Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Forecasting the load of electrical power systems in mid- and long-term horizons: a review

Load forecasting has always been an important part in the planning and operation of electric utilities, i.e. both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This study aims at reviewing the different load forecasting techniques developed for the mid- and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid- and long-term horizons, this study reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. This study is concluded with discussion on future research directions.

References

    1. 1)
      • 30. Ghiassi, M.D., Zimbra, D.K., Saidane, H.: ‘Medium term system load forecasting with a dynamic artificial neural network model’, Electr. Power Syst. Res., 2006, 76, (5), pp. 302316.
    2. 2)
      • 47. Lee, W.J., Hong, J.: ‘A hybrid dynamic and fuzzy time series model for mid-term power load forecasting’, Electr. Power Energy Syst., 2015, 64, pp. 10571062.
    3. 3)
      • 45. You, S.H., Cheng, H.Z., Xie, H.: ‘Mid-and long-term load forecast based on fuzzy linear regression model’, Electr. Power Autom. Equip., 2006, 26, (3), pp. 5153.
    4. 4)
      • 64. Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona-Fernandez, D.: ‘Monthly electric energy demand forecasting based on trend extraction’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 19461953.
    5. 5)
      • 37. Abdel-Aal, R.E., Al-Garni, A.Z.: ‘Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis’, Energy, 1997, 22, (11), pp. 10591069.
    6. 6)
      • 44. Kang, J., Zhao, H.: ‘Application of improved grey model in long-term load forecasting of power engineering’, Syst. Eng. Proc., 2012, 3, pp. 8591.
    7. 7)
      • 70. Yalcinoz, T., Eminoglu, U.: ‘Short term and medium term power distribution load forecasting by neural networks’, Energy Convers. Manage., 2005, 46, (9), pp. 13931405.
    8. 8)
      • 34. Bart, A., Benahmed, M., Cherkaoui, R., et al: ‘Long-term energy management optimization according to different types of transactions’, IEEE Trans. Power Syst., 1998, 13, (3), pp. 804809.
    9. 9)
      • 103. Chen, T.: ‘A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan’, Comp. Ind. Eng., 2012, 63, (3), pp. 663670.
    10. 10)
      • 4. Ranaweera, D.K., Karady, G.G.: ‘Farmer RG. Economic impact analysis of load forecasting’, IEEE Trans. Power Syst., 1997, 12, (3), pp. 13881392.
    11. 11)
      • 36. Willis, H.L., Tram, H.N.: ‘Load forecasting for transmission planning’, IEEE Trans. Power App. Syst., 1984, PER-4, (3), pp. 561568.
    12. 12)
      • 88. Al-Hamadi, H.M.: ‘Long-term electric power load forecasting using fuzzy linear regression technique’. Proc. IEEE Power Engineering Automation Conf., 2011.
    13. 13)
      • 87. Tripathy, S.C.: ‘Demand forecasting in a power system’, Energy Convers. Manage., 1997, 38, (14), pp. 14751481.
    14. 14)
      • 48. Smola, A.J., Schölkopf, B.: ‘A tutorial on support vector regression’, Stat. Comput., 2004, 14, (3), pp. 199222.
    15. 15)
      • 54. Lee, D.G., Lee, B.W., Chang, S.H.: ‘Genetic programming model for long-term forecasting of electric power demand’, Electr. Power Syst. Res., 1997, 40, (1), pp. 1722.
    16. 16)
      • 29. Fu, C.W., Nguyen, T.T.: ‘Models for long-term energy forecasting’. Proc. IEEE Power Eng. Society General Meeting, 2003.
    17. 17)
      • 82. Pan, X., Lee, B.: ‘A comparison of support vector machines and artificial neural networks for mid-term load forecasting’. Proc. IEEE Int. Conf. Industrial Technology, 2012.
