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Restoration of Short Periods of Missing Energy Use and Weather Data Using Cubic Spline and Fourier Series Approaches: Qualitative Analysis
Abstract
The paper presents seventeen approaches that use
cubic splines and Fourier series for restoring short
term missing data in time series of building energy
use and weather data. The study is based on twenty
samples of hourly data, each at least one year long. In
order to differentiate the approaches, two
comparisons were carried out. The first comparison
was made between the estimated and actual values, as
time series and as cross check plots. The second
comparison is based on the fraction of the total data
that can be estimated by an approach within specific
ranges or error. Thus for the cooling and heating data,
the fraction of the data set estimated within 1%, 5%,
and 10% of the measured values was determined. For
the dew point and the dry-bulb temperature samples,
the performance is based on the amount of data that
are within 1, 3, 5 and 10 °F from the actual data.
From the results of this analysis, it appears that linear
interpolation is a better approach for filling gaps one
to three hours long. The cubic splines approach gave
better performance for gaps between four and six.
Citation
Baltazar, J. C.; Claridge, D. E. (2002). Restoration of Short Periods of Missing Energy Use and Weather Data Using Cubic Spline and Fourier Series Approaches: Qualitative Analysis. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from https : / /hdl .handle .net /1969 .1 /4575.