Solar Radiation Time Series Prediction

A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.

• Cameron Hamilton
• Walter Potter
• Gerrit Hoogenboom
• Ronald McClendon
• Will Hobbs

• Artificial Neural Networks
• Resilient Propagation
• Solar Radiation
• Time Series Forecasting.

[1] Bodri, B. (2001). A neural-network model for earthquake occurrence.
Journal of Geodynamics, 32(3), 289-310.
[2] Ball, R. A., Purcell, L. C., & Carey, S. K. (2004). Evaluation of solar
radiation prediction models in North America. Agronomy Journal, 96(2),
391-397.
[3] Thornton, P. E., Hasenauer, H., & White, M. A. (2000). Simultaneous
estimation of daily solar radiation and humidity from observed
temperature and precipitation: an application over complex terrain in
Austria. Agricultural and Forest Meteorology, 104(4), 255-271.
[4] Mellit, A., &Pavan, A. M. (2010). A 24-h forecast of solar irradiance
using artificial neural network: Application for performance prediction
of a grid-connected PV plant at Trieste, Italy. Solar Energy, 84(5), 807-
821.
[5] Elizondo, D., Hoogenboom, G., & McClendon, R. W. (1994).
Development of a neural network model to predict daily solar radiation.
Agricultural and Forest Meteorology, 71(1), 115-132.
[6] Rehman, S., &Mohandes, M. (2008). Artificial neural network
estimation of global solar radiation using air temperature and relative
humidity. Energy Policy, 36(2), 571-576.
[7] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer
feedforward networks are universal approximators. Neural networks,
2(5), 359-366.
[8] Gueymard, C. A. (2003). Direct solar transmittance and irradiance
predictions with broadband models. Part I: detailed theoretical
performance assessment. Solar Energy, 74(5), 355-379.
[9] Akansu, A. N., & Smith, M. J. (Eds.). (1996). Subband and wavelet
transforms: design and applications (No. 340). Springer.
[10] Jensen, A., & la Cour-Harbo, A. (2001). Ripples in mathematics: the
discrete wavelet transform. Springer. [11] Li, B., McClendon, R. W., & Hoogenboom, G. (2004). Spatial
interpolation of weather variables for single locations using artificial
neural networks. Transactions of the ASAE, 47(2), 629-637.
[12] Richardson, C.W. & Wright, D.A. (1984). WGEN: A model for
generating daily weather variables. U.S. Department of Agriculture,
Agricultural Research Service, ARS-8, 83.
[13] Hoogenboom, G., Jones, J. W., Wilkens, P. W., Batchelor, W. D.,
Bowen, W. T., Hunt, L. A., ... & White, J. W. (1994). Crop models.
DSSAT version, 3(2), 95-244.
[14] Yang, K., Huang, G. W., &Tamai, N. (2001). A hybrid model for
estimating global solar radiation. Solar energy, 70(1), 13-22.
[15] Wong, L. T., & Chow, W. K. (2001). Solar radiation model. Applied
Energy, 69(3), 191-224.
[16] Geiger, M., Diabaté, L., Ménard, L., & Wald, L. (2002). A web service
for controlling the quality of measurements of global solar irradiation.
Solar energy, 73(6), 475-480.
[17] Rigollier, C., Bauer, O., & Wald, L. (2000). On the clear sky model of
the ESRA—European Solar Radiation Atlas—with respect to the
Heliosat method. Solar energy, 68(1), 33-48.
[18] Badescu, V., Gueymard, C. A., Cheval, S., Oprea, C., Baciu, M.,
Dumitrescu, A., & Rada, C. (2012). Computing global and diffuse solar
hourly irradiation on clear sky. Review and testing of 54 models.
Renewable and Sustainable Energy Reviews, 16(3), 1636-1656.
[19] Paoli, C., Voyant, C., Muselli, M., &Nivet, M. L. (2010). Forecasting of
preprocessed daily solar radiation time series using neural networks.
Solar Energy, 84(12), 2146-2160.
[20] Igel, C. &Hüsken, M. (2000) Improving the Rprop Learning Algorithm.
Second International Symposium on Neural Computation (NC 2000),
115-121.
[21] Igel,C. &Hüsken, M. (2003) Empirical Evaluation of the Improved
Rprop Learning Algorithm. Neurocomputing 50:105-123.
[22] Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural
networks and robust time series prediction. Neural Networks, IEEE
Transactions on, 5(2), 240-254.
[23] Giles, C. L., Lawrence, S., &Tsoi, A. C. (2001). Noisy time series
prediction using recurrent neural networks and grammatical inference.
Machine learning, 44(1-2), 161-183.
[24] Cheng, E. S., Chen, S., &Mulgrew, B. (1996). Gradient radial basis
function networks for nonlinear and nonstationary time series prediction.
Neural Networks, IEEE Transactions on, 7(1), 190-194.
[25] Kim, K. J. (2003). Financial time series forecasting using support vector
machines. Neurocomputing, 55(1), 307-319.
[26] Thissen, U., Van Brakel, R., De Weijer, A. P., Melssen, W. J.,
&Buydens, L. M. C. (2003). Using support vector machines for time
series prediction. Chemometrics and intelligent laboratory systems,
69(1), 35-49.
[27] Grant, R. H., Hollinger, S. E., Hubbard, K. G., Hoogenboom, G.,
&Vanderlip, R. L. (2004). Ability to predict daily solar radiation values
from interpolated climate records for use in crop simulation models.
Agricultural and forest meteorology, 127(1), 65-75.
[28] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and
neural network model. Neurocomputing, 50, 159-175.
[29] Ho, S. L., Xie, M., & Goh, T. N. (2002). A comparative study of neural
network and Box-Jenkins ARIMA modeling in time series prediction.
Computers & Industrial Engineering, 42(2), 371-375.
[30] ÖmerFaruk, D. (2010). A hybrid neural network and ARIMA model for
water quality time series prediction. Engineering Applications of
Artificial Intelligence, 23(4), 586-594.
[31] Khashei, M., &Bijari, M. (2011). A novel hybridization of artificial
neural networks and ARIMA models for time series forecasting. Applied
Soft Computing, 11(2), 2664-2675.