Solar Radiation Forecast Using Cloud Velocity for Photovoltaic Systems

Calvin Kong Leng Sing, Tan Lit Ken, Lai Kok Yee, Jane Oktavia Kamadinata, Nor Azwadi bin Che Sidik, Yutaka Asako, Lee Kee Quen

Abstract


Today, solar energy is used in a many different ways. One of the most popular technological developments for this purpose is photovoltaic conversion to electricity. However, power fluctuations due to the variability of solar energy are one of the challenges faced by the implementation of photovoltaic systems. To overcome this problem, forecasting solar radiation data several minutes in advance is needed. In this research, a methodology to forecast solar radiation using cloud velocity and cloud moving angle is proposed. Generally, a red-to-blue ratio (RBR) color model and correlation analysis are used for obtaining the cloud velocity and moving angle. Artificial neural network (ANN) forecast models with different input combinations are established. This methodology requires lower computational time since it only uses part of the pixels in the sky image. Based on R-squared analysis, it can be concluded that the ANN model with inputs of cloud velocity and moving angle and average solar radiation showed the highest accuracy among other combinations of inputs. The R-squared value was 0.59 with only a relatively small sample size of 42. The proposed model showed a highest improvement of 75.79% when compared to the ANN model based on historical solar radiation data only.

Keywords


cloud velocity; forecasting model; photovoltaic systems; sky image; solar energy.

Full Text:

PDF

References


Ewing, R.A., Harness Nature’s Free Energy to Heat and Power Your Grid-Tied Home, American Geographical Society, Masonville, CO : PixyJack Press, pp. 10-15, 2009.

Sharma, S., Jain, K.K. & Sharma, A., Solar Cells: In Research and Applications-A Review, Materials Sciences and Applications, 6(12), pp. 1145-1155, 2015.

Shrestha, G.B. & Goel, L., A Study on Optimal Sizing of Stand-alone photovoltaic stations, IEEE Transactions on Energy Conversion, 13(4), pp. 373-378, 1998.

Woyte, A., Thong, V.V., Belmans, R. & Nijs, J., Voltage Fluctuations on Distribution Level Introduced by Photovoltaic Systems, IEEE Transactions on Energy Conversion, 21(1), pp. 202-209, 2006.

Jewell, W.T. & Unruh, T.D., Limits on Cloud-induced Fluctuation in Photovoltaic Generation, IEEE Transactions on Energy Conversion, 5(1), pp. 8-14, 1990.

Mellit, A., Benghanem, M., Arab, A.H. & Guessoum, A., A Simplified Model for Generating Sequences of Global Solar Radiation Data for Isolated Sites: Using Artificial Neural Network and a Library of Markov Transition Matrices Approach, Solar Energy, 79(5), pp. 469-482, 2005.

Mellit, A., Arab, A.H., Khorissi, N. & Salhi, H., An ANFIS-based Forecasting for Solar Radiation Data from Sunshine Duration and Ambient Temperature, BT – 2007 IEEE Power Engineering Society General Meeting, PES, June 24, 2007 – June 28, 2007.

Şenkal, O. & Kuleli, T., Estimation of Solar Radiation over Turkey Using Artificial Neural Network and Satellite Data, Applied Energy, 86(7-8), pp. 1222-1228, 2009.

Mousavi, S.M., Mostafavi, E.S. & Jiao, P., Next Generation Prediction Model for Daily Solar Radiation on Horizontal Surface Using a Hybrid Neural Network and Simulated Annealing Method, Energy Conversion and Management, 153(November), pp. 671-682, 2017.

Xue, X., Prediction of Daily Diffuse Solar Radiation Using Artificial Neural Networks, International Journal of Hydrogen Energy, 42(47), pp. 28214-28221, 2017.

Bakirci, K., Prediction of Global Solar Radiation and Comparison with Satellite Data, Journal of Atmospheric and Solar-Terrestrial Physics, 152-153, pp. 41-49, 2017.

Laungrungthip, N., McKinnon, A.E., Churcher, C.D. & Unsworth, K., Edge-based Detection of Sky Regions in Images for Solar Exposure Prediction, 2008 23rd International Conference Image and Vision Computing New Zealand, 2008.

Tzoumanikas, P., Nikitidou, E., Bais, A.F. & Kazantzidis, A., The Effect of Clouds on Surface Solar Irradiance, based on Data from an All-sky Imaging System, Renewable Energy, 95, pp. 314-322, 2016.

Hammer, A., Heinemann, D., Lorenz, E. & Lückehe, B., Short-term Forecasting of Solar Radiation: A Statistical Approach using Satellite Data, Solar Energy, 67(1-3), pp. 139-150, 1999.

Alonso-Montesinos, J., Batlles, F.J. & Portillo, C., Solar Irradiance Forecasting at One-minute Intervals for Different Sky Conditions using Sky Camera Images, Energy Conversion and Management, 105, pp. 1166-1177, 2015.

Alonso, J., Batlles F.J., Villarroel C., Ayala R. & Burgaleta, J.I., Determination of the Sun Area in Sky Camera Images Using Radiometric Data, Energy Conversion and Management, 78, pp.24-31, 2014.

Martínez-Chico, M., Battles, F.J. & Bosch, J.L., Cloud Classification in a Mediterranean Location using Radiation Data and Sky Images, Energy, 36(7), pp. 4055-4062, 2011.

Bernecker, D., Riess, C., Angelopoulou, E. & Hornegger, J., Continuous Short-term Irradiance Forecasts Using Sky Images, Solar Energy, 110, pp. 303-315, 2014.

Chow, C.W., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J., Shields, J. & Washom, B., Intra-hour Forecasting with a Total Sky Imager at the UC San Diego Solar Energy Testbed, Solar Energy, 85(11), pp. 2881-2893, 2011.

Coimbra, C.F.M., Kleissl, J. & Marquez, R., Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation in: Solar Energy Forecasting and Resource Assessment, Elsevier Academic Press, pp. 171-193, 2013.




DOI: http://dx.doi.org/10.5614%2Fj.eng.technol.sci.2018.50.4.3

Refbacks

  • There are currently no refbacks.