The intent of this user guide is to provide a description of the functionality of the Energy Charting and Metrics plus Building Re-tuning and Measurement and Verification (ECAM+) tool. ECAM+ facilitates the charting and analysis of energy use and point-level data from utility meters, building automation systems (BASs), and data loggers. This document describes the tool’s general functions and features, including installation, use, guidance, and limitations.The Energy Charting and Metrics Tool (ECAM) is an add-on for Microsoft Excel® which was developed to facilitate analysis of data from building (energy and other data). Key features of ECAM+ include the creation of charts to help re-tuning.
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The lack of empirical data on the energy performance of buildings is a key barrier to accelerating the energy efficiency retrofit market. The DOE’s Buildings Performance Database (BPD) helps address this gap by allowing users to perform exploratory analyses on an anonymous dataset of hundreds of thousands of commercial and residential buildings. These analyses enable market actors to assess energy efficiency opportunities, forecast project performance, and quantify performance risk using empirical building data. In this paper, we describe the process of collecting and preparing data for the database, and present a peer-group analysis tool that allows users to analyze building performance for narrowly defined subsets of the database, or peer groups. We use this tool to explore a case study of a multifamily portfolio owner comparing his buildings’ performance to the peer group of multifamily buildings in the local metro area. We also present a performance comparison tool that uses statistical methods to estimate the expected change in energy performance due to changes in building-component technologies. We demonstrate a low-effort retrofit analysis, providing a probabilistic estimate of energy savings for a sample building retrofit. The key advantages of this approach compared to conventional engineering models are that it provides probabilistic risk analysis based on actual
measured data and can significantly reduce transaction costs for predicting savings across a portfolio.