Araştırma Makalesi
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Yıl 2023, Cilt: 1 Sayı: 2, 71 - 77, 31.12.2023

Öz

Kaynakça

  • REFERENCES
  • [1] Abedin, Zainal, et al. "A model for prediction of monthly solar irradiation of different meterological locations of Bangladesh using aritficial neural network data mining tool." 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2017. [CrossRef]
  • [2] Raffán, Luis Carlos Parra, Andrés Romero, and Maximiliano Martinez. "Solar energy production forecasting through artificial neuronal networks, considering the Föhn, north and south winds in San Juan, Argentina." The Journal of Engineering 2019.18 (2019): 4824−4829. [CrossRef]
  • [3] Munir, Muhammad Asim, et al. "Solar PV Generation Forecast Model Based on the Most Effective Weather Parameters." 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2019. [CrossRef]
  • [4] Shirbhate, I. M., & Barve, S. S. "Time-Series Energy Prediction using Hidden Markov Model for Smart Solar System." 3rd International Conference on Communication and Electronics Systems (ICCES), 2018. 1123−1127. [CrossRef]
  • [5] Moosa, Aaftaab, et al. "Predicting solar irradiation using machine learning techniques." 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. [CrossRef]
  • [6] Abuella, Mohamed, and Badrul Chowdhury. "Solar power probabilistic forecasting by using multiple linear regression analysis." SoutheastCon 2015. IEEE, 2015. [CrossRef]
  • [7] Chugh, Ayushi, Priyanka Chaudhary, and M. Rizwan. "Fuzzy logic approach for short term solar energy forecasting." 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. [CrossRef]
  • [8] G. Notton et al., "Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting," Renewable and Sustainable Energy Reviews, vol. 87, 2018. pp. 96−105. [CrossRef]
  • [9] Hong, Ying-Yi, John Joel F. Martinez, and Arnel C. Fajardo. "Day-ahead solar irirradiation forecasting utilizing gramian angular field and convolutional long short-term memory." IEEE Access 8 2020): 18741−18753. [CrossRef]
  • [10] Hassan, Md Ziaul, et al. Forecasting day-ahead solar irradiation using machine learning approach. In: 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017. p. 252−258. [CrossRef]
  • [11] Senapatı, Rajendra Narayan; SAHOO, Nirod Chandra; MISHRA, Sukumar. Convolution integral based multivariable grey prediction model for solar energy generation forecasting. In: 2016 IEEE International Conference on Power and Energy (PECon). IEEE, 2016. p. 663−667. [CrossRef]
  • [12] Cros, Sylvain, et al. Extracting cloud motion vectors from satellite images for solar power forecasting. In: 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2014. p. 4123−4126. [CrossRef]
  • [13] Zhang, Nian; Behera, Pradeep K.; Williams, Charles. Solar irradiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks. In: 2013 IEEE International Systems Conference (SysCon). IEEE, 2013. p. 280−286.
  • [14] Lv, Kai, et al. "A novel solar irradiance forecast model using complex network analysis and classification modeling." 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE, 2019. [CrossRef]
  • [15] Jurj, Dacian I., Dan D. Micu, and Alexandru Muresan. "Overview of Electrical Energy Forecasting Methods and Models in Renewable Energy." 2018 International Conference and Exposition on Electrical And Power Engineering (EPE). IEEE, 2018. [CrossRef]
  • [16] Alsharif, Mohammed H., and Mohammad K. Younes. "Evaluation and forecasting of solar irradiation using time series adaptive neuro-fuzzy inference system: Seoul city as a case study." IET Renewable Power Generation 13.10 (2019): 1711−1723. [CrossRef]
  • [17] Brenna, Morris, et al. "Solar irradiation and load power consumption forecasting using neural network." 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, 2017. [CrossRef]
  • [18] Sudirman, Rubita, Kaveh Ashenayi, and Mostafa Golbaba. "Comparison of methods used for forecasting solar irradiation." 2012 IEEE Green Technologies Conference. IEEE, 2012. [CrossRef]
  • [19] Al-Hajj, Rami, Ali Assi, and Mohamad M. Fouad. "Forecasting Solar İrradiation Strength Using Machine Learning Ensemble." 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. [CrossRef]
  • [20] Bâra, Adela, et al. "Comparative analysis between wind and solar forecasting methods using artificial neural networks." 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2015. [CrossRef]
  • [21] Deng, Fangping, et al. "Global solar irradiation modeling using the artificial neural network technique." 2010 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2010. [CrossRef]
  • [22] Assas, Ouarda, et al. "Use of the artificial neural network and meteorological data for predicting daily global solar irradiation in Djelfa, Algeria." 2014 International Conference on Composite Materials & Renewable Energy Applications (ICCMREA). IEEE, 2014. [CrossRef]
  • [23] Alluhaidah, Bader M., S. H. Shehadeh, and M. E. El-Hawary. "Most influential variables for solar irradiation forecasting using artificial neural networks." 2014 IEEE Electrical power and energy conference. IEEE, 2014. [CrossRef]
  • [24]Mbaye, Amy, et al. "Impact of Meteorological Parameters on Short-Term Forecasting: Application to the Dakar Site." 2019 IEEE 2nd International Conference on Power and Energy Applications
  • (ICPEA). IEEE, 2019. [CrossRef] [25] Shaw, Subham, and M. Prakash. "Forecasting Solar Potential Using Support Vector Regression." 2019 Devices for Integrated Circuit (DevIC). IEEE, 2019. [CrossRef]
  • [26] Kumar, Puneet, Nidhi Singh, and M. Ansari. "Solar irradiation forecasting using artificial neural network with different meteorological variables." Communication and Computing Systems-Prasad (et al) (2017): 9781315364094−88. [CrossRef]
  • [27] Narvaez, Gabriel, et al. "Machine learning for site-adaptation and solar irradiation forecasting." Renewable Energy 167 (2021): 333−342. [CrossRef]

