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Erschienen in:
Generalized Fuzzy Measurability Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

2. Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks

verfasst von : G. J. Tsekouras, F. D. Kanellos, N. Mastorakis

Erschienen in: Computational Problems in Science and Engineering

Verlag: Springer International Publishing

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Abstract

The demand in electric power should be predicted with the highest possible accuracy as it affects decisively many of power system’s operations. Conventional methods for load forecasting were built on several assumptions, while they had to cope with relations between the data used that could not be described analytically. Artificial Neural Networks (ANNs) gave answers to many of the above problems and they became the predominant load forecasting technique. In this chapter the reader is first introduced to Artificial Neural Networks and their usage in forecasting the load demand of electric power systems. Several of the major training techniques are described with their pros and cons being discussed. Finally, feed- forward ANNs are used for the short-term forecasting of the Greek Power System load demand. Various ANNs with different inputs, outputs, numbers of hidden neurons etc. are examined, techniques for their optimization are proposed and the obtained results are discussed.

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Vorheriges Kapitel Generalized Fuzzy Measurability
Nächstes Kapitel Analysis of Non-linear Vibrations of a Fractionally Damped Cylindrical Shell Under the Conditions of Combinational Internal Resonance
Literatur
1.
Hobbs, B.F., Jitprapaikulsarn, S., Maratukulam, D.J.: Analysis of the value for unit commitment of improved load forecasts. IEEE Trans. Power Syst. 14(4), 1342–48 (1999)CrossRef
2.
Gooi, H.B., Mendes, D.P., Bell, K.R.W., Kirschen, D.S.: Optimal scheduling of spinning reserve. IEEE Trans. Power Syst. 14(4), 1485–1492 (1999)CrossRef
3.
Gravener, M.H., Nwankpa, C.: Available transfer capability and first order sensitivity. IEEE Trans. Power Syst. 14(2), 512–518 (1999)CrossRef
4.
Fan, J.Y., McDonald, J.D.: A real-time implementation of short-term load forecasting for distribution power systems. IEEE Trans. Power Syst. 9(2), 988–994 (1994)CrossRef
5.
Haidda, T., Muto, S.: Regression based peak load forecasting using a transformation technique. IEEE Trans. Power Syst. 9(4), 1788–1794 (1994)CrossRef
6.
Rupanagunta, P., Baughman, M.L., Jones, J.W.: Scheduling of cool storage using non-linear programming techniques. IEEE Trans. Power Syst. 10(3), 1279–1285 (1995)CrossRef
7.
Kassaei, H.R., Keyhani, A., Woung, T., Rahman, M.: A hybrid fuzzy neural network bus load modeling and prediction. IEEE Trans. Power Syst. 14(2), 718–724 (1999)CrossRef
8.
Yang, H.T., Huang, C.M., Huang, C.L.: Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Trans. Power Syst. 11(1), 403–408 (1996)CrossRef
9.
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)CrossRef
10.
Mastorocostas, P.A., Theocharis, J.B., Bakirtzis, A.G.: Fuzzy modeling for short-term load forecasting using the orthogonal least squares method. IEEE Trans. Power Syst. 14(1), 29–36 (1999)CrossRef
11.
Infield, D.G., Hill, D.C.: Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Trans. Power Syst. 13(3), 1115–1120 (1998)CrossRef
12.
Charytoniuk, W., Chen, M.S., Van Olinda, P.: Nonparametric regression based short-term load forecasting. IEEE Trans. Power Syst. 13(3), 725–730 (1998)CrossRef
13.
Khotanzad, A., Hwang, R.C., Abaye, A., Maratukulam, D.: An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities. IEEE Trans. Power Syst. 10(3), 1716–1722 (1995)CrossRef
