access icon openaccess Solar energy production forecasting through artificial neuronal networks, considering the Föhn, north and south winds in San Juan, Argentina

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 06/11/2018, Accepted 10/01/2019, Published 15/03/2019
Loading full text...

Full text loading...

/deliver/fulltext/joe/2019/18/JOE.2018.9368.html;jsessionid=1614f5uxnlvuk.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fjoe.2018.9368&mimeType=html&fmt=ahah

Inspec keywords: statistical analysis; load forecasting; neural nets; learning (artificial intelligence); weather forecasting; wind; photovoltaic power systems; solar power stations; sunlight

Other keywords: forecasting method; south winds; day-ahead solar irradiation curve; raw forecast; environmental variables; Föhn; power production; San Juan; artificial neuronal network; ANN; Argentina; numerical weather prediction methods; mentioned phenomena; solar energy production forecasting; calculated ideal solar irradiation curve; methodology merges statistical learning methods; extreme meteorological phenomena

Subjects: Knowledge engineering techniques; Solar power stations and photovoltaic power systems; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Probability theory, stochastic processes, and statistics; Winds and their effects in the lower atmosphere; Other topics in statistics; Neural computing techniques; Power system planning and layout; Other topics in statistics; Weather analysis and prediction

References

    1. 1)
      • 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.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 9. Aires, B.: ‘Sobre El Recurso Solar En La Provincia De San Juan’, inRaichijk, C., Grossi Gallegos, H., Aristegui, R., Righini, R. Grupo de Estudios de la Radiación Solar (GERSolar), Instituto de Ecología y Desarrollo Sustentable (INEDES), Universidad Nacional de Luján (UNLu), Rutas 7 y Avda., 2009.
    10. 10)
      • 10. Norte, F.A.: ‘Características del viento Zonda en la Región de Cuyo’. Tesis Dr., Universidad de Buenos Aires, 1988, p. 245.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.9368
Loading

Related content

content/journals/10.1049/joe.2018.9368
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading