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Description
According to the U.S. Energy Information Administration, commercial buildings represent about 40% of the United State's energy consumption of which office buildings consume a major portion. Gauging the extent to which an individual building consumes energy in excess of its peers is the first step in initiating energy efficiency improvement.

According to the U.S. Energy Information Administration, commercial buildings represent about 40% of the United State's energy consumption of which office buildings consume a major portion. Gauging the extent to which an individual building consumes energy in excess of its peers is the first step in initiating energy efficiency improvement. Energy Benchmarking offers initial building energy performance assessment without rigorous evaluation. Energy benchmarking tools based on the Commercial Buildings Energy Consumption Survey (CBECS) database are investigated in this thesis. This study proposes a new benchmarking methodology based on decision trees, where a relationship between the energy use intensities (EUI) and building parameters (continuous and categorical) is developed for different building types. This methodology was applied to medium office and school building types contained in the CBECS database. The Random Forest technique was used to find the most influential parameters that impact building energy use intensities. Subsequently, correlations which were significant were identified between EUIs and CBECS variables. Other than floor area, some of the important variables were number of workers, location, number of PCs and main cooling equipment. The coefficient of variation was used to evaluate the effectiveness of the new model. The customization technique proposed in this thesis was compared with another benchmarking model that is widely used by building owners and designers namely, the ENERGY STAR's Portfolio Manager. This tool relies on the standard Linear Regression methods which is only able to handle continuous variables. The model proposed uses data mining technique and was found to perform slightly better than the Portfolio Manager. The broader impacts of the new benchmarking methodology proposed is that it allows for identifying important categorical variables, and then incorporating them in a local, as against a global, model framework for EUI pertinent to the building type. The ability to identify and rank the important variables is of great importance in practical implementation of the benchmarking tools which rely on query-based building and HVAC variable filters specified by the user.
ContributorsKaskhedikar, Apoorva Prakash (Author) / Reddy, T. Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
A major problem faced by electric utilities is the need to meet electric loads during certain times of peak demand. One of the widely adopted and promising programs is demand response (DR) where building owners are encouraged, by way of financial incentives, to reduce their electric loads during a few

A major problem faced by electric utilities is the need to meet electric loads during certain times of peak demand. One of the widely adopted and promising programs is demand response (DR) where building owners are encouraged, by way of financial incentives, to reduce their electric loads during a few hours of the day when the electric utility is likely to encounter peak loads. In this thesis, we investigate the effect of various DR measures and their resulting indoor occupant comfort implications, on two prototype commercial buildings in the hot and dry climate of Phoenix, AZ. The focus of this study is commercial buildings during peak hours and peak days. Two types of office buildings are modeled using a detailed building energy simulation program (EnergyPlus V6.0.0): medium size office building (53,600 sq. ft.) and large size office building (498,600 sq. ft.). The two prototype buildings selected are those advocated by the Department of Energy and adopted by ASHRAE in the framework of ongoing work on ASHRAE standard 90.1 which reflect 80% of the commercial buildings in the US. After due diligence, the peak time window is selected to be 12:00-18:00 PM (6 hour window). The days when utility companies require demand reduction mostly fall during hot summer days. Therefore, two days, the summer high-peak (15th July) and the mid-peak (29th June) days are selected to perform our investigations. The impact of building thermal mass as well as several other measures such as reducing lighting levels, increasing thermostat set points, adjusting supply air temperature, resetting chilled water temperature are studied using the EnergyPlus building energy simulation program. Subsequently the simulation results are summarized in tabular form so as to provide practical guidance and recommendations of which DR measures are appropriate for different levels of DR reductions and the associated percentage values of people dissatisfied (PPD). This type of tabular recommendations is of direct usefulness to the building owners and operators contemplating DR response. The methodology can be extended to other building types and climates as needed.
ContributorsKhanolkar, Amruta (Author) / Reddy, T Agami (Thesis advisor) / Addison, Marlin (Committee member) / Bryan, Harvey (Committee member) / Arizona State University (Publisher)
Created2012
Description
The building sector is one of the main energy consumers within the USA. Energy demand by this sector continues to increase because new buildings are being constructed faster than older ones are retired. Increase in energy demand, in addition to a number of other factors such as the finite nature

The building sector is one of the main energy consumers within the USA. Energy demand by this sector continues to increase because new buildings are being constructed faster than older ones are retired. Increase in energy demand, in addition to a number of other factors such as the finite nature of fossil fuels, population growth, building impact on global climate change, and energy insecurity and independence has led to the increase in awareness towards conservation through the design of energy efficient buildings. Net Zero Energy Building (NZEB), a highly efficient building that produces as much renewable energy as it consumes annually, provides an effective solution to this global concern. The intent of this thesis is to investigate the relationship of an important factor that has a direct impact on NZEB: Floor / Area Ratio (FAR). Investigating this relationship will help to answer a very important question in establishing NZEB in hot-arid climates such as Phoenix, Arizona. The question this thesis presents is: “How big can a building be and still be Net Zero?” When does this concept start to flip and buildings become unable to generate the required renewable energy to achieve energy balance? The investigation process starts with the analysis of a local NZEB, DPR Construction Office, to evaluate the potential increase in building footprint and FAR with respect to the current annual Energy Use Intensity (EUI). Through the detailed analysis of the local NZEB, in addition to the knowledge gained through research, this thesis will offer an FAR calculator tool that can be used by design teams to help assess the net zero potential of their project. The tool analyzes a number of elements within the project such as total building footprint, available surface area for photovoltaic (PV) installation, outdoor circulation and landscape area, parking area and potential parking spots, potential building area in regards to FAR, number of floors based on the building footprint, FAR, required area for photovoltaic installation, photovoltaic system size, and annual energy production, in addition to the maximum potential FAR their project can reach and still be Net Zero.
ContributorsBen Salamah, Fahad (Author) / Bryan, Harvey (Thesis advisor) / Reddy, T. Agami (Committee member) / Ramalingam, Muthukumar (Committee member) / Arizona State University (Publisher)
Created2016