A semiparametric spatio-temporal model for solar irradiance data
Abstract
Here, 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. These results indicate a promising approach for modeling irradiance at high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.
- Authors:
-
[1];
- Univ. of California, Davis, CA (United States)
- Baylor Univ., Waco, TX (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1115287
- Alternate Identifier(s):
- OSTI ID: 1396757
- Report Number(s):
- SAND-2013-9073J
Journal ID: ISSN 0960-1481; PII: S0960148115303542
- Grant/Contract Number:
- AC04-94AL85000; 1303122
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Renewable Energy
- Additional Journal Information:
- Journal Volume: 87; Journal Issue: P1; Journal ID: ISSN 0960-1481
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; irradiance; spatio-temporal model; nonseparability; lattice data; semiparametric time series
Citation Formats
Patrick, Joshua D., Harvill, Jane L., and Hansen, Clifford W. A semiparametric spatio-temporal model for solar irradiance data. United States: N. p., 2016.
Web. doi:10.1016/j.renene.2015.10.001.
Patrick, Joshua D., Harvill, Jane L., & Hansen, Clifford W. A semiparametric spatio-temporal model for solar irradiance data. United States. https://doi.org/10.1016/j.renene.2015.10.001
Patrick, Joshua D., Harvill, Jane L., and Hansen, Clifford W. Tue .
"A semiparametric spatio-temporal model for solar irradiance data". United States. https://doi.org/10.1016/j.renene.2015.10.001. https://www.osti.gov/servlets/purl/1115287.
@article{osti_1115287,
title = {A semiparametric spatio-temporal model for solar irradiance data},
author = {Patrick, Joshua D. and Harvill, Jane L. and Hansen, Clifford W.},
abstractNote = {Here, 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. These results indicate a promising approach for modeling irradiance at high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.},
doi = {10.1016/j.renene.2015.10.001},
journal = {Renewable Energy},
number = P1,
volume = 87,
place = {United States},
year = {Tue Mar 01 00:00:00 EST 2016},
month = {Tue Mar 01 00:00:00 EST 2016}
}
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Works referencing / citing this record:
Spline-backfitted kernel forecasting for functional-coefficient autoregressive models
preprint, January 2015
- Patrick, Joshua; Harvill, Jane; Sims, Justin
- arXiv