Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2018, Cilt: 5 Sayı: 3, 131 - 139, 13.12.2018
https://doi.org/10.31593/ijeat.464210

Öz

Kaynakça

  • Shalev-Shwartz, S., Ben-David, S., 2014, “Understanding machine learning: From theory to algorithms.”, Cambridge University Press, England.
  • Jayalakshmi, T., Santhakumaran, A., 2011, “Statistical normalization and back propagationfor classification”, International Journal of Computer Theory and Engineering, 3(1), 89.
  • Nayak, S. C., Misra, B. B., Behera, H. S., 2014, “Impact of data normalization on stock index forecasting.” Int. J. Comp. Inf. Syst. Ind. Manag. Appl, 6, 357-369.
  • Elmas. Ç., 2010, Yapay zeka uygulamaları:(yapay sinir ağı. bulanık mantık. genetik algoritma). Seçkin Yayıncılık, Turkey.
  • Jain. A.K., Mao. J., 1996, “Mohiuddin. K.M. Artificial neural networks: A tutorial”, Computer, 29, 31-44.
  • Yavuz. S., Deveci. M., 2012, “İstatiksel normalizasyon tekniklerinin yapay sinir ağin performansina etkisi”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 167-187.
  • Methaprayoon, K., Yingvivatanapong, C., Lee, W. J., Liao, J. R., 2007, “An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty”, IEEE Transactions on Industry Applications, 43(6), 1441-1448.
  • Sreelakshmi, K., Ramakanthkumar, P., 2008, “Neural networks for short term wind speed prediction”, World Academy of Science, Engineering and Technology, 42, 721-725.
  • Bilgili. M., Sahin. B., 2010, “Comparative analysis of regression and artificial neural network models for wind speed prediction”, Meteorology and atmospheric physics, 109, 61-72.
  • Peng, X., Deng, D., Wen, J., Xiong, L., Feng, S., Wang, B., 2016, “A very short term wind power forecasting approach based on numerical weather prediction and error correction method”, In Electricity Distribution (CICED), 2016 China International Conference on (pp. 1-4). IEEE.
  • Yaniktepe. B., Tasdemir. S., Guher. A.B., Akcan. S., 2016, “Wind power forecasting for the province of osmaniye using artificial neural network method”, International Journal of Intelligent Systems and Applications in Engineering, 4, 114-117.
  • Wang. S., Liu. X., Jin. Y., Qu. K., 2015, “Wind power short term forecasting based on back propagation neural network”, International Journal of Smart Home, 9, 231-240.
  • Kalogirou. S.A., 2001, “Artificial neural networks in renewable energy systems applications: A review”, Renewable and sustainable energy reviews, 5, 373-401.
  • Çetin, M., Uğur, A., Bayzan, Ş., 2006, “İleri beslemeli yapay sinir ağlarında backpropagation (geriye yayılım) algoritmasının sezgisel yaklaşımı”, Akademik Bilişim Kongresi, Pamukkale Üniversitesi.
  • Wang. S., Liu. X., Jin. Y., Qu. K., 2015, “Wind power short term forecasting based on back propagation neural network”, International Journal of Smart Home, 9, 231-240.
  • Yaniktepe. B., Genc. Y.A., 2015, “Establishing new model for predicting the global solar radiation on horizontal surface”, International Journal of Hydrogen Energy, 40, 15278-15283.
  • Giebel. G., Brownsword. R., Kariniotakis. G., Denhard. M., Draxl. C., 2011, “The state-of-the-art in short-term prediction of wind power: A literature overview”, ANEMOS, plus.
  • Quah. T.-S., Srinivasan. B., 1999, “Improving returns on stock investment through neural network selection”, Expert Systems with Applications, 17, 295-301.
  • Mustafidah. H., Hartati. S., Wardoyo. R., Harjoko. A., 2014, “Selection of most appropriate backpropagation training algorithm in data pattern recognition”, International Journal of Computer Trends and Technology (IJCTT), 14(2), 92-95.
  • Chaturvedi, D. K., 2008, “Soft computing: techniques and its applications in electrical engineering”, Vol. 103, Springer.
  • Ş. Taşdemir. Cinar. A.C., 2011, “Application of Artificial Neural Network Forecasting of Daily Maximum Temperature in Konya”, MENDEL 2011-17th International Conference on Soft Computing-, 236-243.
  • Soman, S. S., Zareipour, H., Malik, O., Mandal, P., 2010, “A review of wind power and wind speed forecasting methods with different time horizons”, In North American power symposium (NAPS), IEEE, 1-8.
  • Panigrahi, S., Karali, Y., Behera, H. S., 2013, “Normalize time series and forecast using evolutionary neural network”, Int. J. Eng. Res. Technol, 2(9), 2518-2522.
  • Ataseven, B., 2013, Yapay sinir ağları ile öngörü modellemesi, Öneri Dergisi, 10(39), 101-115.
  • Meade, N., 1983, “Industrial and business forecasting methods”, Lewis, C.D., Borough Green, Sevenoaks, Kent: butterworth. J Forecast 2(2):194–196.

