A semiparametric spatio-temporal model for solar irradiance data

被引:11
|
作者
Patrick, Joshua D. [1 ]
Harvill, Jane L. [2 ]
Hansen, Clifford W. [3 ]
机构
[1] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA
[2] Baylor Univ, Dept Stat Sci, Waco, TX 76798 USA
[3] Sandia Natl Labs, Albuquerque, NM 87185 USA
基金
美国国家科学基金会;
关键词
Irradiance; Spatio-temporal model; Nonseparability; Lattice data; Semiparametric time series; COEFFICIENT AUTOREGRESSIVE MODELS; PV POWER-PLANTS; TIME-SERIES; VARIABILITY MODEL; RADIATION; FORECASTS; NETWORK;
D O I
10.1016/j.renene.2015.10.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. Our results indicate a promising approach for modeling irradiance at high spatial resolution consistent with available ground-based measurements. Such modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:15 / 30
页数:16
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