The Buildings Performance Database (BPD) includes empirically measured data from a variety of data sources with varying degrees of data quality and data availability. The purpose of the data preparation process is to maintain data quality within the database and to ensure that all database entries have sufficient data for meaningful analysis and for the database API. Data preparation is a systematic process of mapping data into the Building Energy Data Exchange Specification (BEDES), cleansing data using a set of criteria and rules of thumb, and deriving values such as energy totals and dominant asset types. The data preparation process takes the most amount of effort and time therefore most of the cleansing process has been automated. The process also needs to adapt as more data is contributed to the BPD and as building technologies over time. The data preparation process is an essential step between data contributed by providers and data published to the public inthe BPD.

Many US cities are addressing climate change by setting goals to reduce their greenhouse gas (GHG) emissions by a specified amount within a specified time period. In order to achieve these goals, reducing emissions from existing buildings is crucial. Many cities are passing legislation to target existing buildings through benchmarking, auditing, retuning, or energy or emissions performance standards. As cities design legislation, they must consider the timing of the policies, how to prioritize building types and sizes, and how these design decisions will impact the city’s emissions. This paper addresses these questions for one particular US city: Seattle, Washington. A model of Seattle’s building stock was created with benchmarking and tax assessor data. It was then used to predict GHG emissions reductions due to different policy implementations for existing commercial and multifamily buildings. Key findings are: (1) the proposed emissions policy is expected to reduce cumulative emissions from buildings by 19% between 2020 and 2050; (2) delaying the implementation of the policy by five years could limit savings to 12%; and (3) including smaller buildings in the policy could increase savings to 34%. The lessons learned and how this can be used by other cities are discussed.

This dissertation explores the problem of making building design decisions when facing complex systems with interactive effects and unreliable or inadequate information. I approach the understanding of decision making from a probabilistic point of view, with an emphasis on utilizing increasingly-available measured data. In particular, I encourage the characterization of uncertainty in predictions of building behavior and the use of the estimates to understand and weigh risks.

Intelligent design and operation of building systems can significantly reduce operating costs, mitigate environmental impacts, and minimize occupant health effects. To do so, building systems must be understood from a probabilistic point of view, i.e., the relationship between system design and the likelihood of improving building performance must be characterized. The availability of measured data on building systems and performance has grown in recent years, and is likely to continue growing. These data provide an opportunity to understand design trade-offs, but data are often noisy and incomplete. Realizing their full utility requires statistical models that can quantify uncertainty. Probabilistic methods are under-utilized in the field of building systems; analytical and theoretical models are often used instead. Thus, this dissertation focuses on understanding building systems by utilizing probabilistic techniques informed by measured data.

In Chapter 2, I present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. I develop an algorithm to design sampling architectures which maximize the probability of detecting a release, and which minimize the time to detection. Using a model of a real, large, commercial building, I demonstrate the approach by optimizing networks against uncertain release and sampler characteristics. Finally, I speculate on rules of thumb for general sampler placement.

In Chapter 3, I present methods for quantifying uncertainty in predictions of baseline building energy use. I show that uncertainty estimation improves measurement and verification (M

amp;V) information and overcomes some of the difficulties with deciding how much data is needed to confirm energy savings. I show that cross-validation is an effective method for computing uncertainty, and extend a regression-based method of predicting energy use using short-interval meter data. I demonstrate the methods by predicting energy use in 17 real commercial buildings. I discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

In Chapter 4, I demonstrate an approach to estimating energy savings due to implementing building equipment retrofits. I show that building data and statistical algorithms can provide savings estimates when detailed energy audits or simulations are not cost- or time-feasible. I develop a multivariate linear regression model to quantify the contribution of building characteristics and systems to energy use, and use it to infer the expected savings when modifying particular equipment. I apply the model to a large collection of building data. I discuss the ways understanding the risk associated with retrofit investments can inform decision making.

The scientific contribution of this dissertation is a new probabilistic approach to designing and operating building systems. I provided a clearer understanding of the risks associated with their design and operation. I present methods for utilizing noisy and incomplete data to design systems that are robust with respect to the uncertain conditions in which they must operate.

Retrofitting building systems is known to provide cost-effective energy savings. However, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.

Catalyzed by recent market, technology, and policy trends, energy data collection in the building industry is becoming more widespread. This wealth of information allows more data-driven decision-making by designers, commissioning agents, facilities staff, and energy service providers during the course of building design, operation and retrofit. The U.S. Department of Energy’s Building Performance Database (BPD) has taken advantage of this wealth of building asset- and energy-related data by collecting, cleansing, and standardizing data from across the U.S. on over 870,00 buildings, and is designed to support building benchmarking, energy efficiency project design, and buildings-related policy development with real-world data. This article explores the promises and perils energy professionals are faced with when leveraging such tools, presenting example analyses for commercial and residential buildings, highlighting potential issues, and discussing solutions and best practices that will enable designers, operators and commissioning agents to make the most of ‘big data’ resources such as the BPD.

Utility incentive programs remain one of the most significant means of deploying commercialized, but underutilized building technologies to scale. However, these programs have been largely limited to component-based products (e.g., lamps, RTUs). While some utilities do provide ‘custom’ incentive programs with whole building and system level technical assistance, these programs require deeper levels of analysis, resulting in higher program costs. This results in custom programs being restricted to utilities with greater resources, and are typically applied mainly to large or energy-intensive facilities, leaving much of the market without cost effective access and incentives for these solutions. In addition, with increasingly stringent energy codes, cost effective component-based solutions that achieve significant savings are dwindling. Building systems (e.g., integrated façade, HVAC and/or lighting solutions) can deliver higher savings that translate into large sector-wide savings if deployed at the scale of these programs. However, systems application poses a number of challenges – baseline energy use must be defined and measured; the metrics for energy and performance must be defined and tested against; in addition, system savings must be validated under well understood conditions. This paper presents a sample of findings of a project to develop validated utility incentive program packages for three specific integrated building systems, in collaboration with Xcel Energy (CO, MN), ComEd, and a consortium of California Public Owned Utilities (CA POUs) (Northern California Power Agency(NCPA) and the Southern California Public Power Authority(SCPPA)). These program packages consist of system specifications, system performance, M&V protocols, streamlined assessment methods, market assessment and implementation guidance.

This report is a part of the California Energy Commission (Energy Commission) EPIC study Leading in Los Angeles: Demonstrating scalable emerging energy efficient technologies for integrated façade, lighting, and HVAC. The set of technologies are called the Integrated Technologies for Energy-efficient Retrofits (INTER) and are comprised of automated shading products and LED lighting systems with networked luminaire-level sensors and controls. In addition, the project will include control modifications and assessments of HVAC savings. This document includes the test plan and test results for the FLEXLAB testing of the shading and lighting INTER system and related energy use impact. The test plan describes the test objectives and features, test cases, schedule, and measurements. The test results cover system performance in the lab; including lighting and HVAC energy, visual comfort, and thermal comfort.