Research Article
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Year 2018, Volume 5, Issue 3, 131 - 139, 13.12.2018
https://doi.org/10.31593/ijeat.464210

Abstract

References

  • 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

Year 2018, Volume 5, Issue 3, 131 - 139, 13.12.2018
https://doi.org/10.31593/ijeat.464210

Abstract

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.

References

  • 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.

Details

Primary Language English
Subjects Engineering, Multidisciplinary
Journal Section Research Article
Authors

Sakir TASDEMIR> (Primary Author)
SELCUK UNIVERSITY, DEPARTMENT OF COMPUTER ENGINEERING
0000-0002-2433-246X
Türkiye


Bulent YANİKTEPE>
OSMANİYE KORKUT ATA UNIVERSITY, DEPARTMENT OF ENERGY SYSTEMS ENGINEERING
Türkiye


A.burak GUHER>
OSMANİYE KORKUT ATA UNIVERSITY, DEPARTMENT OF INFORMATICS
0000-0002-3971-6765
Türkiye

Publication Date December 13, 2018
Application Date September 26, 2018
Acceptance Date November 30, 2018
Published in Issue Year 2018, Volume 5, Issue 3

Cite

Bibtex @research article { ijeat464210, journal = {International Journal of Energy Applications and Technologies}, eissn = {2548-060X}, address = {editor.ijeat@gmail.com}, publisher = {İlker ÖRS}, year = {2018}, volume = {5}, number = {3}, pages = {131 - 139}, doi = {10.31593/ijeat.464210}, title = {The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network}, key = {cite}, author = {Tasdemır, Sakir and Yaniktepe, Bulent and Guher, A.burak} }
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 . DOI: 10.31593/ijeat.464210
MLA 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" . International Journal of Energy Applications and Technologies 5 (2018 ): 131-139 <https://dergipark.org.tr/en/pub/ijeat/issue/41119/464210>
Chicago 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". International Journal of Energy Applications and Technologies 5 (2018 ): 131-139
RIS TY - JOUR T1 - The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network AU - SakirTasdemır, BulentYaniktepe, A.burakGuher Y1 - 2018 PY - 2018 N1 - doi: 10.31593/ijeat.464210 DO - 10.31593/ijeat.464210 T2 - International Journal of Energy Applications and Technologies JF - Journal JO - JOR SP - 131 EP - 139 VL - 5 IS - 3 SN - -2548-060X M3 - doi: 10.31593/ijeat.464210 UR - https://doi.org/10.31593/ijeat.464210 Y2 - 2018 ER -
EndNote %0 International Journal of Energy Applications and Technologies The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network %A Sakir Tasdemır , Bulent Yaniktepe , A.burak Guher %T The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network %D 2018 %J International Journal of Energy Applications and Technologies %P -2548-060X %V 5 %N 3 %R doi: 10.31593/ijeat.464210 %U 10.31593/ijeat.464210
ISNAD Tasdemır, Sakir , Yaniktepe, Bulent , Guher, A.burak . "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 (December 2018): 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. 2018; 5(3): 131-139.
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. International Journal of Energy Applications and Technologies. 2018; 5(3): 131-139.
IEEE S. Tasdemır , B. Yaniktepe and A. 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, vol. 5, no. 3, pp. 131-139, Dec. 2018, doi:10.31593/ijeat.464210