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Solar Radiation Modeling with Adaptive Approach

Yıl 2019, Cilt: 3 Sayı: 2, 110 - 115, 10.10.2019

Öz

The unsustainable formation of fossil fuels, increase the
interest on different resources and  this
leads to greater emphasis on clean resources. Solar energy is one of the
popular sources among the renewables. Electricity generation from PV panels
directly related to the solar radiation value measured on surface of the panel.
Modeling of solar radiation is important due to manage the integration of
different sources to the grid. In this study, previously developed Adaptive
Approach method is used for modeling the solar radiation values. This method
combines linear prediction filter method with an empiric approach. Linear
prediction filter used in this study utilize the current value of the solar
radiation to predict next hour
s solar radiation value while the empiric
model utilize from the current value of the solar radiation and the deviation
on extraterrestrial radiation. One year solar radiation data belong to Van
region is used in this study. The accuracies of the forecasting results are
compared and discussed.

Kaynakça

  • [1]. K. Benmouiza and A. Cheknane, “Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models,” Energy Convers. Manag., vol. 75, pp. 561–569, Nov. 2013.
  • [2]. J. R. Trapero, N. Kourentzes, and A. Martin, “Short-term solar irradiation forecasting based on Dynamic Harmonic Regression,” Energy, vol. 84, pp. 289–295, May 2015.
  • [3]. Y. Kashyap, A. Bansal, and A. K. Sao, “Solar radiation forecasting with multiple parameters neural networks,” Renew. Sustain. Energy Rev., vol. 49, pp. 825–835, Sep. 2015.
  • [4]. Y. Gala, Á. Fernández, J. Díaz, and J. R. Dorronsoro, “Hybrid machine learning forecasting of solar radiation values,” Neurocomputing, vol. 176, pp. 48–59, May 2015.
  • [5]. M. Ghayekhloo, M. Ghofrani, M. B. Menhaj, and R. Azimi, “A novel clustering approach for short-term solar radiation forecasting,” Sol. Energy, vol. 122, pp. 1371–1383, Dec. 2015.
  • [6]. L. Mazorra Aguiar, B. Pereira, M. David, F. Díaz, and P. Lauret, “Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks,” Sol. Energy, vol. 122, pp. 1309–1324, Dec. 2015.
  • [7]. F. O. Hocaoğlu, Ö. N. Gerek, and M. Kurban, “Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks,” Sol. Energy, vol. 82, no. 8, pp. 714–726, Aug. 2008.
  • [8]. E. Akarslan, F. O. Hocaoğlu, and R. Edizkan, “A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting,” Energy, vol. 73, pp. 978–986, Aug. 2014.
  • [9]. E. Akarslan and F. O. Hocaoglu, “A novel adaptive approach for hourly solar radiation forecasting,” Renew. Energy, vol. 87, pp. 628–633, Mar. 2016.
  • [10]. R. G. Allen, “Environmental, and E. Water Resources Institute,” Task Comm. Stand. Ref. ASCE Stand. Ref. evapotranspiration equation. Reston, Va. Am. Soc. Civ. Eng., 2005.
  • [11]. N. Z. Al-Rawahi, Y. H. Zurigat, and N. A. Al-Azri, “Prediction of hourly solar radiation on horizontal and inclined surfaces for Muscat/Oman,” J. Eng. Res., vol. 8, no. 2, pp. 19–31, 2011.
  • [12]. P. T. Nastos, A. G. Paliatsos, K. V. Koukouletsos, I. K. Larissi, and K. P. Moustris, “Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece,” Atmos. Res., vol. 144, pp. 141–150, Jul. 2014.
  • [13]. K. P. Moustris, I. C. Ziomas, and A. G. Paliatsos, “3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece,” Water, Air, Soil Pollut., vol. 209, no. 1–4, pp. 29–43, Aug. 2009.
Yıl 2019, Cilt: 3 Sayı: 2, 110 - 115, 10.10.2019

