The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy efficiency improvements, performing building Monitoring and Verification (M&V;) and continuous commissioning (CCx) were based on them. Although robust, these methods were not sensitive enough to detect a number of common causes for increased energy use. In recent years, prevalence of short-term building energy consumption data, also known as Energy Interval Data (EID), made available through the Smart Meters, along with data mining techniques presents the potential of knowledge discovery inherent in this data. This allows more sophisticated analytical tools to be developed resulting in greater sensitivities due to higher prediction accuracies; leading to deep energy savings and highly efficient building system operations. The research explores enhancements to Inverse Statistical Modeling techniques due to the availability of EID. Inverse statistical modeling is the process of identification of prediction model structure and estimates of model parameters. The methodology is based on several common statistical and data mining techniques: cluster analysis for day typing, outlier detection and removal, and generation of building scheduling. Inverse methods are simpler to develop and require fewer inputs for model identification. They can model changes in energy consumption based on changes in climatic variables and up to a certain extent, occupancy. This makes them easy-to-use and appealing to building managers for evaluating any general retrofits, building condition monitoring, continuous commissioning and short-term load forecasting (STLF). After evaluating several model structures, an elegant model form was derived which can be used to model daily energy consumption; which can be extended to model energy consumption for any specific hour by adding corrective terms. Additionally, adding AR terms to this model makes it usable for STLF. Two different buildings, one synthetic (ASHRAE medium-office prototype) building and another, an actual office building, were modeled using these techniques. The methodologies proposed have several novel features compared to the manner in which these models have been described earlier. Finally, this thesis investigates characteristic fault signature identification from detailed simulation models and subsequent inverse analysis.

Through manipulation of adaptable opportunities available within a given environment, individuals become active participants in managing personal comfort requirements, by exercising control over their comfort without the assistance of mechanical heating and cooling systems. Similarly, continuous manipulation of a building skin's form, insulation, porosity, and transmissivity qualities exerts control over the energy exchanged between indoor and outdoor environments. This research uses four adaptive response variables in a modified software algorithm to explore an adaptive building skin's potential in reacting to environmental stimuli with the purpose of minimizing energy use without sacrificing occupant comfort. Results illustrate that significant energy savings can be realized with adaptive envelopes over static building envelopes even under extreme summer and winter climate conditions; that the magnitude of these savings are dependent on climate and orientation; and that occupant thermal comfort can be improved consistently over comfort levels achieved by optimized static building envelopes. The resulting adaptive envelope's unique climate-specific behavior could inform designers in creating an intelligent kinetic aesthetic that helps facilitate adaptability and resiliency in architecture.

Buildings in the United States, account for over 68 percent of electricity consumed, 39 percent of total energy use, and 38 percent of the carbon dioxide emissions. By the year 2035, about 75% of the U.S. building sector will be either new or renovated. The energy efficiency requirements of current building codes would have a significant impact on future energy use, hence, one of the most widely accepted solutions to slowing the growth rate of GHG emissions and then reversing it involves a stringent adoption of building energy codes. A large number of building energy codes exist and a large number of studies which state the energy savings possible through code compliance. However, most codes are difficult to comprehend and require an extensive understanding of the code, the compliance paths, all mandatory and prescriptive requirements as well as the strategy to convert the same to energy model inputs. This paper provides a simplified solution for the entire process by providing an easy to use interface for code compliance and energy simulation through a spreadsheet based tool, the ECCO or the Energy Code COmpliance Tool. This tool provides a platform for a more detailed analysis of building codes as applicable to each and every individual building in each climate zone. It also facilitates quick building energy simulation to determine energy savings achieved through code compliance. This process is highly beneficial not only for code compliance, but also for identifying parameters which can be improved for energy efficiency. Code compliance is simplified through a series of parametric runs which generates the minimally compliant baseline building and 30% beyond code building. This tool is seen as an effective solution for architects and engineers for an initial level analysis as well as for jurisdictions as a front-end diagnostic check for code compliance.  

