Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 175 - 179, 26.12.2016

Öz

Kaynakça

  • [1] W. Shlomo, and C. Kulikowski. "Computer systems that learn." (1991).
  • [2] Bailey, Gerald D., ed. Computer-based integrated learning systems. Educational Technology, 1993.
  • [3] F. Piatetsky-Shapiro, and R. Piatetsky-Shapiro. "Smyth, and Uthurusamy." Advances in Knowledge Discovery and Data Mining (1995).
  • [4] J Jiawei, Han, and Micheline Kamber. "Data mining: concepts and techniques."San Francisco, CA, itd: Morgan Kaufmann 5 (2001).
  • [5] Matheus, Christopher J. Knowledge discovery in databases. Eds. William J. Frawley, and Gregory Piatetsky-Shapiro. Vol. 37. Menlo Park, CA: AAAI Press, 1991.
  • [6] Kusiak, Andrew, Zijun Zhang, and Anoop Verma. "Prediction, operations, and condition monitoring in wind energy." Energy 60 (2013): 1-12.
  • [7] C. W. Potter, A. Archambault, and K. Westrick, “Building a Smarter Grid through Better Renewable Energy Information”, Proceedings of IEEE/PES Power Systems Conference and Exposition, Seattle, USA, March,2009.
  • [8] Kutner, Michael H., Chris Nachtsheim, and John Neter. Applied linear regression models. McGraw-Hill/Irwin, 2004.
  • [9] https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/regress.htm#CIHHFFHB
  • [10] Mellit, A., et al. "Artificial intelligence techniques for sizing photovoltaic systems: A review." Renewable and Sustainable Energy Reviews 13.2 (2009): 406-419.
  • [11] Vapnik V., “The nature of statistical learning theory,” Springer-Verlag, New-York, 1995.
  • [12] Vapnik V., “Statistical learning theory,” John Wiley, New-York, 1998.
  • [13] Vapnik V., “The support vector method of function estimation,” In Nonlinear Modeling: advanced black-box techniques, Suykens J.A.K., Vandewalle J. (Eds.), Kluwer Academic Publishers, Boston, pp.55-85, 1998.
  • [14] http://www.statsoft.com/Textbook/Support-Vector-Machines
  • [15] Belousov A, Verzakov SA, Von Frese J. A flexible classification approach with optimal generalisation performance; support vector machines. Chemom IntellLab Syst 2002;64:15–25.
  • [16] Salcedo-Sanz S, Ortiz-Garcia EG, Perez-Bellido AM, Portilla-Figueras A,Prieto L. Short term wind speed prediction based on support vector regressionalgorithms. Expert Systems with Applications 2011;38(4):4052e7.
  • [17] Mohandes MA, Halawani TO, Rehman S, Hussain AA. Support vector machines for wind speed prediction. Renewable Energy 2004;29(6):939e47.
  • [18] Ortiz-Garcia EG, Salcedo-Sanz S, Perez-Bellido AM, Gascon-Moreno J, Portilla-Figueras JA, Prieto L. Short-term wind speed prediction in wind farms based on banks of support vector machines. Wind Energy 2011;14(2):193e207.
  • [19] Zhou, Junyi, Jing Shi, and Gong Li. "Fine tuning support vector machines for short-term wind speed forecasting." Energy Conversion and Management 52.4 (2011): 1990-1998.
  • [20] Kusiak A, Zhang Z. Short-horizon prediction of wind power: a data-drivenapproach. IEEE Transactions on Energy Conversion 2010;25(4):1112e22.
  • [21] Riahy GH, Abedi M. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable Energy 2008;33(1):35e41.
  • [22] Bossanyi EA. Short-term wind prediction using Kalman filters. Wind Engineering 1985;9(1):1e8.
  • [23] Liu H, Shi J, Erdem E. Prediction of wind speed time series using modified Taylor Kriging method. Energy 2010;35(12):4870e9.
  • [24] Song YD. A new approach for wind speed prediction. Wind Engineering 2000;24(1):35e47.
  • [25] Hong Y, Chang H, Chiu C. Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs. Energy 2010;35(9):3870e6.
  • [26] Bouzgou H, Benoudjit N. Multiple architecture system for wind speed prediction.Applied Energy 2011;88(7):2463e71.
  • [27] Guo Z, Zhao J, Zhang W, Wang J. A corrected hybrid approach for wind speed prediction in hexi corridor of China. Energy 2011;36(3):1668e79.
