Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

journal of Artificial Intelligence in Electrical Engineering

مقاله 6، دوره 3، شماره 9، شهریور 2014، صفحه 46-53   

چکیده

Short term load forecasting (STLF) plays an important role in the economic and reliable operation of
power systems. Electric load demand has a complex profile with many multivariable and nonlinear
dependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. The
proposed model is capable of forecasting next 24-hour load profile. The main feature in this network
is internal feedback to highlight the effect of past load data for efficient load forecasting results.
Testing results on the three year demand profile shows higher performance with respect to common
feed forward back propagation architecture.

کلیدواژه‌ها

Short term load forecasting (STLF)؛ Recurrent neural network (RNN)؛ hourly load forecast؛ Load Data normalization

مراجع

[1] Eugene A. Feinberg and Dora Genethliou,
"Chapter 12 Load Forecasting" Weather
(2006), Issue: August, Publisher:
Springer, pp. 269-285
[2] Milos Bozic, Milos Stojanovic and Zoran
Stajic, "Short-term electric load
forecasting using least square support
vector machines" Facta Universitatis,
Series: Automatic Control and Robotics
Vol. 9, No 1, pp. 141-150,2010
[3] R. C. Garcia, et al., “GARCH Forecasting
Model to Pre-dict Day-ahead Electricity
Prices,” IEEE Transactions on Power
Systems, Vol. 20, No. 2, May 2005, pp.
867-874.
doi:10.1109/TPWRS.2005.846044
[4] M. Stevenson, “Filtering and Forecasting
Spot Electricity Prices in the Increasingly
Deregulated Australian Elec-tricity
Market,” Quantitative Finance Research
Centre, University of Technology,
Sydney, 2001.
[5] N. Hubele, et al., “Identification of
Seasonal Short-term Load Forecasting
Models Using Statistical Decision
Functions,” IEEE Transactions on Power
Systems, Vol. 5, No. 1, 1990, pp. 40-5.
doi:10.1109/59.49084
[6] M. El-Hawary, et al, “Short-Term Power
System Load Forecasting Using the
Iteratively Reweighted Least Squares
Algorithm,” Electrical Power Systems
Research, Vol. 19, 1990, pp. 11-22.
doi:10.1016/0378-7796(90)900 03-L
[7] V. S. Kodogiannis and E. M.
Anagnostakis, “A Study of Advanced
Learning Algorithms for Short-term Load
Fo-recasting,” Engineering Applications
of Artificial Intelli-gence , Vol. 12, 1999,
pp. 159-173. doi:10.1016/S0952-
1976(98)00064-5
[8] G.-C. Liao and T.-P. Tsao, “Application
of Fuzzy Neural Networks and Artificial
Intelligence for Short-term load
Forecasting,” Electrical Power Systems
Research, Vol. 70, 2004, pp. 237-244.
doi:10.1016/j.epsr. 2003.12.012
[9] H. Yamin, M. Shahidehpour and Z. Li,
“Adaptive short-term Price Forecasting

using artificial Neural Net-works in the
Restructured Power Markets,” Electrical
Power and Energy Systems, Vol. 26,
2004, pp. 571-581.
doi:10.1016/j.ijepes.2004.04.005
[10] A. K. Topalli, I. Erkmen and I. Topalli,
“Intelligent Short-term Load Forecasting
in Turkey,” Electrical Pow-er and Energy
Systems, Vol. 28, 2006, pp. 437-447. doi:
10.1016/j.ijepes.2006.02.004
[11] R.C.Bansal, “Overview and Literature
Survey of Artificial Neural Networks
Applications to Power Systems (1992-
2004)”,IE Journal, Vol86, March, 2006.
[12] M.Tarafdar Haque, A.M.Kashtiban,
“Application of Neural Networks in
Power systems: A Review”, Proceedings
of world Academy of Science and
Technology, Vol 6, June 2006
[13] Zbigniew Gontarand Nikos
Hatziargyriou, “Short Term load
forecasting using Radial basis neural
network”, SM, IEEE2001 IEEE Porto
Power Tech Conference, September,
Porto, Portugal.
[14] J Donald. F. Specht, “Probabilistic neural
networks for classification mapping, or
associative memory”, Proc. IEEE Inf.
Conf. Neural Networks, San Diego, CA,
VOI. 1, pp. 525-532, July 1988.
[15] P. Mandal, T. Senjyu, N. Urasaki and T.
Funabashi, “A Neural Network Based
Several-Hour-Ahead Electric Load
Forecasting using Similar Days
Approach,” Elec-trical Power and Energy
Systems, Vol. 28, 2006, pp. 367-373.
doi:10.1016/j.ijepes.2005.12.007.
[16] Bodn, " A guide to recurrent neural
networks and back propagation", Report
from NUTEK-supported project AIS-8:
Application of data analysis with learning
systems, 1999-2001, Holst, A.(ed), SICS
Technical Report T2002:3, SICS, Kista,
Sweden, 2002.
[17] L. Elman, Finding Structure in time,
Cognitive science, 14, pp.179-211, 1990.
[18] Jordan, "Attractor Dynamics and
parallelism in a connectionist sequential
machine, In Proceedings of the Eighth
conference of the Cognitive Science
Society, pp.531-546, 1986.
[19] ww.energyseec.com/downloadiranedatab
ank.asp. Last visited Jan. 2015