Hourly Photovoltaics Power Output Prediction for Malaysia Using Support Vector Regression

Abstract:

Reliable solar energy forecasting enables grid operators to manage the grid better as PV penetration level increases. This research explores the use of support vector regression to forecast hourly power output from a grid-connected PV system in Malaysia. Data is obtained from a grid-connected PV system that is equipped with a weather monitoring station. Three parameters are used as input to the forecast model; global irradiance, tilted irradiance and ambient temperature. Results were compared against a persistence model. The SVR model manages to forecast hourly power production with satisfactory accuracy.

• Periodical: Applied Mechanics and Materials (Volume 785)
• Pages: 591-595
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.785.591
• Online since: August 2015
• Authors: Kyairul Azmi Baharin, Hasimah Abd Rahman*, Mohammad Yusri Hassan, Chin Kim Gan
• Keywords: Photovoltaics, Solar Energy Forecasting, Support Vector Machine

References

[1] R. H. Inman, H. T. Pedro, and C. F. Coimbra, Solar forecasting methods for renewable energy integration, Progress in Energy and Combustion Science, vol. 39, no. 6, p.535–576, (2013).

DOI: 10.1016/j.pecs.2013.06.002

[2] J. -L. Chen, G. -S. Li, and S. -J. Wu, Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration, Energy Conversion and Management, vol. 75, p.311–318, (2013).

DOI: 10.1016/j.enconman.2013.06.034

[3] B. B. Ekici, A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems, Measurement, vol. 50, p.255–262, (2014).

DOI: 10.1016/j.measurement.2014.01.010

[4] K. A. Baharin, H. A. Rahman, M. Y. Hassan, and C. K. Gan, Hourly irradiance forecasting in Malaysia using support vector machine, in Energy Conversion (CENCON), 2014 IEEE Conference on, 2014, p.185–190.

DOI: 10.1109/cencon.2014.6967499

[5] H. W. Mei and J. J. Ma, Photovoltaic Power Generation Forecasting Model with Improved Support Vector Machine Regression Based on Rough Set and Similar Day, Advanced Materials Research, vol. 805, p.114–120, (2013).

DOI: 10.4028/www.scientific.net/amr.805-806.114

[6] R. Xu, H. Chen, and X. Sun, Short-term photovoltaic power forecasting with weighted support vector machine, in Automation and Logistics (ICAL), 2012 IEEE International Conference on, 2012, p.248–253.

DOI: 10.1109/ical.2012.6308206

[7] J. G. da S. F. Junior, T. Oozeki, H. Ohtake, K. Shimose, T. Takashima, and K. Ogimoto, Forecasting Regional Photovoltaic Power Generation-A Comparison of Strategies to Obtain One-Day-Ahead Data, Energy Procedia, vol. 57, p.1337–1345, (2014).

DOI: 10.1016/j.egypro.2014.10.124

[8] J. G. Silva Fonseca, T. Oozeki, T. Takashima, G. Koshimizu, Y. Uchida, and K. Ogimoto, Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan, Progress in Photovoltaics: Research and Applications, vol. 20, no. 7, p.874.

DOI: 10.1002/pip.1152

[9] M. Bouzerdoum, A. Mellit, and A. Massi Pavan, A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Solar Energy, vol. 98, p.226–235, (2013).

DOI: 10.1016/j.solener.2013.10.002

[10] A. J. Smola and B. Schӧlkopf, A tutorial on support vector regression, Statistics and computing, vol. 14, no. 3, p.199–222, (2004).

DOI: 10.1023/b:stco.0000035301.49549.88

[11] H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines, Advances in neural information processing systems, vol. 9, p.155–161, (1997).

[12] T. Fletcher, Support vector machines explained, Tutorial paper., Mar, (2009).

[13] C. -C. Chang and C. -J. Lin, LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, p.27, (2011).

DOI: 10.1145/1961189.1961199