  • [28] Carro-Calvo L, Salcedo-Sanz S, Kirchner-Bossi N, Portilla-Figueras A, Prieto L, Garcia-Herrera R, et al. Extraction of synoptic pressure patterns for longterm wind speed estimation in wind farms using evolutionary computing. Energy 2011;36(3):1571e81.
  • [29] El-Fouly THM, El-Saadany EF, Salama MMA. One day ahead prediction of wind speed and direction. IEEE Transactions on Energy Conversion 2008;23(1):191e201.
  • [30] Kusiak, Andrew, Zijun Zhang, and Anoop Verma. "Prediction, operations, and condition monitoring in wind energy." Energy 60 (2013): 1-12.
  • [31] Friedman JH. Stochastic gradient boosting. Computational Statistics and Data Analysis 2002;38(4):367e78.
  • [32] Friedman JH. Greedy function approximation: a gradient boosting machine.Annals of Statistics 2001;29(5):1189e232.
  • [33] Breiman L. Random forests. Machine Learning 2001;45(1):5e32.
  • [34] Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole; 1984.
  • [35] Shakhnarovish G, Darrell T, Indyk P. Nearest-neighbor methods in learning and vision. Cambridge, MA: The MIT Press; 2005.
  • [36] Schölkopf B, Burges CJC, Smola AJ. Advances in kernel methods: support vector learning. Cambridge, MA: The MIT Press; 1999.
  • [37] Steinwart I, Christmann A. Support vector machines. New York: Springer-Verlag; 2008.
  • [38] Siegelmann H, Sontag E. Analog computation via neural networks. Theoretical Computer Science 1994;131(2):331e60.
  • [39] Liu GP. Nonlinear identification and control: a neural network approach. London, UK: Springer; 2001.
  • [40] Smith M. Neural networks for statistical modeling. New York: Van Nostrand Reinhold; 1993.
  • [41] Hansen LK, Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990;12(10):993e1001.
  • [42] Kusiak A, Li W. Estimation of wind speed: a data-driven approach. Journal of Wind Engineering and Industrial Aerodynamics 2010;98(10e11):559e67.
  • [43] Kusiak A, Zheng HY, Zhang Z. A wind speed virtual sensor for wind turbines. ASCE Journal of Energy Engineering 2011;137(2):60e9.
  • [44] Barbounis TG, Theocharis JB. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 2007; 177(24):5775e97.
  • [45] Barbounis TG, Theocharis JB. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 2007; 177(24):5775e97.
  • [46] Bilgili M, Sahin B, Yasar A. Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy 2007;32(14):2350e60.
  • [47] Mohandes MA, Rehman S, Halawani TO. A neural networks approach for wind speed prediction. Renewable Energy 1998;13(3):345e54.
  • [48] Kalogirou, Soteris A. "Artificial intelligence in renewable energy systems modeling and prediction." Proceedings of World Renewable Energy Congress VII. 2002.
  • [49] Kusiak, Andrew, and Haiyang Zheng. "Data mining for prediction of wind farm power ramp rates." 2008 IEEE International Conference on Sustainable Energy Technologies. IEEE, 2008.
  • [50] Kusiak, Andrew, Haiyang Zheng, and Zhe Song. "Models for monitoring wind farm power." Renewable Energy 34.3 (2009): 583-590.
  • [51] Kusiak, Andrew, Haiyang Zheng, and Zhe Song. "Short-term prediction of wind farm power: a data mining approach." IEEE Transactions on Energy Conversion 24.1 (2009): 125-136.
  • [52] Kalogirou, Soteris A. "Artificial neural networks in renewable energy systems applications: a review." Renewable and sustainable energy reviews 5.4 (2001): 373-401.
  • [53] Kalogirou SA, Panteliou S, Dentsoras A. Artificial neural networks used for the performance prediction of a thermosyphon solar water heater. Renewable Energy 1999;18(1):87–99.
  • [54] Inman, Rich H., Hugo TC Pedro, and Carlos FM Coimbra. "Solar forecasting methods for renewable energy integration." Progress in energy and combustion science 39.6 (2013): 535-576.
  • [55] Sharma, Navin, et al. "Predicting solar generation from weather forecasts using machine learning." Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, 2011.

A Survey on Learning System Applications in Energy System Modeling and Prediction

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 175 - 179, 26.12.2016

Öz

Learning Systems (LS) such as
machine learning, statistical pattern recognition and neural networks are
computer programs that can learn from sample data and develop a prediction
model that makes prediction for new cases. The most important think related with
a prediction model is to achieve results as closer as to real situation while
making predictions. This is important because being closer to real results help
to reduce the costs of feasibility studies in system installation. The
performance of Learning Systems has been raised in latest years such as it
sometimes exceeds the performance of humans. That’s why the applications of
Learning Systems have been increased in many areas. This paper reviews the
present applications of Learning Systems in energy system modeling and
prediction especially in renewable energy systems such as wind and solar. The
aim of this paper is to create a vision for researchers by gathering the
present applications and outline their merits and limits and the prediction of
their future performance on specific applications. 

Kaynakça

  • [1] W. Shlomo, and C. Kulikowski. "Computer systems that learn." (1991).
  • [2] Bailey, Gerald D., ed. Computer-based integrated learning systems. Educational Technology, 1993.
  • [3] F. Piatetsky-Shapiro, and R. Piatetsky-Shapiro. "Smyth, and Uthurusamy." Advances in Knowledge Discovery and Data Mining (1995).
  • [4] J Jiawei, Han, and Micheline Kamber. "Data mining: concepts and techniques."San Francisco, CA, itd: Morgan Kaufmann 5 (2001).
  • [5] Matheus, Christopher J. Knowledge discovery in databases. Eds. William J. Frawley, and Gregory Piatetsky-Shapiro. Vol. 37. Menlo Park, CA: AAAI Press, 1991.
  • [6] Kusiak, Andrew, Zijun Zhang, and Anoop Verma. "Prediction, operations, and condition monitoring in wind energy." Energy 60 (2013): 1-12.
  • [7] C. W. Potter, A. Archambault, and K. Westrick, “Building a Smarter Grid through Better Renewable Energy Information”, Proceedings of IEEE/PES Power Systems Conference and Exposition, Seattle, USA, March,2009.
  • [8] Kutner, Michael H., Chris Nachtsheim, and John Neter. Applied linear regression models. McGraw-Hill/Irwin, 2004.
  • [9] https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/regress.htm#CIHHFFHB
  • [10] Mellit, A., et al. "Artificial intelligence techniques for sizing photovoltaic systems: A review." Renewable and Sustainable Energy Reviews 13.2 (2009): 406-419.
  • [11] Vapnik V., “The nature of statistical learning theory,” Springer-Verlag, New-York, 1995.
  • [12] Vapnik V., “Statistical learning theory,” John Wiley, New-York, 1998.
  • [13] Vapnik V., “The support vector method of function estimation,” In Nonlinear Modeling: advanced black-box techniques, Suykens J.A.K., Vandewalle J. (Eds.), Kluwer Academic Publishers, Boston, pp.55-85, 1998.
  • [14] http://www.statsoft.com/Textbook/Support-Vector-Machines
  • [15] Belousov A, Verzakov SA, Von Frese J. A flexible classification approach with optimal generalisation performance; support vector machines. Chemom IntellLab Syst 2002;64:15–25.
  • [16] Salcedo-Sanz S, Ortiz-Garcia EG, Perez-Bellido AM, Portilla-Figueras A,Prieto L. Short term wind speed prediction based on support vector regressionalgorithms. Expert Systems with Applications 2011;38(4):4052e7.
  • [17] Mohandes MA, Halawani TO, Rehman S, Hussain AA. Support vector machines for wind speed prediction. Renewable Energy 2004;29(6):939e47.
  • [18] Ortiz-Garcia EG, Salcedo-Sanz S, Perez-Bellido AM, Gascon-Moreno J, Portilla-Figueras JA, Prieto L. Short-term wind speed prediction in wind farms based on banks of support vector machines. Wind Energy 2011;14(2):193e207.
  • [19] Zhou, Junyi, Jing Shi, and Gong Li. "Fine tuning support vector machines for short-term wind speed forecasting." Energy Conversion and Management 52.4 (2011): 1990-1998.
  • [20] Kusiak A, Zhang Z. Short-horizon prediction of wind power: a data-drivenapproach. IEEE Transactions on Energy Conversion 2010;25(4):1112e22.
  • [21] Riahy GH, Abedi M. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable Energy 2008;33(1):35e41.
  • [22] Bossanyi EA. Short-term wind prediction using Kalman filters. Wind Engineering 1985;9(1):1e8.
  • [23] Liu H, Shi J, Erdem E. Prediction of wind speed time series using modified Taylor Kriging method. Energy 2010;35(12):4870e9.