14.
Khotanzad, A., Afkhami-Rohani, R., Lu, T.L., Abaye, A., Davis, M., Maratukulam, D.: ANNSTLF- neural network-based electric load forecasting system. IEEE Trans. Neural Netw. 8(4), 835–846 (1996)CrossRef
15.
Khotanzad, A., Afkhami-Rohani, R., Maratukulam, D.: Artificial neural network short-term load forecaster-generation three. IEEE Trans. Power Syst. 13(4), 1413–1422 (1998)CrossRef
16.
Papalexopoulos, A.D., Hao, S., Peng, T.M.: An implementation of a neural network based load fore-casting model for the EMS. IEEE Trans. Power Syst. 9(4), 1956–62 (1994)CrossRef
17.
Mohammed, O., Park, D., Merchant, R., Dinh, T., Tong, C., Azeem, A.: Practical experiences with an adaptive neural network short-term load forecasting system. IEEE Trans. Power Syst. 10(1), 254–265 (1995)CrossRef
18.
Wang, X., Shi, M.: Matlab for forecasting of electric power load based on BP neural network. Proceedings ICICIS 2011, Part I, Series: Communications in Computer and Information Science 134: 636-642 (2011)
19.
Barghinia, S., Ansarimehr, P., Habibi, H., Vafadar, N.: Short-term load forecasting of Iran national power system using artificial neutral network, p. 5. ΙΕΕΕ Porto Power Tech Conference, Porto, Portugal (2001)
20.
Kalaitzakis, K., Stavrakakis, G.S., Anagnostakis, E.M.: Short-term load forecasting based on artificial neural networks parallel implementation. Electr. Power Syst. Res. 63, 185–196 (2002)CrossRef
21.
Saini, L.M., Soni, M.K.: Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Trans. Power Syst. 17(3), 907–912 (2002)CrossRef
22.
Gontar, Z., Hatziargyriou, N.: Short term load forecasting with radial basis function network. 2001 ΙΕΕΕ Porto Power Tech Conference 10th-13th September, p. 4. Porto, Portugal (2001)
23.
Yun, Z., Quan, Z., Caixin, S., Shaolan, L., Yuming, L., Yang, S.: RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst. 23(3), 853–858 (2008)CrossRef
24.
Ko, C.N., Lee, C.M.: Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49, 413–422 (2013)CrossRef
25.
Vermaak, J., Botha, E.C.: Recurrent neural networks for short-term load forecasting. IEEE Trans. Power Syst. 13(1), 126–132 (1998)CrossRef
26.
Chen, S.T., Yu, D.C., Moghaddamjo, A.R.: Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE Trans. Power Syst. 7(3), 1098–1105 (1992)CrossRef
27.
Abdel-Aal, R.E.: Short-term hourly load forecasting using abductive networks. IEEE Trans. Power Syst. 19(1), 164–173 (2004)CrossRef
28.
Ranaweera, D.K., Karady, G.G., Farmer, R.G.: Effect of probabilistic inputs on neural network-based electric load forecasting. IEEE Trans. Neural Netw. 7(6), 1528–1532 (1996)CrossRef
29.
Taylor, J.W., Buizza, R.: Neural network load forecasting with weather ensemble predictions. IEEE Trans. Power Syst. 17(3), 626–632 (2002)CrossRef
30.
Senjyu, T., Takara, H., Uezato, K., Funabashi, T.: One-hour-ahead load forecasting using neural network. IEEE Trans. Power Syst. 17(1), 113–118 (2002)CrossRef
31.
Kim, K.H., Park, J.K., Hwang, K.J., Kim, S.H.: Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Trans. Power Syst. 10(3), 1534–1539 (1995)MathSciNetCrossRef
32.
Kim, K.H., Youn, H.S., Kang, Y.C.: Short-term load forecasting for special days anomalous load conditions using neural networks and fuzzy inference method. IEEE Trans. Power Syst. 15(2), 559–565 (2000)CrossRef
33.
Srinivasan, D., Tan, S.S., Chang, C.S., Chan, E.K.: Parallel neural network-Fuzzy expert system strategy for short-term load forecasting: system implementation and performance evaluation. IEEE Trans. Power Syst. 14(3), 1100–1106 (1999)CrossRef
34.
