Eren KAMBER, Sencer KÖRPÜZ, Melih CAN, Hacer YUMURTACI AYDOĞMUŞ, Mehmet GÜMÜŞ

YAPAY SİNİR AĞLARINA DAYALI KISA DÖNEMLİ ELEKTRİK YÜKÜ TAHMİNİ

Günümüzün vazgeçilemez unsurlarından olan elektrik enerjisi için kısa dönemli elektrik tahminleri, son yıllarda yüksek öneme sahip konular arasında yer almaktadır. Elektrik üretimi ile talebin dengelenebilmesi için elektrik talep fiyatlarının doğru tahmin edilmesi önemlidir. Söz konusu denge kurulabildiği takdirde tüketicilere rekabetçi fiyatlar sunulabilmektedir. Elektrik talebinde doğru tahminler yapabilmek için literatürde bazı teknikler kullanılmaktadır. Bu çalışmanın amacı, söz konusu tekniklerden yapay sinir ağını (YSA) uzun kısa dönemli bellek (LSTM) mimarisiyle çalıştırarak kısa süreli elektrik talep tahmininde bulunmaktır. YSA metodolojisinin uygulanmasıyla elde edilen sonuçlar kök ortalama kare hatası değerlerine göre zaman serisi analizi (ARIMA) ile karşılaştırılmıştır. Bu bağlamda, İspanya'nın 2015-2016 yılları arasındaki elektrik verileri tahminleme yapmak için kullanılmıştır. Elektrik enerjisi üretim ve tüketim verileri, İletim Hizmeti Operatörü (TSO) verilerini içeren ve açık erişimli bir portal olan ENTSOE'den toplanmıştır.

Anahtar Kelimeler:

Kısa Dönemli Yük Tahmini, Yapay Sinir Ağı, Zaman Serileri, Uzun Kısa Dönemli Hafıza, Enerji Tahmini