  • [24] Song YD. A new approach for wind speed prediction. Wind Engineering 2000;24(1):35e47.
  • [25] Hong Y, Chang H, Chiu C. Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs. Energy 2010;35(9):3870e6.
  • [26] Bouzgou H, Benoudjit N. Multiple architecture system for wind speed prediction.Applied Energy 2011;88(7):2463e71.
  • [27] Guo Z, Zhao J, Zhang W, Wang J. A corrected hybrid approach for wind speed prediction in hexi corridor of China. Energy 2011;36(3):1668e79.
  • [28] Carro-Calvo L, Salcedo-Sanz S, Kirchner-Bossi N, Portilla-Figueras A, Prieto L, Garcia-Herrera R, et al. Extraction of synoptic pressure patterns for longterm wind speed estimation in wind farms using evolutionary computing. Energy 2011;36(3):1571e81.
  • [29] El-Fouly THM, El-Saadany EF, Salama MMA. One day ahead prediction of wind speed and direction. IEEE Transactions on Energy Conversion 2008;23(1):191e201.
  • [30] Kusiak, Andrew, Zijun Zhang, and Anoop Verma. "Prediction, operations, and condition monitoring in wind energy." Energy 60 (2013): 1-12.
  • [31] Friedman JH. Stochastic gradient boosting. Computational Statistics and Data Analysis 2002;38(4):367e78.
  • [32] Friedman JH. Greedy function approximation: a gradient boosting machine.Annals of Statistics 2001;29(5):1189e232.
  • [33] Breiman L. Random forests. Machine Learning 2001;45(1):5e32.
  • [34] Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole; 1984.
  • [35] Shakhnarovish G, Darrell T, Indyk P. Nearest-neighbor methods in learning and vision. Cambridge, MA: The MIT Press; 2005.
  • [36] Schölkopf B, Burges CJC, Smola AJ. Advances in kernel methods: support vector learning. Cambridge, MA: The MIT Press; 1999.
  • [37] Steinwart I, Christmann A. Support vector machines. New York: Springer-Verlag; 2008.
  • [38] Siegelmann H, Sontag E. Analog computation via neural networks. Theoretical Computer Science 1994;131(2):331e60.
  • [39] Liu GP. Nonlinear identification and control: a neural network approach. London, UK: Springer; 2001.
  • [40] Smith M. Neural networks for statistical modeling. New York: Van Nostrand Reinhold; 1993.
  • [41] Hansen LK, Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990;12(10):993e1001.
  • [42] Kusiak A, Li W. Estimation of wind speed: a data-driven approach. Journal of Wind Engineering and Industrial Aerodynamics 2010;98(10e11):559e67.
  • [43] Kusiak A, Zheng HY, Zhang Z. A wind speed virtual sensor for wind turbines. ASCE Journal of Energy Engineering 2011;137(2):60e9.
  • [44] Barbounis TG, Theocharis JB. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 2007; 177(24):5775e97.
  • [45] Barbounis TG, Theocharis JB. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 2007; 177(24):5775e97.
  • [46] Bilgili M, Sahin B, Yasar A. Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy 2007;32(14):2350e60.
  • [47] Mohandes MA, Rehman S, Halawani TO. A neural networks approach for wind speed prediction. Renewable Energy 1998;13(3):345e54.
  • [48] Kalogirou, Soteris A. "Artificial intelligence in renewable energy systems modeling and prediction." Proceedings of World Renewable Energy Congress VII. 2002.
  • [49] Kusiak, Andrew, and Haiyang Zheng. "Data mining for prediction of wind farm power ramp rates." 2008 IEEE International Conference on Sustainable Energy Technologies. IEEE, 2008.
  • [50] Kusiak, Andrew, Haiyang Zheng, and Zhe Song. "Models for monitoring wind farm power." Renewable Energy 34.3 (2009): 583-590.
  • [51] Kusiak, Andrew, Haiyang Zheng, and Zhe Song. "Short-term prediction of wind farm power: a data mining approach." IEEE Transactions on Energy Conversion 24.1 (2009): 125-136.
  • [52] Kalogirou, Soteris A. "Artificial neural networks in renewable energy systems applications: a review." Renewable and sustainable energy reviews 5.4 (2001): 373-401.
  • [53] Kalogirou SA, Panteliou S, Dentsoras A. Artificial neural networks used for the performance prediction of a thermosyphon solar water heater. Renewable Energy 1999;18(1):87–99.
  • [54] Inman, Rich H., Hugo TC Pedro, and Carlos FM Coimbra. "Solar forecasting methods for renewable energy integration." Progress in energy and combustion science 39.6 (2013): 535-576.
  • [55] Sharma, Navin, et al. "Predicting solar generation from weather forecasts using machine learning." Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, 2011.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Ümit Çiğdem Turhal

