Neural Networks in Electric Load Forecasting:A Comprehensive Survey
محورهای موضوعی : journal of Artificial Intelligence in Electrical EngineeringVahid Mansouri 1 , Mohammad Esmaeil akbari 2
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کلید واژه: Artificial Neural Networks (ANNs), Load Forecasting(LF), Short Term LF, Mid Term LF, Long Term LF, Peak LF, Unit Commitment(UC),
چکیده مقاله :
Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs inLF, (5) ANNs in Special applications of LF. The major research articles for each category are brieflydescribed and the related literature reviewed. Conclusions are made on future research directions.
[1] SRINIVASAN, D., and LEE, M. A.,
1995, Survey of hybrid fuzzy neural
approaches to electric load forecasting.
Proceedings of the IEEE International
Conference on Systems, Man and
Cybernetics, Part 5, Vancouver, BC, pp.
4004-4008.
[2] K. L. Ho, Y. Y. Hsu, C. F. Chen, T. E.
Lee, C. C. Liang, T. S. Lai, and K. K.
Chen, “Short term load forecasting of
Taiwan power system using a knowledgebased
expert system,” IEEE Trans.
Power Systems, vol. 5, no. 4, pp. 1214–
1221, 1990.
[3] S. Rahman and O. Hazim, “A generalized
knowledge-based short-term loadforecasting
technique,” IEEE T. Power
Syst, vol. 8, no. 2, pp. 508–514, 1993.
[4] RustumMamlook , Omar Badran,
EmadAbdulhadi, A fuzzy inference
model for short-term load forecasting,
Energy Policy Volume 37 issue 4, 2009.
[5] HUANG Jing, MA Jing, XIAO Xian-
Yong, Mid-Long Term Load Forecasting
Based on Fuzzy Optimal Theory, IEEE
Asia-Pacific Power and Energy
Engineering Conference (APPEEC),
2011.
[6] Juan J. Cárdenas, Luis Romeral, Antonio
Garcia, Fabio Andrade, Load forecasting
framework of electricity consumptions
for an Intelligent Energy Management
System in the user-side, Expert Systems
with Applications Volume 39 issue 5,
2012.
[7] I. Moghram and S. Rahman, “Analysis
and evaluation of five short-term load
forecasting techniques,” IEEE Trans.
Power Systems, vol. 4, no. 4, pp. 1484–
1491, 1989.
[8] Young-Min Wi, Sung-Kwan Joo, and
Kyung-Bin Song, Holiday Load
Forecasting Using Fuzzy Polynomial
Regression With Weather Feature
Selection and Adjustment, IEEE
Transactions on Power Systems Volume
27 issue 2, 2012.
[9] A. G. Bakirtzis, J. B. Theocharis, S. J.
Kiartzis, and K. J. Satsios, “Short-term
load forecasting using fuzzy neural
networks,” IEEE Trans. Power Systems,
vol. 10, no. 3, pp. 1518–1524, 1995.
[10] S.E. Papadakis, J.B. Theocharis, A.G.
Bakirtzis, load curve based fuzzy
modeling technique for short-term load
forecasting, Fuzzy Sets and Systems
Volume 135 issue 2, 2003.
[11] S. E. Papadakis, J. B. Theocharis, S. J.
Kiartzis, and A. G. Bakirtzis, “A novel
approach to short-term load forecasting
using fuzzy neural networks,” IEEE
Trans. Power Systems, vol. 13, no. 2, pp.
480–492, 1998.
[12] H. Mori and H. Kobayashi, “Optimal
fuzzy inference for short-term load
forecasting,” IEEE Trans. Power
Systems, vol. 11, no. 1, pp. 390–396,
1996.
[13] T. Czernichow, A. Piras, K. Imhof, P.
Caire, Y. Jaccard, B. Dorizzi, and A.
Germond, “Short term electrical load
forecasting with artificial neural
networks,” Engineering Intelligent Syst.,
vol. 2, pp. 85–99, 1996.
[14] A. Khotanzad, R. Afkhami-Rohani, and
D. Maratukulam , “ANNSTLF—
Artificial neural network short-term load
forecaster— Generation three,” IEEE
Trans. Power Systems, vol. 13, no. 4, pp.
1413–1422, 1998.
[15] H. Hippert, C. Pedreira, and R. Souza,
“Neural networks for short-term load
forecasting: A review and evaluation,”
IEEE Trans. Power Syst., vol. 16, no. 1,
pp. 44–55, Feb. 2001.
[16] M. Kazeminejad, M. Dehghan, M. B.
Motamadinejad, H. Rastegar, New Short
Term Load Forecasting Using Multilayer
Perceptron, IEEE International
Conference on Information and
Automation - Colombo, Sri Lanka ,
2006.12.1 .
