Improving energy benchmarking with self-reported data

Abstract

Energy benchmarking for buildings has become increasingly important in government policy and industry practice for energy efficiency. The questions of how energy benchmarking is currently conducted, and how it might be improved using rapidly growing quantities of self-reported data, are examined. A case study of commercial office buildings in New York City demonstrates how the rapid growth in self-reported data presents both new opportunities and challenges for energy benchmarking for buildings. A critique is presented for the scoring methodology and data sources for Energy Star, one of the largest and most successful benchmarking certification schemes. Findings from recent studies are examined to illustrate how this certification currently works in the marketplace. Self-reported building energy data are rapidly growing in Portfolio Manager (the user interface to Energy Star) due to mandatory energy benchmarking laws, and can be used to improve Energy Star's current scoring methods. These self-reported data are tested and improved for analysis by applying theories and methods of data quality developed in computer science, statistics and data management. These new data constitute a critical building block for the development of energy efficiency policies, and will affect how government, consultants, and owners measure and compare building energy use.