Review
Year 2019,
Volume: 3 Issue: 2, 110 - 115, 10.10.2019
Abstract
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.
References
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- [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.
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- [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.
Year 2019,
Volume: 3 Issue: 2, 110 - 115, 10.10.2019
Abstract
References
- [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.
There are 13 citations in total.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Makaleler |
| Authors | |
| Publication Date | October 10, 2019 |
| Published in Issue | Year 2019 Volume: 3 Issue: 2 |