    18. 18)
      • 7. Wu, L., Shahidehpour, M., Li, T.: ‘Cost of reliability analysis based on stochastic unit commitment’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 13641374.
    19. 19)
      • 57. Mohamad, E.A., Mansour, M.M., El-Debeiky, S., et al: ‘Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface’, Electr. Power Syst. Res., 1996, 39, (3), pp. 171177.
    20. 20)
      • 46. Yue, L., Zhang, Y., Xie, H., et al: ‘The fuzzy logic clustering neural network approach for middle and long term load forecasting’. Proc. IEEE Grey Systems and Intelligence Services, 2007.
    21. 21)
      • 63. Oliveira, F.S., Ruiz, C., Conejo, A.J.: ‘Contract design and supply chain coordination in the electricity industry’, Eur. J. Operat. Res., 2013, 227, (3), pp. 527537.
    22. 22)
      • 6. Wu, L., Shahidehpour, M., Li, T.: ‘Stochastic security-constrained unit commitment’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 800811.
    23. 23)
      • 2. Willis, H.L., Northcote-Green, J.E.: ‘Spatial electric load forecasting: a tutorial review’, Proc. IEEE, 1983, 71, (2), pp. 232253.
    24. 24)
      • 13. Troccoli, A.: ‘Management of weather and climate risk in the energy industry’ (Springer, 2009).
    25. 25)
      • 96. Alsayegh, O., Almatar, O., Fairouz, F., et al: ‘Prediction of the long-term electric power demand under the influence of A/C systems’, Proc. Inst. Mech. Eng. A, J. Power Energy, 2007, 221, (1), pp. 6775.
    26. 26)
      • 10. Xia, C., Wang, J., McMenemy, K.: ‘Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks’, Electr. Power Energy Syst., 2010, 32, (7), pp. 743750.
    27. 27)
      • 78. Gavrilas, M., Ciutea, I., Tanasa, C.: ‘Medium-term load forecasting with artificial neural network models’. Proc. 16th Int. Conf. Exhibition Electricity Distribution, 2001.
    28. 28)
      • 106. Tan, Z., Zhang, J., Wu, L., et al: ‘A model integrating econometric approach with system dynamics for long-term load forecasting’, Power Syst. Tech., 2011, 1, pp. 186190.
    29. 29)
      • 12. Makridakis, S.: ‘Forecasting: methods and applications’, Makridakis, S., Wheelwright, S.C., Hyndman, R.J. (Eds.) (John Wiley & Sons, New York, 1998).
    30. 30)
      • 32. Islam, S.M., Al-Alawi, S.M., Ellithy, K.A.: ‘Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network’, Electr. Power Syst. Res., 1995, 34, (1), pp. 19.
    31. 31)
      • 80. Moreno-Chaparro, C., Salcedo-Lagos, J., Trujillo, E.R., et al: ‘A method for the monthly electricity demand forecasting in Colombia based on wavelet analysis and a nonlinear autoregressive model’, Ingeniería, 2011, 16, (2), pp. 94106.
    32. 32)
      • 21. Srinivasan, D., Lee, M.A.: ‘Survey of hybrid fuzzy neural approaches to electric load forecasting’. Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, 1995.
    33. 33)
      • 49. Hong, W.C., Dong, Y., Lai, C.Y., et al: ‘SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting’, Energies, 2011, 4, (6), pp. 960977.
    34. 34)
      • 43. Morita, H., Zhang, D.P., Tamura, Y.: ‘Long-term load forecasting using grey system theory’, Electr. Eng. Jpn., 1995, 115, (2), pp. 1120.
    35. 35)
      • 68. Chen, B.J., Chang, M.W., Lin, C.J.: ‘Load forecasting using support vector machines: A study on EUNITE competition 2001’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 18211830.
    36. 36)
      • 62. Khuntia, S.R., Tuinema, B.W., Rueda, J.L., et al: ‘Time-horizons in the planning and operation of transmission networks: an overview’, IET Gener. Transm. Distrib., 2016, 10, (4), pp. 841848.
