Öz
Elektrik enerjisi üretiminde Fosil yakıtlardan, kaynak sıkıntısı olmayan yenilenebilir enerjiye doğru bir geçiş söz konusudur. Bu kaynaklardan en önemlisi güneş enerjisidir.
Güneş panellerinden üretilen enerji ise güneş ışınım miktarıyla doğrudan bağıntılıdır. Bu sebeple güneş ışınım tahmini, üretilecek enerji miktarının talebi karşılamada gereksinim duyulan enerjinin bilinmesi, diğer enerji kaynaklarının ekonomik kullanımını sağlar. Enerji üretiminde talep tahmininin önceden bilinmesi büyük önem taşımaktadır. Enerji talep tahmininde birçok yöntem kullanılmaktadır. Bu çalışmada NARX tahmin modeli kullanılarak sıcaklık, nem ve yağış verilerinin değişken olduğu durumlarında güneş ışınımının tahmini incelenmiştir. Bunun için Ankara ili sınırlarından alınan ölçümler kullanılmıştır. Simülasyon sonuçları tablolar halinde verilerek araştırılması yapılmıştır
Anahtar Kelimeler
Kaynakça
- [1] F. V. Bekun, A. A. Alola, and S. A. Sarkodie, “Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries,” Sci. Total Environ., vol. 657, pp. 1023–1029, Mar. 2019.
- [2] S. A. Hosseini, A. M. Kermani, and A. Arabhosseini, “Experimental study of the dew formation effect on the performance of photovoltaic modules,” Renew. Energy, vol. 130, pp. 352–359, Jan. 2019.
- [3] E. Akarslan and R. Doğan, “Harmonik Sinyallerin Yük Tanımadaki Başarısının İncelenmesi ve Yeni Bir Model Önerisi,” Bilecik Şeyh Edebali Üniversitesi Fen Bilim. Derg., vol. 7, no. 1, pp. 452–460, Jun. 2020.
- [4] M. M. H. Khan, N. S. Muhammad, and A. El-Shafie, “Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting,” J. Hydrol., vol. 590, p. 125380, Nov. 2020.
- [5] J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them,” Acta Chim. Slov., vol. 41, Jan. 1994.
- [6] K. Priddy and P. Keller, Artificial neural networks: an introduction. 2005.
- [7] Z. Boussaada, O. Curea, A. Remaci, H. Camblong, and N. Mrabet Bellaaj, “A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation,” Energies, vol. 11, no. 3, p. 620, Mar. 2018.
- [8] Z. Boussaada et al., “Prediction of the Daily Direct Solar Radiation Using Nonlinear Autoregressive Exogenous (NARX) Network Model,” Jun. 2017.
- [9] H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota, and R. Gadh, “Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method,” in Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2016, vol. 2016-July.
- [10] S. Mohanty, P. K. Patra, and S. S. Sahoo, “Prediction of global solar radiation using nonlinear auto regressive network with exogenous inputs (narx),” in Proceedings of the 2015 39th National Systems Conference, NSC 2015.
- [11] Z. Bounoua and A. Mechaqrane, “Prediction of daily global horizontal solar irradiation using artificial neural networks and commonly measured meteorological parameters,” in AIP Conference Proceedings, 2018, vol. 2056, no. 1, p. 020024.
- [12] A. Ahmad, T. N. Anderson, and T. T. Lie, “Hourly global solar irradiation forecasting for New Zealand,” Sol. Energy, vol. 122, pp. 1398–1408, Dec. 2015.
- [13] M. Louzazni, H. Mosalam, and A. Khouya, “A non-linear auto-regressive exogenous method to forecast the photovoltaic power output,” Sustain. Energy Technol. Assessments, vol. 38, p. 100670, Apr. 2020.
- [14] D. Aşkin, İ. İskender, and A. Mamizadeh, “Farklı Yapay Sinir Ağları Yöntemlerini Kullanarak Kuru Tip Transformatör Sargısının Termal Analizi,” 2011.
- [15] F. Di Nunno and F. Granata, “Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network,” Environ. Res., vol. 190, p. 110062, Nov. 2020.
- [16] K. M. Zubier and L. S. Eyouni, “Investigating the Role of Atmospheric Variables on Sea Level Variations in the Eastern Central Red Sea Using an Artificial Neural Network Approach,” Oceanologia, vol. 62, no. 3, pp. 267–290, Jul. 2020.
- [17] L. Ruiz, M. Cuéllar, M. Calvo-Flores, and M. Jiménez, “An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings,” Energies, vol. 9, no. 9, p. 684, Aug. 2016.
- [18] S. Karasu, A. Altan, Z. Saraç, R. Hacıoğlu, and S. Karasu, “Estimation of Fast Varied Wind Speed Based on Narx Neural Network by Using Curve Fitting,” Oct. 2017.
- [19] E. Cadenas, W. Rivera, R. Campos-Amezcua, and R. Cadenas, “Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México,” Neural Comput. Appl., vol. 27, no. 8, pp. 2417–2428, Nov. 2016.