[17] Ayca Kumluca Topalli ,Ismet Erkmen,
Ihsan To palli Intelligent , short-term load
forecasting in Turkey, Electrical Power
and Energy Systems 28 (2006) 437–447.
[18] Philippe Lauret , Eric Fock, Rija N.
Randrianarivony, Jean-Francois
Manicom-Ramsamy, Bayesian neural
network approach to short time load
forecasting, Energy Conversion and
Management 49 (2008) 1156–1166.
[19] Zhi Xiao, Shi-Jie Ye, Bo Zhong, Cai-Xin
Sun, BP neural network with rough set
for short term load forecasting, Expert
Systems with Applications 36 (2009)
273–279.
[20] Abbas Khosravi, Saeid Nahavandi and
Doug Creighton, Construction of Optimal
Prediction Intervals for Load Forecasting
Problems, IEEE TRANSACTIONS ON
POWER SYSTEMS, VOL. 25, NO. 3,
AUGUST 2010.
[21] Ali Deihimi, Hemen Showkati,
Application of echo state networks in
short-term electric load forecasting,
Energy 39 (2012) 327e340.
[22] Yongli Wang, Dongxiao Niu, Li Ji, Shortterm
power load forecasting based on
IVL-BP neural network technology, The
2nd International Conference on
Complexity Science & Information
Engineering, Systems Engineering
Procedia 4 (2012) 168 – 174.
[23] M. Lópeza, S. Valeroa, C. Senabrea, J.
Apariciob, A. Gabaldonc, Application of
SOM neural networks to short-term load
forecasting: The Spanish electricity
market case study Electric Power Systems
Research 91 (2012) 18– 27.
[24] Adiga S. Chandrashekaraa, T.
Ananthapadmanabhab, A.D. Kulkarnib, A
neuro-expert system for planning and
load forecasting of distribution systems,
Electrical Power and Energy Systems 21
(1999) 309–314.
[25] M. Ghiassi, David K. Zimbra, H. Saidane,
Medium term system load forecasting
with a dynamic artificial neural network
model, Electric Power Systems Research
76 (2006) 302–316.
[26] Pituk Bunnoona, Kusumal
Chalermyanonta, Chusak Limsakula ,
Mid-Term Load Forecasting: Level
Suitably of Wavelet and Neural Network
based on Factor Selection , International
Conference on Advances in Energy
Engineering, Energy procedia 14(2012),
438-444.
[27] Arash Ghanbari, S. FaridGhaderi, M. Ali
Azadeh, Adaptive Neuro-Fuzzy Inference
System vs. Regression Based Approaches
for Annual Electricity Load Forecasting,
IEEE 2nd International Conference on
Computer and Automation Engineering
(ICCAE 2010) – Singapore.
[28] Shahid M. Awan, Zubair. A. Khan, M.
Aslam, Waqar Mahmood, Affan Ahsan,
Application of NARX based FFNN, SVR
and ANN Fitting models for long term
industrial load forecasting and their
comparison, IEEE 21st International
Symposium on Industrial Electronics
(ISIE) - Hangzhou, China, 2012.
[29] Kwang-Ho Kim, Hyoung-Sun Youn and
Yong-Cheol Kang, Short-Term Load
Forecasting for Special Days in
Anomalous Load Conditions Using
Neural Networks and Fuzzy Inference
Method, IEEE TRANSACTIONS ON
POWER SYSTEMS, VOL. 15, NO. 2,
MAY 2000.
[30] V.S. Kodogiannis, E.M. Anagnostakis,
Soft computing based techniques for
short-term load forecasting, Fuzzy Sets
and Systems 128 (2002) 413–426.
[31] NimaAmjady, Farshid Keynia, Mid-term
load forecasting of power systems by a
new prediction method, Energy
Conversion and Management, 49 (2008)
2678–2687.
[32] Titti Saksornchai, Wei-Jen Lee, Kittipong
Methaprayoon, James R. Liao and
Richard J. Ross, Improve the Unit
Commitment Scheduling by Using the
Neural-Network-Based Short-Term Load
Forecasting, IEEE TRANSACTIONS
ON INDUSTRY APPLICATIONS, VOL.
41, NO. 1, JANUARY/FEBRUARY
2005.
[33] A. J.Wood and B. F.Wollenberg, Power
Generation Operation and Control. New
York: Wiley, 1996.
[34] G. B. Sheblé and G. N. Fahd, “Unit
commitment literature synopsis,” IEEE
Trans. Power Syst., vol. 9, no. 1, pp. 128–
135, Feb. 1994.
[35] S. Sen and D. P. Kothari, “Optimal
thermal generating unit commitment :A
review,” Elect. Power Energy Syst., vol.
20, no. 7, pp. 443–451, 1998.
[36] M.R. Amin-Naseri, A.R. Soroush,
Combined use of unsupervised and
supervised learning for daily peak load
forecasting, Energy Conversion and
Management 49 (2008) 1302–1308.