Öz

Kaynakça

  • [1]. K. Benmouiza and A. Cheknane, “Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models,” Energy Convers. Manag., vol. 75, pp. 561–569, Nov. 2013.
  • [2]. J. R. Trapero, N. Kourentzes, and A. Martin, “Short-term solar irradiation forecasting based on Dynamic Harmonic Regression,” Energy, vol. 84, pp. 289–295, May 2015.
  • [3]. Y. Kashyap, A. Bansal, and A. K. Sao, “Solar radiation forecasting with multiple parameters neural networks,” Renew. Sustain. Energy Rev., vol. 49, pp. 825–835, Sep. 2015.
  • [4]. Y. Gala, Á. Fernández, J. Díaz, and J. R. Dorronsoro, “Hybrid machine learning forecasting of solar radiation values,” Neurocomputing, vol. 176, pp. 48–59, May 2015.
  • [5]. M. Ghayekhloo, M. Ghofrani, M. B. Menhaj, and R. Azimi, “A novel clustering approach for short-term solar radiation forecasting,” Sol. Energy, vol. 122, pp. 1371–1383, Dec. 2015.
  • [6]. L. Mazorra Aguiar, B. Pereira, M. David, F. Díaz, and P. Lauret, “Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks,” Sol. Energy, vol. 122, pp. 1309–1324, Dec. 2015.
  • [7]. F. O. Hocaoğlu, Ö. N. Gerek, and M. Kurban, “Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks,” Sol. Energy, vol. 82, no. 8, pp. 714–726, Aug. 2008.
  • [8]. E. Akarslan, F. O. Hocaoğlu, and R. Edizkan, “A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting,” Energy, vol. 73, pp. 978–986, Aug. 2014.
  • [9]. E. Akarslan and F. O. Hocaoglu, “A novel adaptive approach for hourly solar radiation forecasting,” Renew. Energy, vol. 87, pp. 628–633, Mar. 2016.
  • [10]. R. G. Allen, “Environmental, and E. Water Resources Institute,” Task Comm. Stand. Ref. ASCE Stand. Ref. evapotranspiration equation. Reston, Va. Am. Soc. Civ. Eng., 2005.
  • [11]. N. Z. Al-Rawahi, Y. H. Zurigat, and N. A. Al-Azri, “Prediction of hourly solar radiation on horizontal and inclined surfaces for Muscat/Oman,” J. Eng. Res., vol. 8, no. 2, pp. 19–31, 2011.
  • [12]. P. T. Nastos, A. G. Paliatsos, K. V. Koukouletsos, I. K. Larissi, and K. P. Moustris, “Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece,” Atmos. Res., vol. 144, pp. 141–150, Jul. 2014.
  • [13]. K. P. Moustris, I. C. Ziomas, and A. G. Paliatsos, “3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece,” Water, Air, Soil Pollut., vol. 209, no. 1–4, pp. 29–43, Aug. 2009.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

Emre Akarslan

Fatih Onur Hocaoglu

Yayımlanma Tarihi 10 Ekim 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 3 Sayı: 2

Kaynak Göster

APA Akarslan, E., & Hocaoglu, F. O. (2019). Solar Radiation Modeling with Adaptive Approach. European Journal of Engineering and Natural Sciences, 3(2), 110-115.
AMA Akarslan E, Hocaoglu FO. Solar Radiation Modeling with Adaptive Approach. European Journal of Engineering and Natural Sciences. Ekim 2019;3(2):110-115.
Chicago Akarslan, Emre, ve Fatih Onur Hocaoglu. “Solar Radiation Modeling With Adaptive Approach”. European Journal of Engineering and Natural Sciences 3, sy. 2 (Ekim 2019): 110-15.
EndNote Akarslan E, Hocaoglu FO (01 Ekim 2019) Solar Radiation Modeling with Adaptive Approach. European Journal of Engineering and Natural Sciences 3 2 110–115.
IEEE E. Akarslan ve F. O. Hocaoglu, “Solar Radiation Modeling with Adaptive Approach”, European Journal of Engineering and Natural Sciences, c. 3, sy. 2, ss. 110–115, 2019.
ISNAD Akarslan, Emre - Hocaoglu, Fatih Onur. “Solar Radiation Modeling With Adaptive Approach”. European Journal of Engineering and Natural Sciences 3/2 (Ekim 2019), 110-115.
JAMA Akarslan E, Hocaoglu FO. Solar Radiation Modeling with Adaptive Approach. European Journal of Engineering and Natural Sciences. 2019;3:110–115.
MLA Akarslan, Emre ve Fatih Onur Hocaoglu. “Solar Radiation Modeling With Adaptive Approach”. European Journal of Engineering and Natural Sciences, c. 3, sy. 2, 2019, ss. 110-5.
Vancouver Akarslan E, Hocaoglu FO. Solar Radiation Modeling with Adaptive Approach. European Journal of Engineering and Natural Sciences. 2019;3(2):110-5.