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AlFuhaid A. S., El-Sayed M. A. and Mahmoud M. S., (1997). Cascaded artificial neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 12(4), pp. 1524–1529.
Amjady N., (2001). Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Trans Power Syst, 16, pp. 798–805.
Aydinalp M., Ugursal V.I. and Fung A., (2002). Modeling of the appliance, lighting, and space cooling energy consumptions in the residential sector using neural networks. Applied Energy, 72(2), pp. 87–110.
Bakirtzis A.G., Petridis V., Kiartzis S.J., Alexiadis M.C. and Maissis A.H., (1996). A neural network short term load forecasting model for the Greek power system. IEEE Trans. Power Syst. 11, pp. 858–863.
Bakirtzis A.G., Theocharis J.B., Kiartzis S.J. and Satsois K.J., (1995). Short term load forecasting using fuzzy neural networks. IEEE Trans Power Syst, 10(3), pp. 1518–24.
Beccali M., Cellura M., Lo Brano V., Marvuglia A., (2004). Forecasting daily urban electric load profiles using artificial neural networks. Energy Conversion and Management, 45, pp. 2879–2900.
Box G. E. P. and Jenkins G. M., (1976). Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
Bozkurt, Ö. Ö., Biricik, G., Tayşi, Z. C. (2017). Artificial Neural Network and SARIMA based models for power load forecasting in Turkish Electricity Market, PloS one, 12 (4).
Bridger M.M., (1986). A short guide to electric utility load forecasting.
Broomhead D.S. and Lowe D., (1988). Multivariable Functional İnterpolation and Adaptive Networks. Complex Syst., 2, pp. 321–355.
Brownlee J., (2017). A Gentle Introduction to Autocorrelation and Partial Autocorrelation. Machine Learning Mastery, retrieved from: https://machinelearningmastery.com/gentle-introduction-autocorrelation-partial-autocorrelation/
Carpinteiro O., Reis A. and Silva A., (2004). A hierarchical neural model in short-term load forecasting, Applied Soft Computing, 4, pp. 405-412.
Charytoniuk W., Chen M.S. and Van Olinda P., (1998). Nonparametric regression based shortterm load forecasting. IEEE Trans Power Syst,, 13 pp. 725–730.
Cho M.Y., Hwang J.C. and Chen C.S., (1995). Customer short-term load forecasting by using ARIMA transfer function model. Proc Int Conf Energy Manage Power Deliv., 1, pp. 317–322.
Drezga I. and Rahman S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Trans. Power Syst., 13, pp. 1238–1244.
Drezga I. and Rahman S., (1999). Short-term load forecasting with local ANN predictors. IEEE Trans. Power Syst. 1999, 14, pp. 844–850.
Elman J.L., (1990). Finding structure in time. Cognit. Sci., 14, pp. 179–211.
Elman J.L., (1991). Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learn, 7, pp. 95–126.
Fan J.Y. and McDonald J.D., (1994). A real-time implementation of short-term load forecasting for distribution power systems. IEEE Trans Power Syst, 9, pp. 988–994.
Feinberg E. A. and Genethliou D., (2005). Load forecasting, chapter 12 in Applied Mathematics for Restructured Electric Power Systems: Optimization Control, and Computational Intelligence, Springer- Verlag, New York.
Gall R., (2018).What does LSTM stand for?. Retrieved from: https://hub.packtpub.com/what-is-lstm/#:~:text=LSTM%20stands%20for%20long%20short,in%20the%20current%20neura l%20network.
Gers F. A., Schmidhuber J. and Cummins F., (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12 (10), pp. 2451–2471.
Gers F. A., Schraudolph N. N. and Schmidhuber J., (2003). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, pp. 115–143.
Gross G. and Galiana F. D., (1987). Short-term load forecasting, Proceedings of the IEEE, 75(12), pp. 1558–1573.
Hagan M.T. and Behr S.M., (1987). The time series approach to short term load forecasting. IEEE Trans Power Syst, 2, pp. 785–91.
Haida T. and Muto S., (1994). Regression based peak load forecasting using a transformation technique. IEEE Trans Power Syst, 9, pp. 1788–94.
Hernández L., Baladrón C., Aguiar J.M., Carro B., Sánchez-Esguevillas A. and Lloret J., (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6, pp. 1385-1408; doi:10.3390/en6031385
Hernández L., Baladrón C., Aguiar J.M., Carro B., Sánchez-Esguevillas A., Lloret J., Chinarro D., Gómez J.J. and Cook D., (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Commun. Mag. 51, pp. 106–113.
Hill T., OConnor M., Remus W., (1996). Neural networks models for time series forecasts. Manage Sci, pp. 1082–92.
Hippert H. S., Pedreira C. E., and Souza R. C., (2001). Neural networks for short-term load forecasting: a review and evaluation, IEEE Transactions on Power Systems, 16(1), pp. 44–55.
Ho K.L., Hsu Y.Y., Chen C.F., Lee T.E., Liang C.C., Lai T.S., Chen K.K. (1990). Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Trans. Power Syst. 5, pp. 1214–1221.
Ho K.L., Hsu Y.Y., and Chen F.F., (1990). Short-term load forecasting of Taiwan power system using a knowledge based expert system. IEEE Trans Power Syst, 5, pp. 1214–1221.
Ho K.L., Hsu Y.Y. and Yang C.C., (1992). Short term load forecasting using a multilayer neural network with and adaptative learning algorithm. IEEE Trans. Power Syst. 7, pp. 141–149.
Irisarri G.D., Widergren S.E. and Yehsakul P.D., (1982). On-line load forecasting for energy control center application. IEEE Trans Power Appar Syst; 101:71–8.
Kiartzis S.J. and Bakirtzis A.G., (2000). A fuzzy expert system for peak load forecasting: application to the Greek power system. In: Proceedings of the 10th mediterranean electrotechnical conference, vol. 3; p. 1097–100.
Kohonen T., (1990). The Self-organizing Map. Proc. IEEE, 78, 1464–1480.
Lee K.Y., Cha Y.T. and Park J.H., (1992). Short-term load forecasting using an artificial neural network. IEEE Trans. Power Syst. 7, pp. 124–132.
Lu C.N., Wu H.T. and Vemuri S., (1993). Neural network based short term load forecasting. IEEE Trans. Power Syst. 8, pp. 336–342.
McMenamin J.S. and Monforte F.A., (1998). Short-term energy forecasting with neural networks. Energy J. 19, 43–61.
Miranda V. and Monteiro C. (2000). Fuzzy inference in spatial load forecasting. In:Proceedings of IEEE power engineering winter meeting, vol. 2; pp. 1063–8.
Nounou M.N. and Bakshi B. R., ( 2000). Multiscale Methods for Denoising and Compression. Data Handling in Science and Technology, 22(5), pp. 119-150
Rodrigues F., Cardeira C. and Calado J. M. F., (2014). The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia, 62, pp. 220 – 229.
Ryu S., Noh J. and Kim H., (2017). Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies, 10, 3; doi:10.3390/en10010003
Srinivasan D., Chang D.S. and Liew A.C., (1995). Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting. IEEE Trans Power Syst. 10(4), pp. 1897–903.
Srinivasan D., Liew A.C. and Chang C.S., (1994). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proc. Gener. Transm. Distrib. 141, pp. 561–567.
Papalexopoulos A.D., Hao S. and Peng T.M., (1994). An implementation of a neural network based load forecasting models for the EMS. IEEE Trans. Power Syst, 9, 1956–1962.
Papalexopoulos A.D. and Hesterberg T.C., (1989). A regression based approach to short term system load forecasting. In: Proceedings of PICA conference, 3, pp. 414–23.
Pankratz A., (1983). Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. Wiley, New York.
Park D.C.; El-Sharkawi, M.A.; Marks II, R.J.; Atlas, L.E.; Damborg, M.J. (1991). Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6, pp. 442–449.
Rahman S. and Hazim O., (1996). Load forecasting for multiple sites: development of an expert system-based technique. Electr Power Syst Res,39, pp. 161–169.
Reşat, H. G. (2020). Sürdürülebilir enerji yönetimi için yapay sinir ağları ve ARIMA metotları kullanılarak melez tahmin modelinin tasarlanması ve geliştirilmesi: Tütün endüstrisinde vaka çalışması. Journal of the Faculty of Engineering & Architecture of Gazi University, 35(3).
Rumelhart D., Hinton G. and Williams R., (1996). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition; Rumelhart, D., McClelland, J.L., Eds., MIT Press Cambridge: Cambridge, MA, USA, pp. 318–362.
Taylor J.W., (2012). Short-Term Load Forecasting with Exponentially Weighted Methods, IEEE Transactions on Power Systems, 27, pp. 458-646.
Topalli, A. K., Erkmen, I., Topalli, I. (2006). Intelligent short-term load forecasting in Turkey, International Journal of Electrical Power & Energy Systems, 28(7), 437–447.
Ünlü, R. (2019). A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Turkey Electricity Production. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(2), 635-646.