Solar Intensity Characterization Using Data-Mining to Support Solar Forecasting

  • Tiago PintoEmail author
  • Gabriel Santos
  • Luis Marques
  • Tiago M. Sousa
  • Isabel Praça
  • Zita Vale
  • Samuel L. Abreu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)


The increase of renewable based generation as alternative power source brings an added uncertainty to power systems. The intermittent nature of renewable resources, such as wind speed and solar intensity, requires the use of adequate forecast methodologies to support the management and integration of this type of energy resources. This paper proposes a clustering methodology to group historic data according to the data correlation and relevance for different contexts of use. Using the clustering process as a data filter only the most adequate data is used for the training process of forecasting methodologies. Artificial Neural Networks and Support Vector Machines are used to test and compare the quality of forecasts when using the proposed methodology to select the training data. Data from the Brazilian city of Florianópolis, Santa Catarina, has been used, including solar irradiance components and other meteorological variables, e.g. temperature, wind speed and humidity. Experimental findings show that using the proposed method to filter data used for training ANN and SVM achieved promising results, outperforming the approaches without clustering.


Artificial Neural Network Clustering Data Mining Machine Learning Solar Forecasting Support Vector Machine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    European Commission, The 2020 climate and energy package, (last accessed December 2014)
  2. 2.
    Silva, M., et al.: An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation. Energy Conversion and Management 64, 273–288 (2012)CrossRefGoogle Scholar
  3. 3.
    Pinto, T., et al.: Dynamic Artificial Neural Network for Electricity Market Prices Forecast. In: IEEE 16th Int. Conference on Intelligent Engineering Systems, Portugal (June 2012)Google Scholar
  4. 4.
    Pereira, R., Sousa, T.M., Pinto, T., Praça, I., Vale, Z., Morais, H.: Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines. In: Bajo Perez, J., et al. (eds.) Trends in Practical Applications of Heterogeneous Multi-agent Systems. The PAAMS Collection. AISC, vol. 293, pp. 9–18. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  5. 5.
    Kopp, G., Lean, J.L.: A new, lower value of total solar irradiance: Evidence and climate significance. Geophysical Research Letters 38, L01706 (2011)Google Scholar
  6. 6.
    Inman, R., et al.: Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science 39, 535–576 (2013)CrossRefGoogle Scholar
  7. 7.
    Pelland, S., et al.: Photovoltaic and Solar Forecasting: State of the Art. International Energy Agency Photovoltaic Power Systems Programme (2013)Google Scholar
  8. 8.
    Diagne, M., et al.: Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Ren. and Sust. Energy Reviews 27, 65–76 (2013)CrossRefGoogle Scholar
  9. 9.
    Pedro, H., Coimbra, C.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy 86, 2017–2028 (2012)CrossRefGoogle Scholar
  10. 10.
    Ioakimidis, C.S., et al.: Solar Production Forecasting Based on Irradiance Forecasting Using Artificial Neural Networks. In: IEEE Industrial Electronics Society Conference (2013)Google Scholar
  11. 11.
    Quan, H., et al.: Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals. IEEE Trans. NN and Learning Systems 25(2) (February 2014)Google Scholar
  12. 12.
    Xu, R., et al.: Short-term Photovoltaic Power Forecasting with Weighted Support Vector Machine. In: IEEE International Conference on Automation and Logistics (August 2012)Google Scholar
  13. 13.
    Zeng, J., Qiao, W.: Short-term solar power prediction using a support vector machine. Renewable Energy 52, 118–127 (2013)CrossRefGoogle Scholar
  14. 14.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K.: Data Clustering: 50 years beyond K-Means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  16. 16.
    Ilie, C.: Support Vector Clustering of Electrical Load Pattern Data. IEEE Transactions on Power Systems 24(3), 1619–1628 (2009)CrossRefGoogle Scholar
  17. 17.
    Abreu, S.L., et al.: Qualificação e Recuperação de Dados de Radiação Solar Medidos em Florianópolis – SC. In: 8th Brazilian Congress of Thermal Engineering and Sciences, Porto Alegre (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tiago Pinto
    • 1
    Email author
  • Gabriel Santos
    • 1
  • Luis Marques
    • 1
  • Tiago M. Sousa
    • 1
  • Isabel Praça
    • 1
  • Zita Vale
    • 1
  • Samuel L. Abreu
    • 2
  1. 1.GECAD – Knowledge Engineering and Decision-Support Research Center, Institute of EngineeringPolytechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.General Alternative Energies Group - IFSC – Instituto Federal de Santa CatarinaFlorianópolisBrazil

Personalised recommendations