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Objective framework for optimal distribution of solar irradiance monitoring networks

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  • Zagouras, Athanassios
  • Kolovos, Alexander
  • Coimbra, Carlos F.M.

Abstract

Time-resolved characterization of solar irradiance at the ground level is a critical element in solar energy analysis. Siting of nodes in a network of solar irradiance monitoring stations (MS) is a multi-faceted problem that directly affects the determination of the solar resource and its spatio-temporal variability. The present work proposes an objective framework to optimize the deployment of solar MS over a sub-continental region. There are two main components in the proposed methodology. The first employs cluster analysis using the affinity propagation algorithm, to select the optimal number of clusters (regions with coherent solar microclimates) upon internal coherence criteria. The second component employs stochastic prediction and validation, through the use of a Bayesian maximum entropy method, and selects the optimal MS configuration, according to geostatistical criteria, among the solutions recommended by the cluster analysis. We apply this two-pronged methodology to determine clusters and optimal locations for global horizontal irradiance monitoring across the state of California. In this proof-of-concept study, 3 disparate MS configurations are examined within the cluster partition. The subsequent geostatistical analysis indicates that all configurations rank almost equally well based on different statistical error measures. The optimal configuration can be singled out depending on desired criteria of choice.

Suggested Citation

  • Zagouras, Athanassios & Kolovos, Alexander & Coimbra, Carlos F.M., 2015. "Objective framework for optimal distribution of solar irradiance monitoring networks," Renewable Energy, Elsevier, vol. 80(C), pages 153-165.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:153-165
    DOI: 10.1016/j.renene.2015.01.046
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    References listed on IDEAS

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    1. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Perpiñán-Lamigueiro, O., 2013. "Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain," Renewable Energy, Elsevier, vol. 60(C), pages 604-614.
    2. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    3. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    4. Rehman, Shafiqur & Ghori, Saleem G, 2000. "Spatial estimation of global solar radiation using geostatistics," Renewable Energy, Elsevier, vol. 21(3), pages 583-605.
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    3. Laguarda, A. & Alonso-Suárez, R. & Terra, R., 2020. "Solar irradiation regionalization in Uruguay: Understanding the interannual variability and its relation to El Niño climatic phenomena," Renewable Energy, Elsevier, vol. 158(C), pages 444-452.
    4. Soulis, Konstantinos X. & Manolakos, Dimitris & Ntavou, Erika & Kosmadakis, George, 2022. "A geospatial analysis approach for the operational assessment of solar ORC systems. Case study: Performance evaluation of a two-stage solar ORC engine in Greece," Renewable Energy, Elsevier, vol. 181(C), pages 116-128.
    5. Lung-Chang Chien & Yu-An Chen & Hwa-Lung Yu, 2018. "Lagged Influence of Fine Particulate Matter and Geographic Disparities on Clinic Visits for Children’s Asthma in Taiwan," IJERPH, MDPI, vol. 15(4), pages 1-14, April.

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