abstract: 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. / Dissertation/Thesis / M.S. Built Environment 2013

Energy benchmarking and disclosure policies exist in several local and state governments to manage the energy consumption of existing buildings and encourage energy efficient retrofits and upgrades, yet little is known about whether these efforts have improved overall energy efficiency. The purpose of this repeated-measures study was to examine the influence of New York City's (NYC's) Benchmarking Law (LL84) on the energy performance of the city's existing commercial buildings through investigating whether the energy performance of the city's existing commercial buildings significantly improved after the implementation of this policy. The study was based on Ostrom's institutional analysis and development framework. Paired-sample t tests were performed to statistically analyze the annually disclosed energy benchmarking data for 1,072 of NYC's existing commercial buildings that were benchmarked in both 2011 and 2016. Compared to 2011, the study results revealed statistically significant improvements in the energy performance of NYC's commercial buildings by 2016. On average, their site energy use intensity (EUI) significantly reduced by 5%, source EUI significantly decreased by 10%, greenhouse gas emissions significantly dropped by 12%, and ENERGY STAR performance rating significantly improved by 5%. However, these improvements were primarily achieved in 2012, 1 year after the city's energy benchmarking data were publicly disclosed. Additional measures should be considered to maintain continuous energy savings and greenhouse gas mitigation patterns. Positive social change implications include the potential to promote energy-efficient upgrades and inspire the adoption of sustainable building concepts.

Energy efficiency is currently a hot topic on all regional, national, and global stages. Accurate measurements on how energy is being used over a period of time can improve performance of the drinking water utility substantially and reduce energy consumption. Nevertheless, the drinking water industry does not have a specific benchmarking practice to evaluate its energy performance of the system. Therefore, there are no standards to compare energy use between water utilities that have a variety of system characteristics. The goal of this research is to develop quantitative approach to select energy benchmarking parameters of the water system, so the drinking water utilities can use those parameters to improve their energy efficiency. In addition to a typical benchmarking of drinking water utilities, the energy benchmarking can specifically compare energy efficiency of a utility with other utilities nationwide. The research developed a regression model based on the statistical representation of the energy use and descriptive characteristics of the drinking water utilities data throughout the U.S. Methodologies to eliminate singularity and multicollinearity from collinear survey dataset are discussed. The all possible regressions were chosen as parameters selection methodology to identify a subset of most significant parameters, i.e. system characteristics, that can mathematically correspond to energy use across different utilities. As a result, the energy benchmarking would be able to calculate the predicted total energy use of the system from given system characteristics. / Master of Science

As mobile devices are nowadays used regularly and everywhere, their energy consumption has become a central concern for their users. However, mobile applications often do not consider energy requirements and users have to install and try them to reveal information on their energy behavior. In this paper, we compare mobile applications from two domains and show that applications reveal different energy consumption while providing similar services. We define microbenchmarks for emailing and web browsing and evaluate applications from these domains. We show that non-functional features such as web page caching can but not have to have a positive influence on applications' energy consumption.

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"Numerous higher education (HE) institutions in the United States (US) have created sustainability agendas, including construction of sustainable buildings. More than 200 US HE institutions, have at least one Leadership in Energy and Environmental Design (LEED) certified building on their campus (Princeton Review 2012). With the growing student population and need to house them, residence hall construction is rising nationwide. A profile of newly constructed building types shows residence halls hold the largest median area (Princeton Review 2012). In an effort to assess if sustainable residence halls are performing sustainably, a series of post occupancy evaluation (POE) indicators were selected. POE indicators were chosen through a review of widely adopted sustainability rating systems, scientific literature and student occupant feedback. The selected indicators address a range of parameters including: water and energy consumption, occupant thermal comfort, occupant consumption behavior and education, noise insulation (indoor and outdoor), and Facilities Management (FM) operational feedback. Furthermore, specific indicators such as building energy management systems (BEMS), building automation control systems (BACS) and artificial intelligence (AI) agents were examined. The proposed POE indicator framework data was collected from various key stakeholders including: designers, HE FM departments, residential life personnel, and student occupants. The dataset includes: actual temperature (T) and relative humidity (RH) measurements of a LEED-Gold residence hall, actual water (9 residence halls) and energy consumption (4 residence halls) data, and feedback from designers, HE FM departments and 593 student occupants (LEED and non-LEED residence halls). The proposed POE indicator framework triangulates quantitative and qualitative data, via investigative and diagnostic techniques; creating a comprehensive building performance picture, vis-à-vis technical and non-technical parameters."

As mobile devices are nowadays used regularly and everywhere, their energy consumption has become a central concern for their users. However, mobile applications often do not consider energy requirements and users have to install and try them to reveal information on their energy behavior. In this paper, we compare mobile applications from two domains and show that applications reveal different energy consumption while providing similar services. We define microbenchmarks for emailing and web browsing and evaluate applications from these domains. We show that non-functional features such as web page caching can but not have to have a positive influence on applications' energy consumption.

Increasing concern about global climate change has led to a growing interest in energy usage and water consumption. It is well known that changes in consumption habits lead to more efficient use of energy and water sources. Nowadays, globalization, environmental concerns, and the shortage of resources have led to an increase of stakeholder pressure on companies to expand their focus to sustainability. Also, the high impact that the savings can have in the financial status of the company. It is encouraging the headboards to study and improve the ways water and energy are being used within the processes. Significant economic savings and benefits for the environment could be achieved with slight changes in the company. As an overview, this project starts with the extraction of data from a platform for energy management in an industrial company. Then, it goes through the understanding of the energy and water usage data set. Later, a methodology to handle and process the data will be set. It is intending to extract relevant information using clustering. The idea is to compare the usage profiles between different factories, using key performance indicators and reducing the initial data set. Once the benchmarking is performed, some critical parameters will be selected to support the decision-making process related to investments to reduce the energy usage and water consumption in a specific location. Finally, the case of study will be implemented with the measurements from Alfa Laval. We will study how, from daily measurements with a very low investment and using the proper algorithms and methodologies, the main behaviours and features in an industrial location can be extracted from the utilization data. These characteristics can be used to develop strategies or productions schemes based on the interests of the energy manager and the company.