We develop a semiparametric space-time approach to modeling GHI.
Our proposed models include separable and nonseparable covariance structures.
No evidence was found to support assuming a separable covariance structure.
We show nonseparable models have lower root mean square error in a real data set.
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.
- Spatio-temporal model;
- Lattice data;
- Semiparametric time series
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