Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data
Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data
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三次样条和傅立叶级数作为插值技术用于填补短期建筑能源使用和天气数据缺失的研究
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发表时间：
2002
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通讯作者：
D. Claridge
中科院分区：
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作者：
J. Baltazar;D. Claridge
研究了三次样条和傅立叶级数作为插值技术，用于填补能源和气象时间序列中的缺失数据。以下程序在测量数据集中创建了人为缺失点（伪间隙），并基于这些伪间隙周围数据集的局部行为。测试了三次样条技术的五种变体和傅立叶级数的12种变体，并与线性插值进行了比较，以填补20个能源使用和天气数据样本中1至6小时的数据空白。每个样本的长度至少为一年。分析表明，对于填补时间序列干球和露点温度数据中1-6小时的空白，线性插值上级样条和傅里叶级数技术。为了填补建筑物冷却和加热使用中的1-6小时的间隙，发现具有每个间隙之前和之后的24个数据点和6个常数的傅立叶级数方法是最合适的。如果没有足够的数据点来应用这种方法，建议使用简单的线性插值。Copyright © 2002 by ASME
A study of cubic splines and Fourier series as interpolation techniques for filling in missing data in energy and meteorological time series is presented. The followed procedure created artificially missing points (pseudo-gaps) in measured data sets and was based on the local behavior of the data set around those pseudo-gaps. Five variants of the cubic spline technique and 12 variants of Fourier series were tested and compared with linear interpolation, for filling in gaps of 1 to 6 hours of data in 20 samples of energy use and weather data. Each of the samples is at least one year in length. The analysis showed that linear interpolation is superior to the spline and Fourier series techniques for filling in 1–6 hour gaps in time series dry bulb and dew point temperature data. For filling 1–6 hour gaps in building cooling and heating use, the Fourier series approach with 24 data points before and after each gap and six constants was found to be the most suitable. In cases where there are insufficient data points for the application of this approach, simple linear interpolation is recommended.Copyright © 2002 by ASME