The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the general realm of post-occupancy evaluation (POE). POE involves evaluating building performance in terms of energy-use, indoor environmental quality, acoustics and water-use; the first aspect i.e. energy-use is addressed in this thesis. Normally, a full year or more of energy-use and weather data is required to determine the actual post-occupancy energy-use of buildings. In many cases, either measured building performance data is not available or the time and cost implications may not make it feasible to invest in monitoring the building for a whole year. Knowledge about the minimum amount of measured data needed to accurately capture the behavior of the building over the entire year can be immensely beneficial. This research identifies simple modeling techniques to determine best time of the year to begin in-situ monitoring of building energy-use, and the least amount of data required for generating acceptable long-term predictions. Four analysis procedures are studied. The short-term monitoring for long-term prediction (SMLP) approach and dry-bulb temperature analysis (DBTA) approach allow determining the best time and duration of the year for in-situ monitoring to be performed based only on the ambient temperature data of the location. Multivariate change-point (MCP) modeling uses simulated/monitored data to determine best monitoring period of the year. This is also used to validate the SMLP and DBTA approaches. The hybrid inverse modeling method-1 predicts energy-use by combining a short dataset of monitored internal loads with a year of utility-bills, and hybrid inverse method-2 predicts long term building performance using utility-bills only. The results obtained show that often less than three to four months of monitored data is adequate for estimating the annual building energy use, provided that the monitoring is initiated at the right time, and the seasonal as well as daily variations are adequately captured by the short dataset. The predictive accuracy of the short data-sets is found to be strongly influenced by the closeness of the dataset's mean temperature to the annual average temperature. The analysis methods studied would be very useful for energy professionals involved in POE.

Arizona has an abundant solar resource and technologically mature systems are available to capture it, but solar energy systems are still considered to be an innovative technology. Adoption rates for solar and wind energy systems rise and fall with the political tides, and are relatively low in most rural areas in Arizona. This thesis tests the hypothesis that a consumer profile developed to characterize the adopters of renewable energy technology (RET) systems in rural Arizona is the same as the profile of other area residents who performed renovations, upgrades or additions to their homes. Residents of Santa Cruz and Cochise Counties who had obtained building permits to either install a solar or wind energy system or to perform a substantial renovation or upgrade to their home were surveyed to gather demographic, psychographic and behavioristic data. The data from 133 survey responses (76 from RET adopters and 57 from non-adopters) provided insights about their decisions regarding whether or not to adopt a RET system. The results, which are statistically significant at the 99% level of confidence, indicate that RET adopters had smaller households, were older and had higher education levels and greater income levels than the non-adopters. The research also provides answers to three related questions: First, are the energy conservation habits of RET adopters the same as those of non-adopters? Second, what were the sources of information consulted and the most important factors that motivated the decision to purchase a solar or wind energy system? And finally, are any of the factors which influenced the decision to live in a rural area in southeastern Arizona related to the decision to purchase a renewable energy system? The answers are provided, along with a series of recommendations that are designed to inform marketers and other promoters of RETs about how to utilize these results to help achieve their goals.

The modeling and simulation of airflow dynamics in buildings has many applications including indoor air quality and ventilation analysis, contaminant dispersion prediction, and the calculation of personal occupant exposure. Multi-zone airflow model software programs provide such capabilities in a manner that is practical for whole building analysis. This research addresses the need for calibration methodologies to improve the prediction accuracy of multi-zone software programs. Of particular interest is accurate modeling of airflow dynamics in response to extraordinary events, i.e. chemical and biological attacks. This research developed and explored a candidate calibration methodology which utilizes tracer gas (e.g., CO2) data. A key concept behind this research was that calibration of airflow models is a highly over-parameterized problem and that some form of model reduction is imperative. Model reduction was achieved by proposing the concept of macro-zones, i.e. groups of rooms that can be combined into one zone for the purposes of predicting or studying dynamic airflow behavior under different types of stimuli. The proposed calibration methodology consists of five steps: (i) develop a "somewhat" realistic or partially calibrated multi-zone model of a building so that the subsequent steps yield meaningful results, (ii) perform an airflow-based sensitivity analysis to determine influential system drivers, (iii) perform a tracer gas-based sensitivity analysis to identify macro-zones for model reduction, (iv) release CO2 in the building and measure tracer gas concentrations in at least one room within each macro-zone (some replication in other rooms is highly desirable) and use these measurements to further calibrate aggregate flow parameters of macro-zone flow elements so as to improve the model fit, and (v) evaluate model adequacy of the updated model based on some metric. The proposed methodology was first evaluated with a synthetic building and subsequently refined using actual measured airflows and CO2 concentrations for a real building. The airflow dynamics of the buildings analyzed were found to be dominated by the HVAC system. In such buildings, rectifying differences between measured and predicted tracer gas behavior should focus on factors impacting room air change rates first and flow parameter assumptions between zones second.