Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting

Document Type : Research Article

Authors

1 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

In order to provide an efficient conversion and utilization of solar power, solar radiation data
should be measured continuously and accurately over the long-term period. However, the measurement of
solar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,
several studies were proposed in the literature to find mathematical and physical models to estimate and
forecast the amount of solar radiation such as stochastic prediction models based on time series methods. This
paper proposes a hybridization framework, considering clustering, pre-processing, and training steps for shortterm
solar radiation forecasting. The proposed method is a combination of a novel data clustering method,
time-series analysis, and multilayer perceptron neural network (MLPNN). The proposed Transformed-
Means clustering method is based on inverse data transformation and K-means algorithm that presents more
accurate clustering results when compared to the K-Means algorithm; its improved version and also other
popular clustering algorithms. The performance of the proposed Transformed-Means is evaluated using
several types of datasets and compared with different variants of K-means algorithm. The proposed method
clusters the input solar radiation time-series data into an appropriate number of sub-datasets which are then
preprocessed by the time-series analysis. The preprocessed time-series data provide the input for the training
stage where MLPNN is used to forecast the solar radiation. Solar time-series data with different solar radiation
characteristics are also used to determine the accuracy and the processing speed of the developed forecasting
method with the proposed Transformed-Means and other clustering techniques.

Keywords

Main Subjects

References

[1] A. Mellit, M. Benghanem, A.H. Arab, A. Guessoum, Modelling of sizing the photovoltaic system parameters using artificial neural network, in: Proc. of IEEE, CCA, 2003, pp. 353-357.
[2] M. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, Z. Salameh, A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications, IEEE Transactions on Sustainable Energy, 2(4) (2011) 392-403.
[3] E. Lorenz, D. Heinemann, Prediction of solar irradiance and photovoltaic power, (2012).
[4] A. Mellit, Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review, International Journal of Artificial intelligence and soft computing, 1(1) (2008) 52-76.
[5] K. Tanaka, K. Uchida, K. Ogimi, T. Goya, A. Yona, T. Senjyu, T. Funabashi, C.-H. Kim, Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error, IEEE transactions on smart grid, 2(3) (2011) 438-444.
[6] P. Zhang, Generation Scheduling for Supply and Demand Balancing in Power Systems with Renewable Power Generation, Kyushu University, 2013.
[7] A. Yona, T. Senjyu, T. Funabshi, H. Sekine, Application of neural network to 24-hours-ahead generating power forecasting for PV system, IEEJ Transactions on Power and Energy, 128 (2008) 33-39.
[8] S. Cao, W. Weng, J. Chen, W. Liu, G. Yu, J. Cao, Forecast of solar irradiance using chaos optimization neural networks, in: Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific, IEEE, 2009, pp. 1-4.
[9] G. Capizzi, C. Napoli, F. Bonanno, Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting, IEEE Transactions on neural networks and learning systems, 23(11) (2012) 1805-1815.
[10] J. Shi, W.-J. Lee, Y. Liu, Y. Yang, P. Wang, Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Transactions on Industry Applications, 48(3) (2012) 1064-1069.
[11] T.-C. Yu, H.-T. Chang, The forecast of the electrical energy generated by photovoltaic systems using neural network method, in: Electric Information and Control Engineering (ICEICE), 2011 International Conference on, IEEE, 2011, pp. 2758-2761.
[12] S. Wang, N. Zhang, Y. Zhao, J. Zhan, Photovoltaic system power forecasting based on combined grey model and BP neural network, in: Electrical and Control Engineering (ICECE), 2011 International Conference on, IEEE, 2011, pp. 4623-4626.
[13] T. Kohonen, The self-organizing map, Proceedings of the IEEE, 78(9) (1990) 1464-1480.
[14] C. Cortes, V. Vapnik, Support-vector networks, Machine learning, 20(3) (1995) 273-297.
[15] A.K. Yadav, S. Chandel, Solar radiation prediction
using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews, 33 (2014) 772-781.
[16] S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renewable and sustainable energy reviews, 5(4) (2001) 373-401.
[17] H. Esen, M. Inalli, A. Sengur, M. Esen, Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system, Energy and Buildings, 40(6) (2008) 1074-1083.
[18] C. Paoli, C. Voyant, M. Muselli, M.-L. Nivet, Forecasting of preprocessed daily solar radiation time series using neural networks, Solar Energy, 84(12) (2010) 2146-2160.
[19] M.S. Bobi, Use, operation and maintenance of renewable energy systems: Experiences and future approaches, Springer, 2014.
[20] N. Sengupta, S. Aloka, B. Narayanaswamy, H. Ismail, S. Mathew, Time series data mining for demand side decision support, in: Innovative Smart Grid Technologies-Asia (ISGT Asia), 2013 IEEE, IEEE, 2013, pp. 1-6.
[21] Y. Yang, L. Dong, Short-term PV generation system direct power prediction model on wavelet neural network and weather type clustering, in: Intelligent Human- Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, IEEE, 2013, pp. 207-211.
[22] R. Li, H. Wang, Y. Cui, X. Huang, Solar flare forecasting using learning vector quantity and unsupervised clustering techniques, SCIENCE CHINA Physics, Mechanics & Astronomy, 54(8) (2011) 1546-1552.
[23] K. Benmouiza, A. Cheknane, Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models, Energy Conversion and Management, 75 (2013) 561-569.
[24] M.I. Malinen, R. Mariescu-Istodor, P. Fränti, K-means⁎: Clustering by gradual data transformation, Pattern Recognition, 47(10) (2014) 3376-3386.
[25] http://cs.uef.fi/sipu/clustering/animator/.
[26] D.J. Ketchen Jr, C.L. Shook, The application of cluster analysis in strategic management research: an analysis and critique, Strategic management journal, (1996) 441-458.
[27] http://cs.uef.fi/sipu/datasets.
[28] https://archive.ics.uci.edu/ml/datasets.
[29] http://mesonet.agron.iastate.edu
[30] D. Arthur, S. Vassilvitskii, k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.
[31] J. Herbert, J. Yao, A game-theoretic approach to competitive learning in self-organizing maps, Advances in Natural Computation, (2005) 418-418.
AUT Journal of Electrical Engineering
Volume 49, Issue 2
December 2017
Pages 187-194

Files

Share

How to cite

Statistics