EN
Solar power plant generation forecasting using NARX neural network model: A case study
Öz
New technologies have been developed and adopted to generate energy from renewable sources to satisfy the increasing demand without causing environmental damage. However, estimating the power output of inherently intermittent, weather-driven, and non-dispatchable renewable energy sources is a major scientific and societal concern. In this study, a neural network model to enable short-to-middle term forecasts of a photovoltaic (PV) power system is provided. Using historical weather and power generation data, a non-linear autoregressive network with exogenous input (NARX) model is built to forecast the non-linear photovoltaic system output. The performance of the model is then analyzed by different statistical evaluation parameters. It is shown that the PV system power output estimation method can be successfully employed.
Anahtar Kelimeler
Kaynakça
- Ayvazoğluyüksek, Ö. and Başaran, Filik Ü. 2018. Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir. Renewable and Sustainable Energy Reviews, 91, 639-653.
- Das, U., Tey, K., Seyedmahmoudian, M., Mekhilef, S., Idris, M., Deventerc, W., Horan, B. and Stojcevski, A. 2018. Forecasting of photovoltaic power generation and model optimization: a review. Renewable and Sustainable Energy Reviews, 81, 912-928.
- Lorenz, E., Hurka, J., Heinemann, D. and Beyer, G. 2009. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE Journal of selected topics in applied earth observations and remote sensing, 2, 1-9.
- Ahmad, A., Anderson, T. and Lie, T. 2015. Hourly global solar irradiation forecasting for New Zealand. Solar Energy, 122, 1398-1408.
- Sharifzadeh, M., Sikinioti Lock, A. and Shah, N. 2019. Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and gaussian process regression. Renewable and Sustainable Energy Reviews, 108, 513-538.
- Solangi, K., Islam, M., Saidur, R., Rahim, N. and Fayaz, H. 2010. A review on global solar energy policy. Renewable and sustainable energy reviews, 15, 2149-2163.
- Küçükdeniz, T. 2010. Long term electricity demand forecasting: An alternative approach with support vector machines. Istanbul University of Engineering Science, 1, 45:53.
- Frohlich, C. and Brusa, R. 1981. Solar radiation and its variation in time. Solar physics, 74, 209-215.
Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yazarlar
Yayımlanma Tarihi
30 Eylül 2021
Gönderilme Tarihi
29 Ocak 2021
Kabul Tarihi
26 Eylül 2021
Yayımlandığı Sayı
Yıl 2021 Cilt: 8 Sayı: 3
APA
Nkurıyıngoma, O., & Selçuklu, S. B. (2021). Solar power plant generation forecasting using NARX neural network model: A case study. International Journal of Energy Applications and Technologies, 8(3), 80-92. https://doi.org/10.31593/ijeat.870088
AMA
1.Nkurıyıngoma O, Selçuklu SB. Solar power plant generation forecasting using NARX neural network model: A case study. International Journal of Energy Applications and Technologies. 2021;8(3):80-92. doi:10.31593/ijeat.870088
Chicago
Nkurıyıngoma, Obed, ve Saltuk Buğra Selçuklu. 2021. “Solar power plant generation forecasting using NARX neural network model: A case study”. International Journal of Energy Applications and Technologies 8 (3): 80-92. https://doi.org/10.31593/ijeat.870088.
EndNote
Nkurıyıngoma O, Selçuklu SB (01 Eylül 2021) Solar power plant generation forecasting using NARX neural network model: A case study. International Journal of Energy Applications and Technologies 8 3 80–92.
IEEE
[1]O. Nkurıyıngoma ve S. B. Selçuklu, “Solar power plant generation forecasting using NARX neural network model: A case study”, International Journal of Energy Applications and Technologies, c. 8, sy 3, ss. 80–92, Eyl. 2021, doi: 10.31593/ijeat.870088.
ISNAD
Nkurıyıngoma, Obed - Selçuklu, Saltuk Buğra. “Solar power plant generation forecasting using NARX neural network model: A case study”. International Journal of Energy Applications and Technologies 8/3 (01 Eylül 2021): 80-92. https://doi.org/10.31593/ijeat.870088.
JAMA
1.Nkurıyıngoma O, Selçuklu SB. Solar power plant generation forecasting using NARX neural network model: A case study. International Journal of Energy Applications and Technologies. 2021;8:80–92.
MLA
Nkurıyıngoma, Obed, ve Saltuk Buğra Selçuklu. “Solar power plant generation forecasting using NARX neural network model: A case study”. International Journal of Energy Applications and Technologies, c. 8, sy 3, Eylül 2021, ss. 80-92, doi:10.31593/ijeat.870088.
Vancouver
1.Obed Nkurıyıngoma, Saltuk Buğra Selçuklu. Solar power plant generation forecasting using NARX neural network model: A case study. International Journal of Energy Applications and Technologies. 01 Eylül 2021;8(3):80-92. doi:10.31593/ijeat.870088
Cited By
Modeling a Solar Power Plant with Artificial Neural Networks
International Journal of Innovative Engineering Applications
https://doi.org/10.46460/ijiea.1336917Forecasting energy production of a PV system connected by using NARX neural network model
AIMS Energy
https://doi.org/10.3934/energy.2024045A tiered NARX model for forecasting day-ahead energy production in distributed solar PV systems
Cleaner Engineering and Technology
https://doi.org/10.1016/j.clet.2024.100831