Uncertainty estimation improves energy measurement and verification procedures

Abstract

Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the "baseline" energy use).Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions.However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

Utilising the Cross Industry Standard Process for Data Mining to Reduce Uncertainty in the Measurement and Verification of Energy Savings

“…Walter et al stated that a limitation of methods used for predicting baseline energy is the inadequate quantification of uncertainty in baseline energy consumption predictions. The importance of uncertainty estimation is highlighted as being essential for weighing the risks of investing in ECM [11]. Reviews of the possible modelling techniques have been carried out.…”

Low-cost energy meter calibration method for measurement and verification

“…We will also use the Bayesian approach below. Although Bayesian approaches can be applied to certain mismeasurement problems [35] and are becoming popular in M&V [48][49][50] and metrology generally [51][52][53][54], the way in which we apply it may also be novel. A second reason that imprecise reference instruments may be used for the problem under investigation is that measurement uncertainty for M&V is often dominated by other forms of uncertainty such as sampling, as mentioned before.…”

Bayesian Energy Measurement and Verification Analysis

“…In practice, a two-parameter linear regression model seldom captures the different states of a facility's energy use, for example, heating at low temperatures, a comfortable range, and cooling at high temperatures. Piecewise linear regression techniques are often used [48][49][50][51][52], and they tend to work reasonably well if their assumptions are satisfied, but they are not stable in all cases, are approximate, and the assumptions are often restrictive. Shonder and Im [17] provide a Bayesian alternative.…”