Daneshdoost, M., Lotfalian, M., Bumroonggit, G., Ngoy, J.P.: Neural network with fuzzy set-based classification for short-term load forecasting. IEEE Trans. Power Syst. 13(4), 1386–1391 (1998)CrossRef
35.
Banik, S., Anwer, M., Khodadad Khan, A.F.M.: Predictive Power of the Daily Bangladeshi Exchange Rate Series based on Markov Model, Neuro Fuzzy Model and Conditional Heteroskedastic Model. Proceedings of 12th International Conference on Computer and Information Technology (ICCIT 2009), Dhaka, Bangladesh (2009)
36.
Tanabe, T., Ueda, Y., Suzuki, S., Ito, T., Sasaki, N., Tanaka, T., Funabashi, T., Yokoyama, R.: Optimized operation and stabilization of microgrids with multiple energy resources. 7th International conference on Power Electronics, EXCO, Daegu, Korea (2007)
37.
Jaipradidtham, C.: Next day load demand forecasting of future in electrical power generation on distribution networks using Adaptive Neuro-Fuzzy Inference. 1st International Power and Energy Conference PECon, Putrajaya, Malaysia (2006)
38.
Malkocevic, D., Konjic, T., Miranda, V.: Preliminary comparison of different neural-fuzzy mappers for load curve short term prediction. 8th Seminar on Neural Network Applications in Electrical Engineering, NEUREL-2006, Faculty of Electrical Engineering, University of Belgrade, Serbia (2006)
39.
Wang, X., Hatziargyriou, N., Tsoukalas, L.H.: A new methodology for nodal load forecasting in deregulated power systems. IEEE Power Eng. Rev. 12, 48–51 (2002)CrossRef
40.
Chen, Y., Luh, P.B., Guan, C., Zhao, Y., Michel, L.D., Coolbeth, M.A., Friedland, P.B., Rourke, S.J.: Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans. Power Syst. 25(1), 322–330 (2010)CrossRef
41.
Ferreira, V.H., Alves da Silva, A.P.: Toward estimating autonomous neural network-based electric load forecasters. IEEE Trans. Power Syst. 22(4), 1554–1562 (2007)CrossRef
42.
Cardenas, J.J., Romeral, L., Garcia, A., Andrade, F.: Load forecasting framework of electricity consumptions for an intelligent energy management system in the user-side. Expert Syst. Appl. 39, 5557–5565 (2012)CrossRef
43.
Amjady, N., Keynia, F.: A new neural network approach to short term load forecasting of electrical power systems. Energies 4, 488–503 (2011)CrossRef
44.
Bakirtzis, A.G., Petridis, V., Kiartzis, S.J., Alexiadis, M.C., Maissis, A.H.: A neural network short term load forecasting model for the Greek power system. IEEE Trans. Power Syst. 11(2), 858–863 (1996)CrossRef
45.
Kiartzis, S.J., Zournas, C.E., Theocharis, J.M., Bakirtzis, A.G., Petridis, V.: Short term load forecasting in an autonomous power system using artificial neural networks. IEEE Trans. Power Syst. 12(4), 1591–1596 (1997)CrossRef
46.
Tsekouras, G.J., Kanellos, F.D., Kontargyri, V.T., Tsirekis, C.D., Karanasiou, I.S., Elias, C.N., Salis, A.D., Mastorakis, N.E.: A comparison of artificial neural networks algorithms for short term load forecasting in Greek intercontinental power system. WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, Puerto De La Cruz, Canary Islands, Spain (2008)
47.
Tsekouras, G.J., Kanellos, F.D., Kontargyri, V.T., Tsirekis, C.D., Karanasiou, I.S., Elias, C.N., Salis, A.D., Kontaxis, P.A., Mastorakis, N.E.: Short term load forecasting in Greek Intercontinental Power System using ANNs: a Study for Input Variables. 10th WSEAS International Conference on Neural NetworksPrague, Czech Republic (2009)
48.
Tsekouras, G.J., Kanellos, F.D., Elias, C.N., Kontargyri, V.T., Tsirekis, C.D., Karanasiou, I.S., Salis, A.D., Kontaxis, P.A., Gialketsi, A.A., Mastorakis, N.E.: Short term load forecasting in Greek interconnected power system using ANN: A study for output variables. 15th WSEAS International Conference on Systems Corfu, Greece (2011)
49.
