Photovoltaic power forecasting using statistical methods: impact of weather data
- Author(s): Maria Grazia De Giorgi 1 ; Paolo Maria Congedo 1 ; Maria Malvoni 1
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View affiliations
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Affiliations:
1:
Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy
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Affiliations:
1:
Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy
- Source:
Volume 8, Issue 3,
May 2014,
p.
90 – 97
DOI: 10.1049/iet-smt.2013.0135 , Print ISSN 1751-8822, Online ISSN 1751-8830
An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.
Inspec keywords: power system identification; power system measurement; statistical analysis; power system management; neural nets; load forecasting; regression analysis; power grids; power engineering computing; photovoltaic power systems; time series
Other keywords: meteorological variable measurement; time series; kurtosis parameter; power 960 kW; amplitude error identification; ANN; photovoltaic power forecasting; skewness parameter; multiregression analysis; Italy; grid-connected photovoltaic system; phase error identification; Elmann artificial neural network; PV system; power production prediction; weather data impact; statistical method; decomposition; power system management
Subjects: Other topics in statistics; Other topics in statistics; Power system measurement and metering; Solar power stations and photovoltaic power systems; Power system planning and layout; Neural computing techniques; Power system management, operation and economics; Power engineering computing
References
-
-
1)
-
1. GSE (Italian manager of energy services). Rapporto statistico; 2011. 〈http://www.gse.it〉.
-
-
2)
-
2. Kaplanis, S., Kaplani, E.: ‘A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values’, Renew. Energy, 2007, 32, pp. 1414–25 (doi: 10.1016/j.renene.2006.06.014).
-
-
3)
-
4. Zervas, P.L., Sarimveis, H., Palyvos, J.A., Markatos, N.C.G.: ‘Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques’, Renew. Energy, 2008, 33, pp. 1796–1803 (doi: 10.1016/j.renene.2007.09.020).
-
-
4)
-
23. Chupong, C., Plangklang, B.: ‘Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement’, Energy Procedia, 2011, 9, pp. 230–237 (doi: 10.1016/j.egypro.2011.09.024).
-
-
5)
-
3. Hocaoglu, F.O., Gerek, O.N., Kurban, M.: ‘Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks’, Sol. Energy, 2008, 82, pp. 714–726 (doi: 10.1016/j.solener.2008.02.003).
-
-
6)
-
28. De Giorgi, M.G., Congedo, P.M., Malvoni, M., Tarantino, M.: ‘Short-term power forecasting by statistical methods for photovoltaic plants in south Italy’. Proc. Fourth Imeko TC19 Symp. on Environmental Instrumentation and Measurements Protecting Environment, Climate Changes and Pollution Control, Lecce, Italy, 3–4 June 2013ISBN: 9788896515204 171.
-
-
7)
-
8. Cai, T., Duan, S., Chen, C.: ‘Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement’. Proc. Second Int. Symp. on Power Electronics for Distributed Generation Systems, PEDG 2010, 2010, pp. 773–7.
-
-
8)
-
22. De Giorgi, M.G., Ficarella, A., Tarantino, M.: ‘Error analysis of short term wind power prediction models’, Appl. Energy, 2011, 88, pp. 1298–1311 (doi: 10.1016/j.apenergy.2010.10.035).
-
-
9)
-
10. Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: ‘Irradiance forecasting for the power prediction of grid-connected photovoltaic systems’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2009, 2, (1), pp. 2–9 (doi: 10.1109/JSTARS.2009.2020300).
-
-
10)
-
6. Martín, L., Zarzalejo, L.F., Polo, J., Navarro, A., Marchante, R., Cony, M.: ‘Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning’, Sol. Energy, 2010, 84, pp. 1772–1781 (doi: 10.1016/j.solener.2010.07.002).
-
-
11)
- M. Lange . On the uncertainty of wind power predictions-analysis of the forecast accuracy and statistical distribution of errors. J. Solar Energy Eng. , 2 , 177 - 184
-
12)
-
11. Jimenez, L.A.F., Jimenez, A.M., Falces, A., et al: ‘Short-term power forecasting system for photovoltaic plants’, Renew. Energy, 2012, 44, pp. 311–317 (doi: 10.1016/j.renene.2012.01.108).