- [20] J. Cheng, Z. Ji, M. Li, and J. Dai, “Study of a noninvasive blood glucose detection model using the near-infrared light based on SA-NARX,” Biomed. Signal Process. Control, vol. 56, p. 101694, Feb. 2020.
Öz
There is a transition from fossil fuels to renewable energy without resource constraints in electrical energy production. The most important of these resources is solar energy. The energy produced by solar panels is directly related to the amount of solar radiation. For this reason, solar radiation estimation provides the economical use of other energy sources, knowing the energy needed to meet the demand for the amount of energy to be produced. It is very important to know the demand forecast in advance in energy production. Many methods are used in energy demand forecasting. In this study, the prediction of solar radiation in cases where temperature, humidity and precipitation data are variable by using NARX prediction model has been examined. For this, measurements taken from the borders of Ankara province were used. The simulation results are given in tables and researched.
Anahtar Kelimeler
Solar radiationprediction NARX model Temperature Humidity Precipitation
Kaynakça
- [1] F. V. Bekun, A. A. Alola, and S. A. Sarkodie, “Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries,” Sci. Total Environ., vol. 657, pp. 1023–1029, Mar. 2019.
- [2] S. A. Hosseini, A. M. Kermani, and A. Arabhosseini, “Experimental study of the dew formation effect on the performance of photovoltaic modules,” Renew. Energy, vol. 130, pp. 352–359, Jan. 2019.
- [3] E. Akarslan and R. Doğan, “Harmonik Sinyallerin Yük Tanımadaki Başarısının İncelenmesi ve Yeni Bir Model Önerisi,” Bilecik Şeyh Edebali Üniversitesi Fen Bilim. Derg., vol. 7, no. 1, pp. 452–460, Jun. 2020.
- [4] M. M. H. Khan, N. S. Muhammad, and A. El-Shafie, “Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting,” J. Hydrol., vol. 590, p. 125380, Nov. 2020.
- [5] J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them,” Acta Chim. Slov., vol. 41, Jan. 1994.
- [6] K. Priddy and P. Keller, Artificial neural networks: an introduction. 2005.
- [7] Z. Boussaada, O. Curea, A. Remaci, H. Camblong, and N. Mrabet Bellaaj, “A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation,” Energies, vol. 11, no. 3, p. 620, Mar. 2018.
- [8] Z. Boussaada et al., “Prediction of the Daily Direct Solar Radiation Using Nonlinear Autoregressive Exogenous (NARX) Network Model,” Jun. 2017.
- [9] H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota, and R. Gadh, “Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method,” in Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2016, vol. 2016-July.
- [10] S. Mohanty, P. K. Patra, and S. S. Sahoo, “Prediction of global solar radiation using nonlinear auto regressive network with exogenous inputs (narx),” in Proceedings of the 2015 39th National Systems Conference, NSC 2015.
- [11] Z. Bounoua and A. Mechaqrane, “Prediction of daily global horizontal solar irradiation using artificial neural networks and commonly measured meteorological parameters,” in AIP Conference Proceedings, 2018, vol. 2056, no. 1, p. 020024.
- [12] A. Ahmad, T. N. Anderson, and T. T. Lie, “Hourly global solar irradiation forecasting for New Zealand,” Sol. Energy, vol. 122, pp. 1398–1408, Dec. 2015.
- [13] M. Louzazni, H. Mosalam, and A. Khouya, “A non-linear auto-regressive exogenous method to forecast the photovoltaic power output,” Sustain. Energy Technol. Assessments, vol. 38, p. 100670, Apr. 2020.
- [14] D. Aşkin, İ. İskender, and A. Mamizadeh, “Farklı Yapay Sinir Ağları Yöntemlerini Kullanarak Kuru Tip Transformatör Sargısının Termal Analizi,” 2011.
- [15] F. Di Nunno and F. Granata, “Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network,” Environ. Res., vol. 190, p. 110062, Nov. 2020.
- [16] K. M. Zubier and L. S. Eyouni, “Investigating the Role of Atmospheric Variables on Sea Level Variations in the Eastern Central Red Sea Using an Artificial Neural Network Approach,” Oceanologia, vol. 62, no. 3, pp. 267–290, Jul. 2020.
- [17] L. Ruiz, M. Cuéllar, M. Calvo-Flores, and M. Jiménez, “An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings,” Energies, vol. 9, no. 9, p. 684, Aug. 2016.
- [18] S. Karasu, A. Altan, Z. Saraç, R. Hacıoğlu, and S. Karasu, “Estimation of Fast Varied Wind Speed Based on Narx Neural Network by Using Curve Fitting,” Oct. 2017.
- [19] E. Cadenas, W. Rivera, R. Campos-Amezcua, and R. Cadenas, “Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México,” Neural Comput. Appl., vol. 27, no. 8, pp. 2417–2428, Nov. 2016.
- [20] J. Cheng, Z. Ji, M. Li, and J. Dai, “Study of a noninvasive blood glucose detection model using the near-infrared light based on SA-NARX,” Biomed. Signal Process. Control, vol. 56, p. 101694, Feb. 2020.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 15 Haziran 2021 |
| Gönderilme Tarihi | 6 Kasım 2020 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 4 Sayı: 1 |