Choueiki, M.H., Mount-Campbell, C.A., Ahalt, S.C.: Implementing a weighted least squares procedure in training a neural network to solve the short-term load forecasting problem. IEEE Trans. Power Syst. 12, 41689–1694 (1997)
50.
Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J.: Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Convers. Manag. 44, 1525–1534 (2003)CrossRef
51.
Kyriakides, E., Polycarpou, M.: Short term electric load forecasting: a tutorial (Chapter 16). In: Chen, K., Wang, L. (eds.), Trends in neural computation, studies in computational intelligence, 35 pp. 391–418. Springer (2007)
52.
Hahn, H., Meyer-Nieberg, S., Pickl, S.: Electric load forecasting methods: tools for decision making. Eur. J. Oper. Res. 199, 902–907 (2009)MATHCrossRef
53.
Felice, M., Yao, X.: Short-term load forecasting with neural network ensembles: a comparative study. IEEE Comput. Intell. Mag. 6(3), 47–56 (2011)CrossRef
54.
Rego, L.P., Santana, A.L., Conde, G., Silva, M.S., Francês, C.R.L., Rocha, C.A.: Comparative analyses of computational intelligence models for load forecasting: a case study in the Brazilian Amazon power suppliers. Lecture Notes Comput. Sci. 5553, 1044–1053 (2009)CrossRef
55.
Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall (1994)
56.
Likas, A.: Computational Intelligence (in Greek), 1st edn. Ioannina, Greece (1999)
57.
Ghosh, P.S., Chakravorti, S., Chatterjee, N.: Estimation of time-to-flashover characteristics of contaminated electrolytic surfaces using a neural network. IEEE Trans Dielectr. Electr. Insul. 2(6), 1064–1074 (1995)CrossRef
58.
59.
Polak, E.: Computational methods in optimization: a unified approach, 1st edn. Academic Publication, New York (1971)
60.
Powell, M.J.: Restart procedures for the conjugate gradient method. Math. Program. 12, 241–254 (1977)MATHCrossRef
61.
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993)CrossRef
62.
Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math. 2, 164–168 (1944)MATHMathSciNet
63.
64.
Silva, A.P.A., Moulin, L.S.: Confidence intervals for neural network based short-term load forecasting. IEEE Trans. Power Syst. 15(4), 1191–1196 (2000)CrossRef
65.
Mastorakis, N.E., Tsekouras, G.J.: Short term load forecasting in Greek intercontinental power system using ANN: the confidence interval. Advanced aspects of theoretical electrical engineering. Sozopol, Bulgaria (2010)
66.
Tsekouras, G.J., Mastorakis, N.E., Kanellos, F.D., Kontargyri, V.T., Tsirekis, C.D., Karanasiou, I.S., Elias, C.N., Salis, A.D., Kontaxis, P.A., Gialketsi, A.A.: Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor. WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, Vouliagmeni, Athens, Greece (2010)
67.
Khosravi, A., Nahavandi, S., Creighton, D.: Construction of optimal prediction intervals for load forecasting problems. IEEE Trans. Power Syst. 25(3), 1496–1503 (2010)CrossRef
68.
Quan, H., Srinivasan, D., Khosravi, A.: Uncertainty handling using neural network-based prediction intervals for electrical load forecasting. Energy 73, 916–925 (2014)CrossRef
69.
Hand, D., Manilla, H., Smyth, P.: Principles of data mining, 1st edn. The MIT Press, Cambridge (2001)
70.
Fidalgo, J.N., Peças Lopes, J.A.: Load forecasting performance enhancement when facing anomalous events. IEEE Trans. Power Syst. 20(1), 408–415 (2005)CrossRef
Metadaten
Titel
Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks
verfasst von
G. J. Tsekouras
F. D. Kanellos
N. Mastorakis
Copyright-Jahr
2015
Buch
Computational Problems in Science and Engineering

Print ISBN: 978-3-319-15764-1

Electronic ISBN: 978-3-319-15765-8

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-15765-8_2

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