Türker Demirci Bu kişi benim

Yayımlanma Tarihi 26 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 4 Sayı: Special Issue-1

Kaynak Göster

APA Turhal, Ü. Ç., & Demirci, T. (2016). A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 175-179. https://doi.org/10.18201/ijisae.270502
AMA Turhal ÜÇ, Demirci T. A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering. Aralık 2016;4(Special Issue-1):175-179. doi:10.18201/ijisae.270502
Chicago Turhal, Ümit Çiğdem, ve Türker Demirci. “A Survey on Learning System Applications in Energy System Modeling and Prediction”. International Journal of Intelligent Systems and Applications in Engineering 4, sy. Special Issue-1 (Aralık 2016): 175-79. https://doi.org/10.18201/ijisae.270502.
EndNote Turhal ÜÇ, Demirci T (01 Aralık 2016) A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 175–179.
IEEE Ü. Ç. Turhal ve T. Demirci, “A Survey on Learning System Applications in Energy System Modeling and Prediction”, International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, ss. 175–179, 2016, doi: 10.18201/ijisae.270502.
ISNAD Turhal, Ümit Çiğdem - Demirci, Türker. “A Survey on Learning System Applications in Energy System Modeling and Prediction”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (Aralık 2016), 175-179. https://doi.org/10.18201/ijisae.270502.
JAMA Turhal ÜÇ, Demirci T. A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:175–179.
MLA Turhal, Ümit Çiğdem ve Türker Demirci. “A Survey on Learning System Applications in Energy System Modeling and Prediction”. International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, 2016, ss. 175-9, doi:10.18201/ijisae.270502.
Vancouver Turhal ÜÇ, Demirci T. A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):175-9.