Solar irradiation estimation with meteorological data using multi layer neural network approach

Yıl 2023, Cilt: 1 Sayı: 2, 71 - 77, 31.12.2023

Öz

The depletion of fossil fuels and the release of carbon dioxide into the atmosphere have in-creased the importance of alternative energy sources. Therefore, electricity generation is increasing using renewable energy sources. Solar energy has an important place among re-newable energy sources. The reach of solar irradiation to the earth, which is an important pa-rameter for solar power plants, depends on different climatic conditions. The efficiency of the solar power plant depends on the predictive accuracy of the solar irradiation. Accurate irradi-ation estimation improves the efficiency of the Photovoltaic (PV) plant, enabling accurate and efficient programming of the grid and improving power quality. In this study, simultaneous solar radiation values were predicted through a Multilayer Perceptron (MLP) model utilizing atmospheric pressure, relative humidity, ambient temperature, and wind speed parameters obtained from a station established for the measurement of meteorological data. Furthermore, the relationships between the input parameters employed in the prediction model and the output parameter, which is the solar radiation value, were investigated, along with their impact on the prediction accuracy. In the study using the error test method, solar irradiation values were estimated with high accuracy.

Kaynakça

  • REFERENCES
  • [1] Abedin, Zainal, et al. "A model for prediction of monthly solar irradiation of different meterological locations of Bangladesh using aritficial neural network data mining tool." 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2017. [CrossRef]
  • [2] Raffán, Luis Carlos Parra, Andrés Romero, and Maximiliano Martinez. "Solar energy production forecasting through artificial neuronal networks, considering the Föhn, north and south winds in San Juan, Argentina." The Journal of Engineering 2019.18 (2019): 4824−4829. [CrossRef]
  • [3] Munir, Muhammad Asim, et al. "Solar PV Generation Forecast Model Based on the Most Effective Weather Parameters." 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2019. [CrossRef]
  • [4] Shirbhate, I. M., & Barve, S. S. "Time-Series Energy Prediction using Hidden Markov Model for Smart Solar System." 3rd International Conference on Communication and Electronics Systems (ICCES), 2018. 1123−1127. [CrossRef]
  • [5] Moosa, Aaftaab, et al. "Predicting solar irradiation using machine learning techniques." 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. [CrossRef]
  • [6] Abuella, Mohamed, and Badrul Chowdhury. "Solar power probabilistic forecasting by using multiple linear regression analysis." SoutheastCon 2015. IEEE, 2015. [CrossRef]
  • [7] Chugh, Ayushi, Priyanka Chaudhary, and M. Rizwan. "Fuzzy logic approach for short term solar energy forecasting." 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. [CrossRef]
  • [8] G. Notton et al., "Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting," Renewable and Sustainable Energy Reviews, vol. 87, 2018. pp. 96−105. [CrossRef]
  • [9] Hong, Ying-Yi, John Joel F. Martinez, and Arnel C. Fajardo. "Day-ahead solar irirradiation forecasting utilizing gramian angular field and convolutional long short-term memory." IEEE Access 8 2020): 18741−18753. [CrossRef]
  • [10] Hassan, Md Ziaul, et al. Forecasting day-ahead solar irradiation using machine learning approach. In: 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017. p. 252−258. [CrossRef]
  • [11] Senapatı, Rajendra Narayan; SAHOO, Nirod Chandra; MISHRA, Sukumar. Convolution integral based multivariable grey prediction model for solar energy generation forecasting. In: 2016 IEEE International Conference on Power and Energy (PECon). IEEE, 2016. p. 663−667. [CrossRef]
