This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
This study presents a method to predict a day-ahead solar irradiation curve, under extreme meteorological phenomena (Föhn, north and south winds), existing in the province of San Juan, Argentina. The proposed method is based on an artificial neuronal network (ANN) which is trained with a data set filtered by the environmental variables that characterise the mentioned phenomena. A previously calculated ideal solar irradiation curve is modified from the forecasts generated by the ANN. The proposed methodology merges statistical learning methods and numerical weather prediction (NWP) methods, typically used to improve upon the raw forecast of a NWP model. A reduction of the uncertainty in the power production of photovoltaic plants in San Juan can be achieved with the results of the proposed forecasting method.
References
-
-
1)
-
3. Romero, A.F., Quilumba, F.L., Arcos, H.N.: ‘Short-term active power forecasting of a photovoltaic power plant using an artificial neural network’. International Joint Conference on Neural Networks, Atlanta, GA, USA, 2009, pp. 3335–3340.
-
2)
-
8. Ji, W., Chee, K.C.: ‘Prediction of hourly solar radiation using a novel hybrid model of {ARMA} and {TDNN}’, Sol. Energy, 2011, 85, (5), pp. 808–817.
-
3)
-
5. Alzahrani, A., Shamsi, P., Dagli, C., et al: ‘Solar irradiance forecasting using deep neural networks’, Procedia Comput. Sci., 2017, 114, (1), pp. 989–992.
-
4)
-
9. Aires, B.: ‘Sobre El Recurso Solar En La Provincia De San Juan’, in., 2009.
-
5)
-
1. Tuohy, A., Duffie, J.A., Beckman, W.A., et al: ‘Solar forecasting: methods, challenges, and performance’, IEEE Power Energy Mag., 2013, 13, (6), pp. 50–59.
-
6)
-
2. Duffie, J.A., Beckman, W.A., Worek, W.M.: ‘Solar engineering of thermal processes’. IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador, 2017, pp. 3335–3340.
-
7)
-
6. Cao, S., Cao, J.: ‘Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis’, Appl. Thermal Eng., 2005, 25, (2–3), pp. 161–172.
-
8)
-
10. Norte, F.A.: ‘Características del viento Zonda en la Región de Cuyo’. ., Universidad de Buenos Aires, 1988, p. 245.
-
9)
-
4. Welch, R.L., Ruffing, S.M., Venayagamoorthy, G.K.: ‘Comparison of feedforward and feedback neural network architectures for short term wind speed prediction’. Proc. Int. Jt. Conf. on Neural Networks, San Diego, CA, USA, 2009, pp. 3335–3340.
-
10)
-
7. Cao, J., Lin, X.: ‘Application of the diagonal recurrent wavelet neural network to solar irradiation forecast assisted with fuzzy technique’, Eng. Appl. Artif. Intell., 2008, 21, (8), pp. 1255–1263.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.9368
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