Data-Mining-based filtering to support Solar Forecasting Methodologies

  • Tiago Pinto
    BISITE - University of Salamanca tmcfp[at]isep.ipp.pt
  • Luis Marques
    GECAD - Polytechnic of Porto
  • Tiago M Sousa
    GECAD - Polytechnic of Porto
  • Isabel Praça
    GECAD - Polytechnic of Porto
  • Zita Vale
    GECAD - Polytechnic of Porto
  • Samuel L Abreu
    General – Alternative Energies Group - IFSC – Instituto Federal de Santa Catarina

Abstract

This paper proposes an hybrid approach for short term solar intensity forecasting, which combines different forecasting methodologies with a clustering algorithm, which plays the role of data filter, in order to support the selection of the best data for training. A set of methodologies based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), used for short term solar irradiance forecast, is implemented and compared in order to facilitate the selection of the most appropriate methods and respective parameters according to the available information and needs. Data from the Brazilian city of Florianópolis, in the state of Santa Catarina, has been used to illustrate the methods applicability and conclusions. The dataset comprises the years of 1990 to 1999 and includes four solar irradiance components as well as other meteorological variables, such as temperature, wind speed and humidity. Conclusions about the irradiance components, parameters and the proposed clustering mechanism are presented. The results are studied and analysed considering both efficiency and effectiveness of the results. The experimental findings show that the hybrid model, combining a SVM approach with a clustering mechanism, to filter the data used for training, achieved promising results, outperforming the approaches without clustering.
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Alessandrini, S. Delle Monache, L. Sperati, S. Cervone, G. An analog ensemble for short-term probabilistic solar power forecast, Applied Energy, Volume 157, 1 November 2015, Pages 95-110

Badescu, V. “Modeling solar radiation at the earth surface”, Springer (2008).

Barzin, R., Chen, J.J. Young, B.R., Farid, M.M., “Application of weather forecast in conjunction with price-based method for PCM solar passive buildings – An experimental study”, Applied Energy, Volume 163, 2016, Pages 9-18,

Bian, W.; Chen, X., "Neural Network for Nonsmooth, Nonconvex Constrained Minimization Via Smooth Approxima-tion," Neural Networks and Learning Systems, IEEE Transactions on , vol.25, no.3, pp.545,556, March 2014

Boser, B.E., Guyon, I.M., Vapnik, V.N., "A Training algorithm for optimal margin classifiers", COLT conference, 1992.

Chicco G., Ilie S., “Support Vector Clustering of Electrical Load Pattern Data”. IEEE Transactions on Power Systems, vol.24, no.3, pp.1619-1628, August 2009

Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N., “Review of solar irradiance forecasting methods and a propo-sition for small-scale insular grids”, Renewable and Sustainable Energy Reviews, Elsevier, vol 27, pp. 65-76, 2013.

Dianhui W.; Tapan, S., "A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps," Neural Networks and Learning Systems, IEEE Transactions on , vol.24, no.10, pp.1677,1688, Oct. 2013

European Commissison, ”The 2020 climate and energy package ”. Available at http://ec.europa.eu/clima/policies/package/index_en.htm , last accessed August 2017.

Fisher, R., “The use of multiple measurements in taxonomic problems. Annals of Eugenics”, 7, 111–132, 1936.

Gupta, R., Gupta, G.; Kastwar, D.; Hussain, A.; Ranjan, H.; ”Modeling and design of MPPT controler for a PV model us-ing PSCAD/EMTDC”. Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2010 IEEE PES, 11-13 October 2010.

Han, J. and Kamber, M. “Data mining: concepts and techniques”. The Morgan Kaufmann series in data management sys-tems, San Francisco, 2006.

Hao Q; Srinivasan, D.; Khosravi, A., "Short-Term Load and Wind Power Forecasting Using Neural Network-Based Pre-diction Intervals," Neural Networks and Learning Systems, IEEE Transactions on , vol.25, no.2, pp.303,315, Feb. 2014

Hocaoglu, F.O., Serttas, F., A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting, Renewable Energy, Volume 108, August 2017, Pages 635-643

Huynh T. Q. and Reggia J. A., “Symbolic Representation of Recurrent Neural Network Dynamics,” IEEE Trans. Neural Networks Learn. Syst., vol. 23, no. 10, pp. 1649–1658, Oct. 2012.

Hyndman, J., Koehler, B., “Another look at measures of forecast accuracy. International journal of forecasting”, 22 (4), 679-688, 2006.

Inman, Rich H., Pedro, Hugo T. C., Coimbra, Carlos R. M., “Solar forecasting methods for renewable energy integration”, Progress in Energy and Combustion Science, Elsevier, vol 39, pp. 535-576, 2013.

Ioakimidis, C., et al, “Solar Production Forecasting Based on Irradiance Forecasting Using Artificial Neural Networks”, 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), pp. 8121 – 8126, Nov 2013.

Jain A. K.. “Data Clustering: 50 years beyond K-Means”. Pattern Recognition Letters, Elsevier, Vol. 31, Issue 8, pp.651-666, June 2010.

Jain, A. K., Murty M. N. and Flynn, P. J. (1999) Data Clustering: A Review. In: ACM Computing Surveys, 31 (3). pp. 264-323.