  • [12] Cros, Sylvain, et al. Extracting cloud motion vectors from satellite images for solar power forecasting. In: 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2014. p. 4123−4126. [CrossRef]
  • [13] Zhang, Nian; Behera, Pradeep K.; Williams, Charles. Solar irradiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks. In: 2013 IEEE International Systems Conference (SysCon). IEEE, 2013. p. 280−286.
  • [14] Lv, Kai, et al. "A novel solar irradiance forecast model using complex network analysis and classification modeling." 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE, 2019. [CrossRef]
  • [15] Jurj, Dacian I., Dan D. Micu, and Alexandru Muresan. "Overview of Electrical Energy Forecasting Methods and Models in Renewable Energy." 2018 International Conference and Exposition on Electrical And Power Engineering (EPE). IEEE, 2018. [CrossRef]
  • [16] Alsharif, Mohammed H., and Mohammad K. Younes. "Evaluation and forecasting of solar irradiation using time series adaptive neuro-fuzzy inference system: Seoul city as a case study." IET Renewable Power Generation 13.10 (2019): 1711−1723. [CrossRef]
  • [17] Brenna, Morris, et al. "Solar irradiation and load power consumption forecasting using neural network." 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, 2017. [CrossRef]
  • [18] Sudirman, Rubita, Kaveh Ashenayi, and Mostafa Golbaba. "Comparison of methods used for forecasting solar irradiation." 2012 IEEE Green Technologies Conference. IEEE, 2012. [CrossRef]
  • [19] Al-Hajj, Rami, Ali Assi, and Mohamad M. Fouad. "Forecasting Solar İrradiation Strength Using Machine Learning Ensemble." 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. [CrossRef]
  • [20] Bâra, Adela, et al. "Comparative analysis between wind and solar forecasting methods using artificial neural networks." 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2015. [CrossRef]
  • [21] Deng, Fangping, et al. "Global solar irradiation modeling using the artificial neural network technique." 2010 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2010. [CrossRef]
  • [22] Assas, Ouarda, et al. "Use of the artificial neural network and meteorological data for predicting daily global solar irradiation in Djelfa, Algeria." 2014 International Conference on Composite Materials & Renewable Energy Applications (ICCMREA). IEEE, 2014. [CrossRef]
  • [23] Alluhaidah, Bader M., S. H. Shehadeh, and M. E. El-Hawary. "Most influential variables for solar irradiation forecasting using artificial neural networks." 2014 IEEE Electrical power and energy conference. IEEE, 2014. [CrossRef]
  • [24]Mbaye, Amy, et al. "Impact of Meteorological Parameters on Short-Term Forecasting: Application to the Dakar Site." 2019 IEEE 2nd International Conference on Power and Energy Applications
  • (ICPEA). IEEE, 2019. [CrossRef] [25] Shaw, Subham, and M. Prakash. "Forecasting Solar Potential Using Support Vector Regression." 2019 Devices for Integrated Circuit (DevIC). IEEE, 2019. [CrossRef]
  • [26] Kumar, Puneet, Nidhi Singh, and M. Ansari. "Solar irradiation forecasting using artificial neural network with different meteorological variables." Communication and Computing Systems-Prasad (et al) (2017): 9781315364094−88. [CrossRef]
  • [27] Narvaez, Gabriel, et al. "Machine learning for site-adaptation and solar irradiation forecasting." Renewable Energy 167 (2021): 333−342. [CrossRef]
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çevresel Olarak Sürdürülebilir Mühendislik
Bölüm Research Articles
Yazarlar

Erşan Ömer Yüzer 0000-0002-9089-1358

Altuğ Bozkurt 0000-0001-6458-1260

Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 23 Kasım 2023
Kabul Tarihi 4 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 1 Sayı: 2

Kaynak Göster

Vancouver Yüzer EÖ, Bozkurt A. Solar irradiation estimation with meteorological data using multi layer neural network approach. Clean Energy Technol J. 2023;1(2):71-7.