A Procedure for Linking Projected Energy Performance Uncertainty with Investment Decision-Making

Alireza Bozorgi, LEED Green Associate

PhD Candidate in Design Research | MBA

College of Architecture and Urban Design | Pamplin College of Business

Virginia Tech

James R. Jones, Ph.D.

Professor of Architecture

College of Architecture and Urban Studies

Virginia Polytechnic Institute and State University

ABSTRACT

Energy consultants, design professionals, and property professionals have widely used building energy modeling tools to project energy performance when evaluating energy efficiency investment. These models help decision makers to explore the potential savings, estimate the simple payback (PB) and simple return on investment (ROI), compare the various energy efficiency systems and strategies (EESS), and select the most cost-effective options based on the results of their financial analyses. However, the primary issue with these financial outcomes is that they typically only focus on installation and operational costs when estimating the return on investment (ROI). Many benefits of energy efficiency investment, such as reduced risks and increased asset revenue, are beyond these analyses and ignoring them in the financial analysis may lead to poor investment decisions.

In this paper, primary challenges in evaluating the financial performance of energy efficiency investment are addressed. An eight-step procedure is proposed for a more holistic financial assessment of energy efficiency to incorporate all costs and benefits, in terms of revenues and risks, resulting from the EESS, while explicitly expressing uncertainties. This is an integrated procedure that explains how to build an accurate case-based model and address the uncertainty associated with the input assumptions, and then links the estimated building performance to financial performance indicators. It uses the Discounted Cash Flow approach and Monte Carlo simulation technique for calculating IRR and ROI in order to accommodate revenues, risks and uncertainties.