Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data

[+] Author and Article Information
Juan-Carlos Baltazar

Energy Systems Laboratory,  Texas A&M University, College Station, TX 77843-3581

David E. Claridge

Energy Systems Laboratory,  Texas A&M University, College Station, TX 77843-3581dclaridge@tamu.edu

J. Sol. Energy Eng 128(2), 226-230 (Nov 16, 2005) (5 pages) doi:10.1115/1.2189872 History: Received July 15, 2005; Revised November 16, 2005

A study of cubic splines and Fourier series as interpolation techniques for filling in missing hourly data in energy and meteorological time series data sets is presented. The procedure developed in this paper is based on the local patterns of the data around the gaps. Artificial gaps, or “pseudogaps,” created by deleting consecutive data points from the measured data sets, were filled using four variants of the cubic spline technique and 12 variants of the Fourier series technique. The accuracy of these techniques was compared to the accuracy of results obtained using linear interpolation to fill the same pseudogaps. The pseudogaps filled were 1–6 data points in length created in 18 year-long sets of hourly energy use and weather data. More than 1000 pseudogaps of each gap length were created in each of the 18 data sets and filled using each of the 17 techniques evaluated. Use of mean bias error as the selection criterion found that linear interpolation is superior to the cubic spline and Fourier series methodologies for filling gaps of dry bulb and dew point temperature time series data. For hourly building cooling and heating use data, the Fourier series approach with 24 data points before and after each gap and six terms was found to be the most suitable; where there are insufficient data points to apply this approach, simple linear interpolation is recommended.

Copyright © 2006 by American Society of Mechanical Engineers
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