The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network

Yıl 2018, Cilt: 5 Sayı: 3, 131 - 139, 13.12.2018
https://doi.org/10.31593/ijeat.464210

Öz

Artificial Neural Networks is the most used machine learning approach today. It is a very successful method in terms of accuracy and reliability. It is widely used in classification and estimation calculations. In order to achieve the desired performance a model created with ANN, a series of processes such as selection of network structure, learning algorithms, input and output values adjustment and transfer functions determination needs to be implemented in a sensitive manner. Multilayer Feedforward Backpropagation Network, which is used most frequently in supervised learning approaches, was considered in this study. The effect on the prediction performance of the developed model was investigated by using different statistical normalization methods on the data to be used in the network. For this purpose, 4-input 1-output artificial neural networks model were operated with wind-based data taken from Osmaniye Korkut Ata University measuring station. Wind speed, Wind Direction, Humidity and Density data are defined as input values while wind power was defined as output value. Input and output data are calculated with different normalization methods and more than one network models are designed with calculated values. As a result, the study showed that artificial neural networks model which is established by sigmoid normalization method has the best performance value.

Kaynakça

  • Shalev-Shwartz, S., Ben-David, S., 2014, “Understanding machine learning: From theory to algorithms.”, Cambridge University Press, England.
  • Jayalakshmi, T., Santhakumaran, A., 2011, “Statistical normalization and back propagationfor classification”, International Journal of Computer Theory and Engineering, 3(1), 89.
  • Nayak, S. C., Misra, B. B., Behera, H. S., 2014, “Impact of data normalization on stock index forecasting.” Int. J. Comp. Inf. Syst. Ind. Manag. Appl, 6, 357-369.
  • Elmas. Ç., 2010, Yapay zeka uygulamaları:(yapay sinir ağı. bulanık mantık. genetik algoritma). Seçkin Yayıncılık, Turkey.
  • Jain. A.K., Mao. J., 1996, “Mohiuddin. K.M. Artificial neural networks: A tutorial”, Computer, 29, 31-44.
  • Yavuz. S., Deveci. M., 2012, “İstatiksel normalizasyon tekniklerinin yapay sinir ağin performansina etkisi”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 167-187.
  • Methaprayoon, K., Yingvivatanapong, C., Lee, W. J., Liao, J. R., 2007, “An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty”, IEEE Transactions on Industry Applications, 43(6), 1441-1448.
  • Sreelakshmi, K., Ramakanthkumar, P., 2008, “Neural networks for short term wind speed prediction”, World Academy of Science, Engineering and Technology, 42, 721-725.
  • Bilgili. M., Sahin. B., 2010, “Comparative analysis of regression and artificial neural network models for wind speed prediction”, Meteorology and atmospheric physics, 109, 61-72.
  • Peng, X., Deng, D., Wen, J., Xiong, L., Feng, S., Wang, B., 2016, “A very short term wind power forecasting approach based on numerical weather prediction and error correction method”, In Electricity Distribution (CICED), 2016 China International Conference on (pp. 1-4). IEEE.
  • Yaniktepe. B., Tasdemir. S., Guher. A.B., Akcan. S., 2016, “Wind power forecasting for the province of osmaniye using artificial neural network method”, International Journal of Intelligent Systems and Applications in Engineering, 4, 114-117.
  • Wang. S., Liu. X., Jin. Y., Qu. K., 2015, “Wind power short term forecasting based on back propagation neural network”, International Journal of Smart Home, 9, 231-240.
  • Kalogirou. S.A., 2001, “Artificial neural networks in renewable energy systems applications: A review”, Renewable and sustainable energy reviews, 5, 373-401.
  • Çetin, M., Uğur, A., Bayzan, Ş., 2006, “İleri beslemeli yapay sinir ağlarında backpropagation (geriye yayılım) algoritmasının sezgisel yaklaşımı”, Akademik Bilişim Kongresi, Pamukkale Üniversitesi.
  • Wang. S., Liu. X., Jin. Y., Qu. K., 2015, “Wind power short term forecasting based on back propagation neural network”, International Journal of Smart Home, 9, 231-240.
  • Yaniktepe. B., Genc. Y.A., 2015, “Establishing new model for predicting the global solar radiation on horizontal surface”, International Journal of Hydrogen Energy, 40, 15278-15283.
  • Giebel. G., Brownsword. R., Kariniotakis. G., Denhard. M., Draxl. C., 2011, “The state-of-the-art in short-term prediction of wind power: A literature overview”, ANEMOS, plus.
  • Quah. T.-S., Srinivasan. B., 1999, “Improving returns on stock investment through neural network selection”, Expert Systems with Applications, 17, 295-301.
  • Mustafidah. H., Hartati. S., Wardoyo. R., Harjoko. A., 2014, “Selection of most appropriate backpropagation training algorithm in data pattern recognition”, International Journal of Computer Trends and Technology (IJCTT), 14(2), 92-95.
  • Chaturvedi, D. K., 2008, “Soft computing: techniques and its applications in electrical engineering”, Vol. 103, Springer.
  • Ş. Taşdemir. Cinar. A.C., 2011, “Application of Artificial Neural Network Forecasting of Daily Maximum Temperature in Konya”, MENDEL 2011-17th International Conference on Soft Computing-, 236-243.
  • Soman, S. S., Zareipour, H., Malik, O., Mandal, P., 2010, “A review of wind power and wind speed forecasting methods with different time horizons”, In North American power symposium (NAPS), IEEE, 1-8.
  • Panigrahi, S., Karali, Y., Behera, H. S., 2013, “Normalize time series and forecast using evolutionary neural network”, Int. J. Eng. Res. Technol, 2(9), 2518-2522.
  • Ataseven, B., 2013, Yapay sinir ağları ile öngörü modellemesi, Öneri Dergisi, 10(39), 101-115.
  • Meade, N., 1983, “Industrial and business forecasting methods”, Lewis, C.D., Borough Green, Sevenoaks, Kent: butterworth. J Forecast 2(2):194–196.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Sakir Tasdemır