    37. 37)
      • 52. Hong, W.C., Dong, Y., Zhang, W.Y., et al: ‘Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm’, Electr. Power Energy Syst., 2013, 44, (1), pp. 604614.
    38. 38)
      • 101. Jia, N.X., Yokoyama, R., Zhou, Y.C.: ‘A novel approach to long term load forecasting where functional relations and impact relations coexist’. Proc. IEEE PowerTech, 1999.
    39. 39)
      • 19. Gross, G., Galian, F.D.: ‘Short term load forecasting’, Proc. IEEE, 1987, 75, (12), pp. 15581573.
    40. 40)
      • 56. Karabulut, K., Alkan, A., Yilmaz, A.S.: ‘Long term energy consumption forecasting using genetic programming’, Math. Comput. Appl., 2008, 13, (2), pp. 7180.
    41. 41)
      • 90. Hamzaçebi, C.: ‘Forecasting of Turkey's net electricity energy consumption on sectoral bases’, Energy Policy, 2007, 35, (3), pp. 20092016.
    42. 42)
      • 85. Kermanshahi, B., Iwamiya, H.: ‘Up to year 2020 load forecasting using neural nets’, Electr. Power Energy Syst., 2002, 24, (9), pp. 789797.
    43. 43)
      • 40. Weron, R.: ‘Modeling and forecasting electricity loads and prices: a statistical approach’ (John Wiley and Sons, Chichester, 2006).
    44. 44)
      • 33. Kandil, M.S., El-Debeiky, S.M., Hasanien, N.E.: ‘Long-term load forecasting for fast developing utility using a knowledge-based expert system’, IEEE Trans. Power Syst., 2002, 17, (2), pp. 491496.
    45. 45)
      • 79. Tsekouras, G.J., Dialynas, E.N., Hatziargyriou, N.D., et al: ‘A non-linear multivariable regression model for midterm energy forecasting of power systems’, Electr. Power Syst. Res., 2007, 77, (12), pp. 15601568.
    46. 46)
      • 97. Carpinteiro, O.A., Leme, R.C., de Souza, A.C., et al: ‘Long-term load forecasting via a hierarchical neural model with time integrators’, Electr. Power Syst. Res., 2007, 77, (3), pp. 371378.
    47. 47)
      • 92. Ekonomou, L.: ‘Greek long-term energy consumption prediction using artificial neural networks’, Energy, 2010, 35, (2), pp. 512517.
    48. 48)
      • 41. Ju-Long, D.: ‘Control problems of grey systems’, Syst. Cont. Lett., 1982, 1, (5), pp. 288294.
    49. 49)
      • 42. Tamura, Y., Deping, Z., Umeda, N., et al: ‘Load forecasting using grey dynamic model’, J. Grey Syst., 1992, 4, (1), pp. 4958.
    50. 50)
      • 98. Maralloo, M.N., Koushki, A.R., Lucas, C., et al: ‘Long term electrical load forecasting via a neuro-fuzzy model’. Proc. IEEE Int. Computer Conf., 2009.
    51. 51)
      • 81. Vu, D.H., Muttaqi, K.M., Agalgaonkar, A.P.: ‘A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables’, Appl. Energy, 2015, 140, pp. 385394.
    52. 52)
      • 31. Barakat, E.H., Al-Rashed, S.A.: ‘Long range peak demand forecasting under conditions of high growth’, IEEE Trans. Power Syst., 1992, 7, (4), pp. 14831486.
    53. 53)
      • 76. Elkateb, M.M., Solaiman, K., Al-Turki, Y.: ‘A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques’, Neurocomputing, 1998, 23, (1), pp. 313.
    54. 54)
      • 67. Tsekouras, G.J., Hatziargyriou, N.D., Dialynas, E.N.: ‘An optimized adaptive neural network for annual midterm energy forecasting’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 385391.
