Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Shadrack T. Asiedu, Abhilasha Suvedi, Zongjie Wang, Hossein Moradi Rekabdarkolaee and Timothy M. Hansen ()
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Shadrack T. Asiedu: McComish Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA
Abhilasha Suvedi: McComish Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA
Zongjie Wang: Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06268, USA
Hossein Moradi Rekabdarkolaee: Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA
Timothy M. Hansen: McComish Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA

Energies, 2025, vol. 18, issue 10, 1-26

Abstract: Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Keywords: distributed energy resources; FERC 2222; nearest-neighbor Gaussian process; nearest-neighbor random forest; solar irradiance forecasting; spatiotemporal downscaling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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