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Financial Forecasting Calculation Methodology

The Buildings Performance Database uses a Financial Forecasting Tool, or Financial Tool, to help financial institutions, building owners, and others make informed energy efficiency investment decisions. Specifically, the Tool transforms energy savings estimates from pre- and post-retrofit buildings into financial and risk metrics that are commonly used in investment decisions.

The beta-version Financial Tool offers users the following building and financial input options:

• Building characteristics: Location (climate zone, state, city or ZIP code), residential or commercial sector, building type, size, and age.
• Energy efficiency measures: Broad categories include space heating, space cooling, ventilation, windows, insulation, lighting, retro-commissioning, sensors and controls, plus the option to specify more detail on pre- and post-retrofit conditions.
• Capital cost of energy efficiency measures: Users have the option to input estimates or allow the tool to provide low, mean, and high cost estimates in dollars per square feet ($/sq.ft.). Triangular distribution is assumed to quantify capital cost uncertainty.
• Energy prices and uncertainties: Users choose to define low, mean, and high prices by year and month or select the Energy Information Administration's regional electricity and natural gas price projections. Triangular distribution or Geometric Brownian Motion algorithms are used to quantify energy price uncertainty.
• Investment parameters: Investment amount, time period, investment year and month, construction duration, and discount rate.

Methodology

The Database queries energy usage data of buildings with similar characteristics to those selected by the user. Monthly energy savings distributions are derived from a difference calculated between those buildings with and without the selected energy efficiency measures implemented. Electricity savings are reported in kilowatt hours per square feet (kWh/sq.ft.) and natural gas savings in thousands of British thermal units per square feet (thousands of Btu/sq.ft.) Typically, one year of savings distributions will be returned. The beta-version Financial Tool assumes these savings persist through the time horizon of the investment.

The Financial Tool then applies Monte Carlo methods to derive stochastic cash flows (monthly discounted cash flow distributions) for the energy efficiency investment through the time horizon. Elements of the monthly cash flows include:

• Investment: A constant inputted by the user and assumed to be expensed in equal fractions over the construction period; no uncertainty considered.
• Electricity cost savings distribution: Monthly electricity savings in kilowatt hours per square feet (kWh/sq.ft.) sampled from the Database derived non-standard distribution multiplied by electricity price ($/kWh) for given year and month sampled from the probability distribution (no uncertainty, triangular distribution, or Geometric Brownian Motion algorithm) multiplied by stochastically determined floor area in square feet of retrofitted space that factors in the capital cost uncertainty (triangular distribution).
• Natural gas cost savings distribution: Monthly natural gas savings (thousands of Btu/sq.ft.) sampled from the Database derived non-standard distribution multiplied by natural gas price dollars per thousand British thermal units ($/thousands of Btu) for given year and month sampled from probability distribution (no uncertainty, triangular distribution, or Geometric Brownian Motion algorithm) multiplied by stochastically determined floor area (square feet) of retrofitted space that factors in the capital cost uncertainty (triangular distribution).
• Financial incentives (if selected, otherwise it defaults to 0): Stochastically determined distribution of cash values that factors in capital cost uncertainty (triangular distribution) and is assumed to be credited one month after construction end.

The monthly cash flow elements are discounted with the divisor:

Discfactor_t = ((1+AnnualR/100)^(1/p))^t

Where p = 12, t = time in months, and AnnualR = user input annual discount rate (%)

Results are displayed in a dashboard view and can be exported to Microsoft Excel:

• Cumulative discounted cash flow through the time horizon
• Annual energy cost savings probability distributions through the time horizon
• Bar graph of first year monthly cash flows showing breakdown of costs and savings
• Summary table of results including cumulative value at confidence levels of 5, 25, 50, 75, and 95%, conditional value at risk at 5%, payback years, and internal rate of return

The user then has the guided choice to select a different set of energy efficiency measures or building characteristics for analysis and re-run the calculations. Inputs and results from all user test cases are stored and accessible.