    55. 55)
      • 72. Goude, Y., Nedellec, R., Kong, N.: ‘Local short and middle term electricity load forecasting with semi-parametric additive models’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 440446.
    56. 56)
      • 15. Sidorov, D.: ‘Integral dynamical models: singularities, signals and control’, World Scientific Series on Nonlinear Science Series A (World Scientific Publ., Singapore, 2015), vol. 87.
    57. 57)
      • 38. Barakat, E.H.: ‘Modeling of nonstationary time-series data. Part II. Dynamic periodic trends’, Electr. Power Energy Syst., 2001, 23, (1), pp. 6368.
    58. 58)
      • 77. Amjady, N., Keynia, F.: ‘Mid-term load forecasting of power systems by a new prediction method’, Energy Convers. Manage., 2008, 49, (10), pp. 26782687.
    59. 59)
      • 100. Kumaran, J., Ravi, G.: ‘Long-term sector-wise electrical energy forecasting using artificial neural network and biogeography-based optimization’, Electr. Power Compon. Syst., 2015, 43, (11), pp. 12251235.
    60. 60)
      • 14. Hritonenko, N., Yatsenko, Y.: ‘Energy substitutability and modernization of energy-consuming technologies’, Energy Econ., 2012, 34, (5), pp. 15481556.
    61. 61)
      • 94. Mamun, M.A., Nagasaka, K.: ‘Artificial neural networks applied to long-term electricity demand forecasting’. Proc. IEEE Int. Conf. on Hybrid Intelligent Systems, 2004.
    62. 62)
      • 51. Wang, J., Li, L., Niu, D., et al: ‘An annual load forecasting model based on support vector regression with differential evolution algorithm’, Appl. Energy, 2012, 94, pp. 6570.
    63. 63)
      • 20. Moghram, I.S., Rahman, S.: ‘Analysis and evaluation of five short-term load forecasting techniques’, IEEE Trans. Power Syst., 1989, 4, (4), pp. 14841491.
    64. 64)
      • 93. Nagasaka, K., Al Mamun, M.: ‘Long-term peak demand prediction of 9 Japanese power utilities using radial basis function networks’. Proc. IEEE Power Engineering Society General Meeting, 2004.
    65. 65)
      • 58. Chandrashekara, A.S., Ananthapadmanabha, T., Kulkarni, A.D.: ‘A neuro-expert system for planning and load forecasting of distribution systems’, Electr. Power Energy Syst., 1999, 21, (5), pp. 309314.
    66. 66)
      • 23. Alfares, H.K., Nazeeruddin, M.: ‘Electric load forecasting: literature survey and classification of methods’, Int. J. Syst. Sci., 2002, 33, pp. 2334.
    67. 67)
      • 18. Abu El-Magd, M.A., Sinha, N.K.: ‘Short-term load demand modeling and forecasting’, IEEE Trans. Syst. Man Cyber., 1982, 12, (3), pp. 370382.
    68. 68)
      • 60. Li, H.Z., Guo, S., Li, C.J., et al: ‘A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm’, Knowl.-Based Syst., 2013, 37, pp. 378387.
    69. 69)
      • 5. Billinton, R., Huang, D.: ‘Effects of load forecast uncertainty on bulk electric system reliability evaluation’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 418425.
    70. 70)
      • 75. De Felice, M., Alessandri, A., Catalano, F.: ‘Seasonal climate forecasts for medium-term electricity demand forecasting’, Appl. Energy, 2015, 137, pp. 435444.
    71. 71)
      • 102. Jia, N.X., Yokoyama, R., Zhou, Y.C., et al: ‘A flexible long-term load forecasting approach based on new dynamic simulation theory – GSIM’, Electr. Power Energy Syst., 2001, 23, (7), pp. 549556.
