Efficient longitudinal population survival survey sampling for the measurement and verification of lighting retrofit projects
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Date
Authors
Carstens, Herman
Xia, Xiaohua
Yadavalli, Venkata S. Sarma
Rajan, Arvind
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
A method is presented for reducing the required sample sizes for reporting energy savings with predetermined statistical accuracy in lighting retrofit measurement and verification projects, where the population of retrofitted luminaires is to be tracked over time. The method uses a Dynamic Generalised Linear Model with Bayesian forecasting to account for past survey sample sizes and survey results and forecast future population decay, while quantifying estimation uncertainty. A Genetic Algorithm is used to optimise multi-year sampling plans, and distributions are convolved using a new method of moments technique using the Mellin transform instead of a Monte Carlo simulation. Two cases studies are investigated: single population designs, and stratified population designs, where different kinds of lights are replaced in the same retrofit study. Results show significant cost reductions and increased statistical efficiency when using the proposed Bayesian framework.
Description
Keywords
Measurement and verification (M&V), Method of moments, Mellin transform, Sampling design, Retrofit, Population survival, Bayesian, Buildings, Algorithms, Energy savings, Multivariate data analysis, Standard uncertainty evaluation
Sustainable Development Goals
Citation
Carstens, H., Xia, X.H., Yadavalli, S. & Rajan, A. 2017, 'Efficient longitudinal population survival survey sampling for the measurement and verification of lighting retrofit projects', Energy and Buildings, vol. 150, pp. 163-176.