Building Energy Information Systems: State of the Technology and User Case Studies

Publication Type

Report

Date Published

11/2009

LBNL Report Number

LBNL-2899E

Abstract

The focus of this study is energy information systems (EIS), broadly defined as performance monitoring software, data acquisition hardware, and communication systems used to store, analyze and display building energy data. At a minimum an EIS provides hourly whole-building electric data that is web-accessible, with analytical and graphical capabilities [Motegi 2003a]. Time series data from meters, sensors, and external data streams are used to perform analyses such as baselining, benchmarking, building level anomaly detection, and energy performance tracking.

EIS are viewed as a promising technology for a number of reasons. There is widespread recognition that there is often a large gap between building energy performance as designed, and measured post-occupancy energy consumption, and a growing body of evidence indicates the value of permanent metering and monitoring [Brown 2006; Mills 2005, 2009; Piette 2001b]. EIS are also well aligned with current trends toward benchmarking and performance reporting requirements, as in recent federal and state mandates.

Dozens of EIS are commercially available, yet public domain information is often vague and demonstration software may not be available. In addition, a lack of common terminology across vendors, and a significant degree of salesmanship makes it difficult to discern exactly what functionality the tools offer, what the hardware requirements are, or what makes one product more effective than another. This study was designed to extend and update and earlier report [Motegi 2003a], and is guided by three high-level objectives:

  1. To define a characterization framework of EIS features that provides a common terminology and can be used to understand what EIS are, and what they do.
  2. To apply the framework to EIS products to understand the state of the technology, distinguishing capabilities, and leading-edge functionality.
  3. To conduct case studies to begin to understand the interplay between common features, diagnostics, and energy saving actions.

Year of Publication

2009

Institution

Lawrence Berkeley National Laboratory

City

Berkeley