IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v80y2015icp153-165.html
   My bibliography  Save this article

Objective framework for optimal distribution of solar irradiance monitoring networks

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115000646
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.01.046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Rehman, Shafiqur & Ghori, Saleem G, 2000. "Spatial estimation of global solar radiation using geostatistics," Renewable Energy, Elsevier, vol. 21(3), pages 583-605.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nobre, André M. & Severiano, Carlos A. & Karthik, Shravan & Kubis, Marek & Zhao, Lu & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas, 2016. "PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore," Renewable Energy, Elsevier, vol. 94(C), pages 496-509.
    2. 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.
    3. Sadat, Seyyed Ali & Hoex, Bram & Pearce, Joshua M., 2022. "A Review of the Effects of Haze on Solar Photovoltaic Performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    2. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    3. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    4. Sharma, Vishal & Yang, Dazhi & Walsh, Wilfred & Reindl, Thomas, 2016. "Short term solar irradiance forecasting using a mixed wavelet neural network," Renewable Energy, Elsevier, vol. 90(C), pages 481-492.
    5. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    6. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    7. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    8. 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.
    9. Boland, John, 2015. "Spatial-temporal forecasting of solar radiation," Renewable Energy, Elsevier, vol. 75(C), pages 607-616.
    10. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    11. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    12. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    13. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    14. Koo, Choongwan & Hong, Taehoon & Jeong, Kwangbok & Ban, Cheolwoo & Oh, Jeongyoon, 2017. "Development of the smart photovoltaic system blind and its impact on net-zero energy solar buildings using technical-economic-political analyses," Energy, Elsevier, vol. 124(C), pages 382-396.
    15. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    16. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    17. Arumugham, Dinesh Rajan & Rajendran, Parvathy, 2021. "Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data," Renewable Energy, Elsevier, vol. 180(C), pages 1114-1123.
    18. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    19. Huxley, O.T. & Taylor, J. & Everard, A. & Briggs, J. & Tilley, K. & Harwood, J. & Buckley, A., 2022. "The uncertainties involved in measuring national solar photovoltaic electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    20. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:80:y:2015:i:c:p:153-165. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.