Keles, D., Scelle, J., Paraschiv, F., Fichtner, W., Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks, Applied Energy, Volume 162, 15 January 2016, Pages 218-230

Kopp, G. and Lean, J. L., A new, lower value of total solar irradiance: Evidence and climate significance, Geophysical Research Letters, VOL. 38, L01706, 2011

Liu, H., Tian, H., Liang, X., Li, Y. “Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks”, Applied Energy, Volume 157, 1 November 2015, Pages 183-194

Liu, N., Tang, Q., Zhang, J., Fan, W., Liu, J. “A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids”, Applied Energy, Volume 129, 15 September 2014, Pages 336-345

Martin, L. Zarzalejo, L. Polo, J. Navarro, A. Marchante, R. Cony, M. “Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning”, Solar Energy, 84 (2010), pp. 1772–1781.

Mohanty, S., Patra, P. K., Sahoo, S. S., Mohanty, A. “Forecasting of solar energy with application for a growing economy like India: Survey and implication”, Renewable and Sustainable Energy Reviews, Volume 78, October 2017, Pag-es 539-553

Nikulin, M.S., "Loss function", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, 2001.

Paolik C, Voyant C, Muselli M, Nivet M. Solar radiation forecasting using ad-hoc time series preprocessing and neural networks. In: Proceeding of the 5th international conference on emerging intelligent computing technology and applications, Ulsan, South Korea; 2009. p. 898–907.

Pedro H. T. C. and Coimbra, C. F. M. “Assessment of forecasting techniques for solar power production with no exoge-nous inputs”, Solar Energy, Elsevier, vol. 86, pp. 2017-2028, 2012.

Pelland, S., Remund, J., Kleissl, J., Oozeki, T., De Brabandere, K. “Photovoltaic and Solar Forecasting: State of the Art”, International Energy Agency Photovoltaic Power Systems Programme, 2013. ISBN: 978-3-906042-13-8.

Persson, C. Bacher, P. Shiga,T., Madsen, H. “Multi-site solar power forecasting using gradient boosted regression trees”, Solar Energy, Volume 150, 1 July 2017, Pages 423-436

Pinto, T. , Ramos, S., Sousa, T. M., Vale, Z. "Short-term wind speed forecasting using Support Vector Machines", 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2014.

Pinto, T. et. al, "Solar Intensity Characterization using Data-Mining to support Solar Forecasting", 12th International Con-ference in Distributed Computing and Artificial Intelligence, Advances in Intelligent Systems and Computing, vol. 373, pp. 193-201, Springer International Publishing, 2015

Pinto, T., Sousa, T.M., Praça, I., Vale, Z., Morais,H. Support Vector Machines for decision support in electricity markets? strategic bidding, Neurocomputing, Volume 172, 8 January 2016, Pages 438-445
Schaefer A. M., Udluft S., and Zimmermann H.-G., “A Recurrent Control Neural Network for Data Efficient Reinforce-ment Learning,” in 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforce-ment Learning, 2007, pp. 151–157.

Schwaegerl, C. and Tao, L. (2013) The Microgrids Concept, in Microgrids: Architectures and Control (ed N. Hatziar-gyriou), John Wiley and Sons Ltd, Chichester, United Kingdom.

Sharma N., Sharma, P., Irwin, D., and Shenoy, P. “Predicting Solar Generation from Weather Forecasts Using Machine Learning”, IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 528 – 533, October 2011.

Singh, V.P., Vijay, V., Bhatt, M. S., Chaturvedi, D. K. “Generalized neural network methodology for short term solar power forecasting”, IEEE 13th International Conference on Environment and Electrical Engineering (EEEIC), pp. 58 - 62 , Nov. 2013.

Sioshansi, F.P., “Evolution of Global Electricity Markets – New paradigms, new challenges, new approaches”, Academic Press, 2013

Smola, A.,Schölkopf, B. “A tutorial on support vector regression”, Statistics and Computing, 14, 199–222, 2004.
Vapnik, V., A. Lerner, A., “Pattern recognition using generalized portrait method. Automation and Remote Control”, 24, 774–780, 1963.

Voyant, C. et al, Machine learning methods for solar radiation forecasting: A review, Renewable Energy, Volume 105, May 2017, Pages 569-582
Wilamowski B. M. and Yu H., “Neural network learning without backpropagation.,” IEEE Trans. Neural Netw., vol. 21, no. 11, pp. 1793–803, Nov. 2010.

Xu, R., Chen, H. and Sun, X.“Short-term Photovoltaic Power Forecasting with Weighted Support Vector Machine”, IEEE International Conference on Automation and Logistics (ICAl-2012), pp. 248 - 253, August 2012

Zeng, J., Qiao, W. “Short-term solar power prediction using a support vector machine”, Renewable Energy, Elsevier, vol. 52, pp. 118-127, 2013.
Zhao, J. et al, An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed, Applied Energy, Volume 162, 15 January 2016, Pages 808
Pinto, T., Marques, L., Sousa, T. M., Praça, I., Vale, Z., & Abreu, S. L. (2017). Data-Mining-based filtering to support Solar Forecasting Methodologies. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 6(3), 85–102. https://doi.org/10.14201/ADCAIJ20176385102

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Author Biography

Tiago Pinto

,
BISITE - University of Salamanca
Researcher, BISITE research group, University of Salamanca
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