[37] Ramezani M, Falaghi H, Haghifam M,
Shahryari GA. Short-term electric load
forecasting using neural networks. In:
Proceedings of the EUROCON –
international conference on computer as a
tool; Belgrade, Serbia and Montenegro,
vol. 2; 2005. p. 1525–8.
[38] Fidalgo JN, Peças Lopes JA. Load
forecasting performance enhancement
when facing anomalous events. IEEE
Trans Power Syst 2005;20(1):408–15.
[39] Santos P, Martins A, Pires A. Designing
the input vector to ANN-based models for
short-term load forecast in electricity
distribution systems. Int J Electr Power
Energy Syst 2007;29(4):338–47.
[40] Chicco G, Napoli R, Piglione F. Load
pattern clustering for short-term load
forecasting of anomalous days. In:
Proceedings of the IEEE PowerTech
2001, Porto, Portugal, September 10–13;
2001. p. 2.
[41] Fidalgo J, Matos M. A. forecasting
portugal global load with artificial neural
networks. In: Proceedings of the
ICANN2007 – international congress on
artificial neural networks, Porto, Portugal,
September 9–13; 2007. p. 728–37.
[42] Lamedica R, Prudenzi A, Sforna M,
Caciotta M, Cencellli V. A neural
network based technique for short-term
forecasting of anomalous load periods.
IEEE Trans Power Syst
1996;11(4):1749–56.
[43] Danilo Bassi, Oscar Olivares, "Medium
Term Electric Load Forecasting Using
TLFN Neural Networks" International
Journal of Computers, Communications
& Control Vol. I (2006), No. 2, pp. 23-32.
[44] A. G. Bakirtzis, J.B. Theocharis, S.J.
Kiartzis, K.J. Satsios, Short term load
forecasting using fuzzy neural networks,
IEEE Trans, Power Syst, 10(3) 1518-
1524, 1995.
[45] A. D. Papalexopoulos et al., “An
Implementation of a Neural Network
Based Load Forecasting Model for the
EMS,” IEEE Trans. Power Systems. Vol.
9, No. 4, p. 1956-1962 (1994).
[46] T. Rashid et al., “A Practical Approach
for Electricity Load Forecasting,” World
Academy of Science, Engineering and
Technology (2005).
[47] A.S.Pandey et al., “Clustering based
formulation for Short Term Load
Forecasting” International Journal of
Intelligent Systems and Technologies 4:2
(2009).
[48] M. A. Farhat, “Long-term industrial load
forecasting and planning usingneural
networks technique and fuzzy inference
method,” in Proceedings of the 2004
IEEE Universities Power Engineering
Conference, pp. 368– 372, 2004.
[49] Domingo A. Gundin, Celiano Garcia,
Yannis A. Dimitriadis, Eduardo Garcia,
Guillermo Vega, Short-Term Load
Forecasting for Industrial Customers
Using FASART and FASBACK Neurofuzzy
Systems, Power Systems
Computation Conference (PSCC),
Seville, Spain, 2002.
[50] Santosh Kulkarni, Sishaj P Simon, A
New Spike Based Neural Network for
Short-Term Electrical Load Forecasting,
Fourth International Conference on
Computational Intelligence and
Communication Networks, 2012.
[51] K. Nose-Filho, A. D. P. Lotufo, and C. R.
Minussi, “Short-term multimodal load
forecasting in distribution systems using
general regression neural networks,”
presented at the IEEE Trondheim
PowerTech, Trondheim, Norway, Jun.
19–23, 2011.
[52] K. Nose-Filho, A. D. P. Lotufo, and C. R.
Minussi, “Preprocessing data for shortterm
load forecasting with a general
regression neural network and amoving
average filter,” presented at the IEEE
Trondheim PowerTech, Trondheim,
Norway, Jun. 19–23, 2011.
[53] Kenji Nose-Filho, Anna Diva Plasencia
Lotufo and Carlos Roberto Minussi ,
Short-Term Multinodal Load Forecasting
Using a Modified General Regression
Neural Network, IEEE
TRANSACTIONS ON POWER
DELIVERY, VOL. 26, NO. 4,
OCTOBER 2011,
[54] D. F. Specht, “A generalized regression
neural network,” IEEE Trans. Neural
Netw., vol. 2, no. 6, pp. 568–576, Nov.
1991.
[55] T. Kohonen, Self-organisation and
Associative Memory, 3rd edn., Springer-
Verlag, Berlin, 1989.
[56] M. Lópeza, S. Valeroa, C. Senabrea, J.
Apariciob, A. Gabaldonc, Application of
SOM neural networks to short-term load
forecasting: The Spanish electricity
market case study, Electric Power
Systems Research 91 (2012) 18– 27.