoor environmental conditions. This would in turn, enhance student- learning while minimizing adverse environmental impact through energy efficiency of comfort-related HVAC and lighting systems. This research, which is a part of a larger research project, aims at evaluating different school building designs in Albania in terms of energy use and indoor thermal comfort, and identify energy efficient options of existing schools. We start by identifying three different climate zones in Albania; Coastal (Durres), Hill/Pre-mountainous (Tirana), mountainous (Korca). Next, two prototypical school building designs are identified from the existing stock. Numerous scenarios are then identified for analysis which consists of combinations of climate zone, building type, building orientation, building upgrade levels, presence of renewable energy systems (solar photovoltaic and solar water heater). The existing building layouts, initially outlined in CAD software and then imported into a detailed building energy software program (eQuest) to perform annual simulations for all scenarios. The research also predicted indoor thermal comfort conditions of the various scenarios on the premise that windows could be opened to provide natural ventilation cooling when appropriate. This study also estimated the energy generated from solar photovoltaic systems and solar water heater systems when placed on the available roof area to determine the extent to which they are able to meet the required electric loads (plug and lights) and building heating loads respectively.
The results showed that there is adequate indoor comfort without the need for mechanical cooling for the three climate zones, and that only heating is needed during the winter months.
The results showed that there is adequate indoor comfort without the need for mechanical cooling for the three climate zones, and that only heating is needed during the winter months.
ContributorsDalvi, Ambalika Rajendra (Author) / Reddy, Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Addison, Marlin (Committee member) / Arizona State University (Publisher)
Created2015
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
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