Research Papers

Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction

[+] Author and Article Information
C. Cornaro

Department of Enterprise Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: cornaro@uniroma2.it

F. Bucci

Department of Enterprise Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: frabucci@gmail.com

M. Pierro

Department of Enterprise Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: marco.pierro@gmail.com

F. Del Frate

Department of Civil Engineering and
Computer Science Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: fabio.delfrate@disp.uniroma2.it

S. Peronaci

Department of Civil Engineering and
Computer Science Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: simone.peronaci@hotmail.it

A. Taravat

Department of Civil Engineering and
Computer Science Engineering,
University of Rome “Tor Vergata”,
Via del Politecnico 1, Rome 00133, Italy
e-mail: art23130@gmail.com

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 28, 2014; final manuscript received December 18, 2014; published online January 8, 2015. Assoc. Editor: Philippe Blanc.

J. Sol. Energy Eng 137(3), 031011 (Jun 01, 2015) (9 pages) Paper No: SOL-14-1104; doi: 10.1115/1.4029452 History: Received March 28, 2014; Revised December 18, 2014; Online January 08, 2015

In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.

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Kleissl, J., 2013, Solar Energy Forecasting and Resource Assessment, 1st ed., Academic Press.
Pelland, S., Remund, J., Kleissl, J., Oozeki, T., and De Brabandere, K., 2013, “Photovoltaic and Solar Forecasting: State of the Art,” IEA PVPS, Task 14, Subtask 3.1, Report No. IEA-PVPS T14-01.
Muller, S. C., and Remund, J., 2010, “Advances in Radiation Forecast Based on Regional Weather Models MMF and WRF,” Proceedings of the 25th EUPVSEC Conference 2010, Valencia, Spain, Sept. 6–9, pp. 4629–4632.
Perez, R., Kivalov, S., Schlemmer, J., Hemker, K., Jr., Rennè, D., and Hoff, T. E., 2010, “Validation of Short and Medium Term Operational Solar Radiation Forecasts in the US,” Sol. Energy, 84(12), pp. 2161–2172. [CrossRef]
Martin, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., and Cony, M., 2010, “Prediction of Global Solar Irradiance Based on Time Series Analysis: Application to Solar Thermal Power Plants Energy Production Planning,” Sol. Energy, 84(10), pp. 1772–1781. [CrossRef]
Mellit, A., and Massi Pavan, A., 2010, “A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant in Trieste, Italy,” Sol. Energy, 84(5), pp. 807–821. [CrossRef]
Voyant, C., Randimivololona, P., Nivet, M. L., Poli, C., and Muselli, M., 2013, “Twenty Four Hours Ahead Global Irradiation Forecasting Using Multi-Layer Perceptron,” Meteorol. Appl., 21(3), pp. 644–655 [CrossRef].
Wang, F., Mi, Z., Su, S., and Zhao, H., 2012, “Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters,” Energies, 5(5), pp. 1355–1370. [CrossRef]
Guarnieri, R. A., Pereira, E. B., and Chou, S. C., 2006, “Solar Radiation Forecast Using Artificial Neural Networks in South Brazil,” Proceedings of the 8th ICSHMO 2006, Foz do Iguaçu, Brazil, Apr. 24–28, pp. 1777–1785.
Chen, C., Duan, S., Cai, T., and Liu, B., 2011, “Online 24-h Solar Power Forecasting Based on Weather Type Classification Using Artificial Neural Network,” Sol. Energy, 85(11), pp. 2856–2870. [CrossRef]
Lorenz, E., Hurka, J., Heinemann, D., and Beyer, H. G., 2009, “Irradiance Forecasting for the Power Prediction of Grid Connected Photovoltaic Systems,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2(1), pp. 2–10. [CrossRef]
Lorenz, E., Remund, J., Müller, S. C., Traunmüller, W., Steinmaurer, G., Pozo, D., Ruiz‐Arias, J. A., Fanego, V. L., Ramirez, L., Romeo, M. G., Kurz, C., Pomares, L. M., and Guerrero, C. G., 2009, “Benchmarking of Different Approaches to Forecast Solar Irradiance,” Proceedings of the 24th European Photovoltaic Solar Energy Conference, Germany, Hamburg, Sept. 21–25, pp. 4199–4208.
Pedro, H. T. C., and Coimbra, C. F. M., 2012, “Assessment of Forecasting Techniques for Solar Power Production With No Exogenous Inputs,” Sol. Energy, 86(7), pp. 2017–2028. [CrossRef]
Lorenz, E., Heinermann, D., Wickramarathne, H., Beyer, H., and Bofinger, S., 2007, “Forecast of Ensemble Power Production by Grid-Connected PV Systems,” Proceedings of the 20th EUPVSEC, Milano, Italy, Sept. 3–7.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F., 2011, “The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System,” Q. J. R. Meteorol. Soc., 137(656), pp. 553–597. [CrossRef]
Spena, A., Cornaro, C., and Serafini, S., 2008, “Outdoor ESTER Test Facility for Advanced Technologies PV Modules,” Proceedings of the 33rd IEEE Photovoltaic Specialists Conference, San Diego, CA, May 11–16, pp. 1–5 [CrossRef].
Zahumenský, I., 2004, Guidelines on Quality Control Procedures for Data From Automatic Weather Stations, World Meteorological Organization, Switzerland.
Marquez, R., and Coimbra, C. F. M., 2011, “Forecasting of Global and Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments and the NWS Database,” Sol. Energy, 85(5), pp. 746–756. [CrossRef]
Beyer, H. G., Polo Martinez, J., Suri, M., Torres, J. L., Lorenz, E., Müller, S. C., Hoyer-Klick, C., and Ineichen, P. D., 2009, “Report on Benchmarking of Radiation Products,” MESoR, Report No. 038665, pp. 108–111.
Bishop, C. M., 1995, Neural Network for Pattern Recognition, Clarendon Press, Oxford, UK, p. 290.


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Fig. 1

HV of irradiance for two different days. Variable day: January 1, 2009 and not variable day: January 11, 2009.

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Fig. 2

PDF of normalized daily variation

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Fig. 3

Sketch of the MLPNN architecture. P1: input vector with R rows. For i-layer, IWi: input weights, LWi: layer weights, bi: bias vector, Si: number of neurons, ai: output vector, and fi: transfer function.

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Fig. 4

(a) correlations between measured data and MPL4.1 and MPL4.2 model forecast data (hourly shape forecasting), (b) correlations between measured data and 1MLP and 5NWPMLP

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Fig. 5

Improvement 1MLP model (a) and 5NWPMLP model (b) with respect to the PM

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Fig. 6

Example of sequence of measured and forecast data for the 1MLP and the 5NWPMLP models

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Fig. 7

On the left: MBE values versus forecast Kcs for the ECMWF; on the right: the same for the 5NWPMLP

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Fig. 8

5NWPMLP model KS test

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Fig. 9

Monthly performance of different forecast models




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