Bulent Yaniktepe

A.burak Guher

Yayımlanma Tarihi 13 Aralık 2018
Gönderilme Tarihi 26 Eylül 2018
Kabul Tarihi 30 Kasım 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 5 Sayı: 3

Kaynak Göster

APA Tasdemır, S., Yaniktepe, B., & Guher, A. (2018). The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. International Journal of Energy Applications and Technologies, 5(3), 131-139. https://doi.org/10.31593/ijeat.464210
AMA Tasdemır S, Yaniktepe B, Guher A. The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. IJEAT. Aralık 2018;5(3):131-139. doi:10.31593/ijeat.464210
Chicago Tasdemır, Sakir, Bulent Yaniktepe, ve A.burak Guher. “The Effect on the Wind Power Performance of Different Normalization Methods by Using Multilayer Feed-Forward Backpropagation Neural Network”. International Journal of Energy Applications and Technologies 5, sy. 3 (Aralık 2018): 131-39. https://doi.org/10.31593/ijeat.464210.
EndNote Tasdemır S, Yaniktepe B, Guher A (01 Aralık 2018) The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. International Journal of Energy Applications and Technologies 5 3 131–139.
IEEE S. Tasdemır, B. Yaniktepe, ve A. Guher, “The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network”, IJEAT, c. 5, sy. 3, ss. 131–139, 2018, doi: 10.31593/ijeat.464210.
ISNAD Tasdemır, Sakir vd. “The Effect on the Wind Power Performance of Different Normalization Methods by Using Multilayer Feed-Forward Backpropagation Neural Network”. International Journal of Energy Applications and Technologies 5/3 (Aralık 2018), 131-139. https://doi.org/10.31593/ijeat.464210.
JAMA Tasdemır S, Yaniktepe B, Guher A. The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. IJEAT. 2018;5:131–139.
MLA Tasdemır, Sakir vd. “The Effect on the Wind Power Performance of Different Normalization Methods by Using Multilayer Feed-Forward Backpropagation Neural Network”. International Journal of Energy Applications and Technologies, c. 5, sy. 3, 2018, ss. 131-9, doi:10.31593/ijeat.464210.
Vancouver Tasdemır S, Yaniktepe B, Guher A. The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. IJEAT. 2018;5(3):131-9.