    72. 72)
      • 84. Jaramillo-Morán, M.A., González-Romera, E., Carmona-Fernández, D.: ‘Monthly electric demand forecasting with neural filters’, Electr. Power Energy Syst., 2013, 49, pp. 253263.
    73. 73)
      • 26. Robinson, P.: ‘Modeling utility load and temperature relationships for use with long-lead forecasts’, J. Appl. Meteorol., 1997, 36, pp. 591598.
    74. 74)
      • 25. Ghods, L., Kalantar, M.: ‘Different methods of long-term electric load demand forecasting; a comprehensive review’, Iran. J. Electr. Electron. Eng., 2011, 7, (4), pp. 249259.
    75. 75)
      • 1. Heinemann, G., Nordman, D., Plant, E.: ‘The relationship between summer weather and summer loads: a regression analysis’, IEEE Trans. Power Appl. Syst., 1966, PAS-85, (11), pp. 11441154.
    76. 76)
      • 50. Zhang, Z., Ye, S.: ‘Long term load forecasting and recommendations for china based on support vector regression’. Proc. IEEE Information Management, Innovation Management and Industrial Engineering, 2011.
    77. 77)
      • 59. Kandil, M.S., El-Debeiky, S.M., Hasanien, N.E.: ‘The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II’, Electr. Power Syst. Res., 2001, 58, (1), pp. 1925.
    78. 78)
      • 91. Kermanshahi, B.: ‘Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities’, Neurocomputing, 1998, 23, (1), pp. 125133.
    79. 79)
      • 69. Doveh, E., Feigin, P., Greig, D., et al: ‘Experience with FNN models for medium term power demand predictions’, IEEE Trans. Power Syst., 1999, 14, (2), pp. 538546.
    80. 80)
      • 71. Mirasgedis, S., Sarafidis, Y., Georgopoulou, E., et al: ‘Models for mid-term electricity demand forecasting incorporating weather influences’, Energy, 2006, 31, (2), pp. 208227.
    81. 81)
      • 95. Dalvand, M.M., Azami, S.B.Z., Tarimoradi, H.: ‘Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks’. Proc. IEEE Universities Power Engineering Conf., 2008.
    82. 82)
      • 66. GARPUR consortium: ‘How to upgrade reliability management for asset management decision making’, 2016 [http://www.garpur-project.eu/deliverables].
    83. 83)
      • 104. Chen, T., Wang, Y.C.: ‘Long-term load forecasting by a collaborative fuzzy-neural approach’, Electr. Power Energy Syst., 2012, 43, (1), pp. 454464.
    84. 84)
      • 105. Hyndman, R.J., Fan, S.: ‘Density forecasting for long-term peak electricity demand’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 11421153.
    85. 85)
      • 61. AlRashidi, M.R., El-Naggar, K.M.: ‘Long term electric load forecasting based on particle swarm optimization’, Appl. Energy, 2010, 87, (1), pp. 320326.
    86. 86)
      • 39. Filik, Ü.B., Gerek, Ö.N., Kurban, M.: ‘A novel modeling approach for hourly forecasting of long-term electric energy demand’, Energy Convers. Manage., 2011, 52, (1), pp. 199211.
    87. 87)
      • 65. Amjady, N., Farrokhzad, D., Modarres, M.: ‘Optimal reliable operation of hydrothermal power systems with random unit outages’, IEEE Trans. Power Syst., 2003, 18, (1), pp. 279287.
    88. 88)
      • 28. Vlahović, V.M., Vujošević, I.M.: ‘Long-term forecasting: a critical review of direct-trend extrapolation methods’, Electr. Power Energy Syst., 1987, 9, (1), pp. 28.
    89. 89)
      • 27. Feinberg, E.A., Genethliou, D.: ‘Load forecasting. In applied mathematics for power systems: Optimization, control, and computational intelligence’, Chow, J.H., Wu, F.F., Momoh, J.A. (Eds.) (Springer, New York, 2005), pp. 269285.