-
-
13)
-
24. Congedo, P.M., Malvoni, M., Mele, M., De Giorgi, M.G.: ‘Performance measurements of monocrystalline silicon PV modules in South-eastern Italy’, Energy Convers. Manage., 2013, 68, pp. 1–10 (doi: 10.1016/j.enconman.2012.12.017).
-
-
14)
- J. Cao , X. Lin . Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks. Energy Convers. Manage. , 6 , 1396 - 1406
-
15)
-
1. GSE (Italian manager of energy services). Rapporto statistico; 2011. 〈http://www.gse.it〉.
-
-
16)
-
7. Voyant, C., Muselli, M., Paoli, C., Nivet, M.L.: ‘Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation’, Energy, 2011, 36, pp. 348–359 (doi: 10.1016/j.energy.2010.10.032).
-
-
17)
-
19. Mellit, A.: ‘Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review’, Int. J. Artif. Intell. Soft Comput., 2008, 1, pp. 52–76 (doi: 10.1504/IJAISC.2008.021264).
-
-
18)
-
15. Bacher, P., Madsen, H., Nielsen, H.A.: ‘Online short-term solar power forecasting’, Sol. Energy, 2009, 83, (10), pp. 1772–83 (doi: 10.1016/j.solener.2009.05.016).
-
-
19)
-
5. Mellit, A., Massi Pavan, A.: ‘A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy’, Sol. Energy, 2010, 84, pp. 807–821 (doi: 10.1016/j.solener.2010.02.006).
-
-
20)
-
12. Yona, A., Senjyu, T., Funabashi, T.: ‘Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system’, IEEE Power Eng. Soc. Gen. Meet., 2007, 1–10, pp. 3659–3664.
-
-
21)
-
13. Ruiz-Arias, J.A., Alsamamra, H., Tovar-Pescador, J., Pozo-Vázquez, D.: ‘Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions’, Energy Convers. Manage., 2010, 5, pp. 881–893 (doi: 10.1016/j.enconman.2009.11.024).
-
-
22)
-
9. Paoli, C., Voyant, C., Muselli, M., Nivet, M.L.: ‘Forecasting of preprocessed daily solar radiation time series using neural networks’, Sol. Energy, 2010, 84, pp. 2146–2160 (doi: 10.1016/j.solener.2010.08.011).
-
-
23)
-
21. De Giorgi, M.G., Ficarella, A., Tarantino, M.: ‘Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods’, Energy, 2011, 36, pp. 3968–3978 (doi: 10.1016/j.energy.2011.05.006).
-
-
24)
-
17. Yona, A., Senjyu, T., Saber, A.Y., Funabashi, T., Sekine, H., Kim, C.: ‘Application of neural network to 24-hour-ahead generating power forecasting for PV system’. IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008.
-
-
25)
-
18. Wang, S., Zhang, N., Zhao, Y., Zhan, J.: ‘Photovoltaic system power forecasting based on combined grey model and BP neural network’. Proc. 2011 Int. Conf. on Electrical and Control Engineering, CECE 2011, 2011, pp. 4623–6.
-
-
26)
-
26. Kleissl, J.: ‘Solar energy forecasting and resource assessment’ (Elsevier, 2013) ISBN: 9780123971777.
-
-
27)
- Y. Chiang , L. Chang , F. Chang . Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. J. Hydrol. , 297 - 311
-
28)
-
25. Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H.A., Nielsen, T.S.: ‘Standardizing the performance evaluation of short-term wind power prediction models’, Wind Engergy, 2005, 29, (6), pp. 475–89 (doi: 10.1260/030952405776234599).
-
-
29)
- K.S. Reddy , R. Manish . Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Convers. Manage. , 2519 - 2530
-
30)
-
14. Benghanem, M., Joraid, A.A.: ‘A multiple correlation between different solar parameters in Medina Saudi Arabia’, Renew. Energy, 2007, 14, pp. 2424–2435 (doi: 10.1016/j.renene.2006.12.017).
-
-
1)
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