15Building energy data has been used for decades to understand energy flows in 16 buildings and plan for future energy demand. Recent market, technology and policy 17 drivers have resulted in widespread data collection by stakeholders across the 18 buildings industry. Consolidation of independently collected and maintained 19 datasets presents a cost-effective opportunity to build a database of unprecedented 20 size. Applications of the data include peer group analysis to evaluate building 21 performance, and data-driven algorithms that use empirical data to estimate energy 22 savings associated with building retrofits. This paper discusses technical 23considerations in compiling such a database using the DOE Buildings Performance 24 Database (BPD) as a case study. We gathered data on over 700,000 residential and 25 commercial buildings. We describe the process and challenges of mapping and 26 cleansing data from disparate sources. We analyze the distributions of buildings in 27 the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) 28 and Residential Energy Consumption Survey (RECS), evaluating peer groups of 29 buildings that are well or poorly represented, and discussing how differences in the 30 distributions of the three datasets impact use-cases of the data. Finally, we discuss 31 the usefulness and limitations of the current dataset and the outlook for increasing 32 its size and applications. 33

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Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the "baseline" energy use).Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions.However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

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.