    90. 90)
      • 8. Box, G.E., Jenkins, G.M., Reinsel, G.C.: ‘Time series analysis: forecasting and control’ (Wiley, Hoboken, 2008).
    91. 91)
      • 9. Chatfield, C.: ‘Time-series forecasting’ (Chapman & Hall, New York, 2001).
    92. 92)
      • 17. Matthewman, P.D., Nicholson, H.: ‘Techniques for load prediction in electricity supply industry’, Proc. IEEE, 1968, 115, pp. 14511457.
    93. 93)
      • 86. Hong, T., Wilson, J., Xie, J.: ‘Long term probabilistic load forecasting and normalization with hourly information’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 456462.
    94. 94)
      • 53. Hu, Z., Bao, Y., Chiong, R., et al: ‘Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection’, Energy, 2015, 84, pp. 419431.
    95. 95)
      • 16. Hong, T.: ‘Energy forecasting: past, present, and future’, Foresight: Int. J. Appl. Forecast., 2014, 32, pp. 4348.
    96. 96)
      • 24. Bunnoon, P., Chalermyanont, K., Limsakul, C.: ‘A computing model of artificial intelligent approaches to mid-term load forecasting: a state-of-the-art survey for the researcher’, Int. J. Eng. Techn., 2010, 2, (1), pp. 94100.
    97. 97)
      • 107. Sanstad, A.H., McMenamin, S., Sukenik, A., et al: ‘Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning’, Appl. Energy, 2014, 128, pp. 265276.
    98. 98)
      • 74. OrtizBeviá, M.J., RuizdeElvira, A., Alvarez-García, F.J.: ‘The influence of meteorological variability on the mid-term evolution of the electricity load’, Energy, 2014, 76, pp. 850856.
    99. 99)
      • 55. De Aquino, R.R., Neto, O.N., Lira, M., et al: ‘Development of an artificial neural network by genetic algorithm to mid-term load forecasting’. Proc. IEEE Int. Joint Conf. Neural Networks, 2007.
    100. 100)
      • 3. Willis, H.L.: ‘Load forecasting for distribution planning-error and impact on design’, IEEE Trans. Power Appl. Syst., 1983, PAS-102, (3), pp. 675686.
    101. 101)
      • 73. Apadula, F., Bassini, A., Elli, A., et al: ‘Relationships between meteorological variables and monthly electricity demand’, Appl. Energy, 2012, 98, pp. 346356.
    102. 102)
      • 22. Hippert, H.S., Pedreira, C.E., Souza, R.C.: ‘Neural networks for short-term load forecasting: a review and evaluation’, IEEE Trans. Power Syst., 2001, 16, (1), pp. 4455.
    103. 103)
      • 11. Leahy, P.G., Foley, A.M.: ‘Wind generation output during cold weather-driven electricity demand peaks in Ireland’, Energy, 2012, 39, (1), pp. 4853.
    104. 104)
      • 89. Niu, L., Zhao, J., Liu, M.: ‘Application of relevance vector regression model based on sparse Bayesian learning to long-term electricity demand forecasting’. Proc. IEEE Int. Conf. Mechatronics Automation, 2009.
    105. 105)
      • 83. Abdel-Aal, R.E.: ‘Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks’, Comp. Ind. Eng., 2008, 54, (4), pp. 903917.
    106. 106)
      • 35. Al-Hamadi, H.M., Soliman, S.A.: ‘Long-term/mid-term electric load forecasting based on short-term correlation and annual growth’, Electr. Power Syst. Res., 2005, 74, (3), pp. 353361.
    107. 107)
      • 99. Çunkaş, M., Altun, A.A.: ‘Long term electricity demand forecasting in Turkey using artificial neural networks’, Energy Sources B, Econ. Planning Policy, 2010, 5, (3), pp. 279289.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.0340
Loading

Related content

content/journals/10.1049/iet-gtd.2016.0340
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address