The following is a listing of data assessment techniques for water conservation programs based on manufacturer specifications and estimates, field data, and analysis. The techniques vary in their level of sophistication and likely error, from simplest to complex (based on based on “Measurement and Verification for Federal Energy Projects,” U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, 2000).
Stipulated and measured factors – Water savings are based on a combination of measured and estimated (stipulated) factors. Measurements are collected in the field from specific locations at the component or system level for a single (i.e., spot) or short-term period. A stipulation factor, or estimate, is supported by historical or manufacturer’s data (for example, estimating water savings from high efficiency toilets may involve using a combination of the number of new toilets installed multiplied by the difference in flushing volume between the old toilets (measured in the field) and the new toilets (based on manufacturers specifications) multiplied by the expected number of uses per unit time). These savings are supported by engineering calculations, or component, or system models. Costs to collect the data and perform these calculations are estimated to range from 1%-3% of project costs, depending on number of points measured.
Measured factors - Water savings are based on spot or short-term measurements taken at the component or system level when variations in factors that may create noise or interference regarding estimating water use reductions are not expected, or based on continuous measurements taken at the component or system level when variations are expected (for example, taking continuous, or daily readings of customer water use at their meter after a water audit is performed to identify savings in daily water use). These saving estimates are supported by engineering calculations, or component, or system models. Costs to collect the data and perform these calculations are estimated to range from 3%-15% of project costs, depending on number of points measured and term of metering.
Utility billing data analysis – Estimate water savings based on long-term, whole-building (or home) utility meter, facility level, or sub-meter data. These saving estimates are supported by regression analysis of utility billing meter data. It is important that for many water conservation programs that involve rebates or incentives – for both indoor and outdoor water efficiency – it is important to compare homes and/or business that participate in the program with a control population that is similar to the participants, but did not participate in the subject program. This is performed by developing databases that track customer water use with and without program participation for customers of similar type (e.g., for residential programs include control population based on age of home, home size in square feet, irrigated acres, etc.). In this way, the effect of organic changes to customer water use can be distilled from the effects of any specific program.
It is also important to note that tracking substantial amounts of customer data will require a data validation and quality control step to an ensure that the customer water use records are accurate and can be tracked to specific street addresses (for mapping analyses are an important data assessment method). Therefore, some type of customer data preprocessing software may be needed to develop sets of water use over time for appropriate customer sets. Costs to collect the data and perform these calculations are estimated to range from 5%-12% of project costs depending on complexity of the billing analysis.
Calibrated computer simulation - Computer simulation inputs may be based on several of the following: engineering estimates; spot, short-, or long-term measurements of system components; and long-term, whole-building utility meter data. These savings are supported by computer simulation model calibrated with whole-building and/or customer water use segment end-use data. Estimated range is 5%-12% depending on complexity and the number systems modeled.