Short-term Solar Irradiance Forecasting Using Different Artificial Neural Network Algorithms

Sanjay Kumar Prajapati*, Mukh Raj Yadav**, Kishan Bhushan Sahay***
*Associate Professor, Department of Electrical Engineering, Suyash Institute of Information Technology, Gorakhpur, Uttar Pradesh, India.
**Associate Professor, Department of Electrical Engineering, SUNRISE Institute of Engineering, Technology and Management, Unnao, Uttar Pradesh, India.
***Assistant Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India.
Periodicity:June - August'2017
DOI : https://doi.org/10.26634/jpr.4.2.13723

Abstract

The expectation of sun oriented radiation is essential for a few applications in renewable vitality research. There are various land variables, which influence sun powered radiation forecast, the recognizable proof of these variables for precise sun powered radiation expectation is vital. This paper explores a mixture strategy for the pressure of sun powered radiation utilizing prescient investigation. The forecast of moment insightful sun oriented radiation is performed by utilizing diverse models of Artificial Neural Networks (ANN), to be specific Multi-Layer Perceptron Neural System (MLPNN), Levenberg-Marquardt, Scaled Conjugate Gradient. Root Mean Square Error (RMSE) is utilized to assess the forecast precision of the three ANN models utilized. The data and information picked up from the present study could enhance the precision of examination concerning atmosphere studies and help in blockage control.

Keywords

Information Compression, Predictive Analysis, Artificial Neural Network, Data Analysis

How to Cite this Article?

Prajapati, S. K., Yadav, M. R., and Sahay, K. B. (2017). Short-term Solar Irradiance Forecasting Using Different Artificial Neural Network Algorithms. i-manager’s Journal on Pattern Recognition, 4(2), 1-9. https://doi.org/10.26634/jpr.4.2.13723

References

[1]. Al-Alawi, S. M., & Al-Hinai, H. A. (1998). An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renewable Energy, 14(1-4), 199-204.
[2]. Badosa, J., Haeffelin, M., & Chepfer, H. (2013). Scales of spatial and temporal variation of solar irradiance on Reunion tropical island. Solar Energy, 88, 42-56.
[3]. Bashir, Z. A., & El-Hawary, M. E. (2009). Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Transactions on Power Systems, 24(1), 20-27.
[4]. Bi, Y., Zhao, J., & Zhang, D. (2004, November). Power load forecasting algorithm based on wavelet packet analysis. In Power System Technology, 2004. Power Conference 2004. 2004 International Conference on (Vol. 1, pp. 987-990). IEEE.
[5]. Boland, J. (1995). Time-series analysis of climatic variables. Solar Energy, 55(5), 377-388.
[6]. Boland, J. (2008). Time series modelling of solar radiation. Modeling Solar Radiation at the Earth's Surface, Springer Verlag, 283-312.
[7]. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
[8]. Chapa, J. O., & Rao, R. M. (2000). Algorithms for designing wavelets to match a specified signal. IEEE Transactions on Signal Processing, 48(12), 3395-3406.
[9]. Dan Foresee, F., & Hagan, M. T. (1997, June). Gauss- Newton Approximation to Bayesian Learning. In International Conference on Neural Networks (Vol. 3, pp. 1930-1935).
[10]. Haessig, P., Multon, B., Ahmed, H. B., Lascaud, S., & Bondon, P. (2015). Energy storage sizing for wind power: Impact of the autocorrelation of day-ahead forecast errors. Wind Energy, 18(1), 43-57.
[11]. Hernandez-Torres, D., Bridier, L., David, M., Lauret, P., & Ardiale, T. (2015). Technico-economical analysis of a hybrid wave power-air compression storage system. Renewable Energy, 74, 708-717.
[12]. Huang, J., Korolkiewicz, M., Agrawal, M., & Boland, J. (2013). Forecasting solar radiation on an hourly time scale using a Coupled Auto Regressive and Dynamical System (CARDS) model. Solar Energy, 87, 136-149.
[13]. Ji, W., & Chee, K. C. (2011). Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Solar Energy, 85(5), 808-817.
[14]. Kaplanis, S. N. (2006). New methodologies to estimate the hourly global solar radiation: Comparisons with existing models. Renewable Energy, 31(6), 781-790.
[15]. Kostylev, V., & Pavlovski, A. (2011, October). Solar power forecasting performance-towards industry standards. In 1st International Workshop on the Integration of Solar Power into Power Systems, Aarhus, Denmark.
[16]. Lorenz, E., Hurka, J., Heinemann, D., & Beyer, H. G. (2009). Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 2-10.
[17]. MacKay, D. J. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415-447.
[18]. Mao, P. L., & Aggarwal, R. K. (2001). A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network. IEEE Transactions on Power Delivery, 16(4), 654-660.
[19]. Mathiesen, P., & Kleissl, J. (2011). Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Solar Energy, 85(5), 967-977.
[20]. Møller, M. F. (1993). A Scaled Conjugate Gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525-533.
[21]. Pelland, S., Galanis, G., & Kallos, G. (2013). Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model. Progress in Photovoltaics: Research and Applications, 21(3), 284-296.
[22]. Yap, K. S., Lim, C. P., & Abidin, I. Z. (2008). A Hybrid ART-GRNN Online Learning Neural Network with a ? - Insensitive Loss Function. IEEE Transactions on Neural Networks, 19(9), 1641-1646.
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