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Procedures for Filling Short Gaps in Energy Use and Weather Data
Abstract
Filling short gaps (a few hours) in hourly
energy use and weather data can be useful for (i)
retrofit savings analysis and calculation, and for (ii)
diagnostic purposes. The paper evaluates four
methods for rehabilitating short periods of missing
data. Single variable regression, polynomial models,
Lagrange interpolation, and linear interpolation
models are developed, demonstrated, and used to fill
1-6 hour gaps in weather data, heating data and
cooling data for commercial buildings. The
methodology for comparing the performance of the
four different methods for filling data gaps uses 11
one-year data sets to develop different models and
fill over 50,000 "pseudo-gaps" which are created by
assuming data is missing and then comparing the
"filled" values with the measured values.
Comparisons are made using six statistical
parameters including mean bias error, coefficient of
determination, and coefficient of variation of the
root-mean-square-error.
For filling 1-6 missing hours of cooling data,
heating data or weather data, a linear interpolation
model or a polynomial model with hour-of-day
(HOD) as the independent variable both provide a
mean bias error of less than 0.087 % (0.005 F). The
Lagrange model exhibits mean bias errors of 0.175 %
(-0.010 F) which is better than the SVR model with
temperature as the independent variable, which
exhibits mean bias errors up to 0.909 % (0.062 F).
Based on these findings, the polynomial model
with hour-of-day as the independent variable and the
linear interpolation model are recommended for
filling data gaps of six hours or less in cooling,
heating and weather data.
Citation
Chen, H.; Claridge, D. E. (2000). Procedures for Filling Short Gaps in Energy Use and Weather Data. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from https : / /hdl .handle .net /1969 .1 /6808.