A semiparametric spatio-temporal model for solar irradiance data


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.


  • Irradiance;
  • Spatio-temporal model;
  • Nonseparability;
  • Lattice data;
  • Semiparametric time series

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