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Methods and Models for Electric Load Forecasting: A Comprehensive Review Cover

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Open Access
|Feb 2020

References

  1. [1] Y. Lin, H. Luo, D. Wang, H. Guo, and K. Zhu, “An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting,” Energies, vol. 10, no. 1186, 2017.10.3390/en10081186
  2. [2] M. D. Reddy, “Load Forecasting using Linear Regression Analysis in Time series model for RGUKT, R.K. Valley Campus HT Feeder,” International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 5, 2017.10.17577/IJERTV6IS050443
  3. [3] G. Nalcaci, A. Özmen, and G. W. Weber, “Long-term Load Forecasting: Models Based on MARS, ANN and LR methods,” Central European Journal of Operations Research (CEJOR), Springer-Verlag GmbH Germany, vol. 27, no. 2019, pp. 1033–1049, 2018.10.1007/s10100-018-0531-1
  4. [4] E. Almeshaiei and H. Soltan, “A Methodology for Electric Power Load Forecasting,” Alexandria Engineering Journal, vol. 50, no. 2011, pp. 137–144, 2011.10.1016/j.aej.2011.01.015
  5. [5] J. Zhang, “Research on Power Load Forecasting Based on the Improved Elman Neural Network,” The Italian Association of Chemical Engineering (AIDIC), vol. 51, no. 2016, pp. 589-594, 2016.10.1155/2016/7910971
  6. [6] M. Y. Khamaira, A. S. Krzma, and A. M. Alnass, “Long Term Peak Load Forecasting for the Libyan Network,” in Conference for Engineering Sciences and Technology (CEST), 2018, vol. 1, pp. 185-193: AIJR Publisher.10.21467/proceedings.2.23
  7. [7] O. Demirel, A. Kakilli, and M. Tektas, “Electric Energy Load Forecasting Using ANFIS and ARMA Methods,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 25, no. 3, pp. 601-610, 2010.
  8. [8] Q. Wang, Y. Wang, and L. Zhang, “Research on Post-Evaluation Model and Method of Electric Powe Demand Forecasting,” presented at the Chinese Control and Decision Conference (CCDC), China, 2008.
  9. [9] X. Zhanga, J. Wanga, and K. Zhang, “Short-Term Electric Load Forecasting Based on Singular Spectrum Analysis and Support Vector Machine Optimized by Cuckoo Search Algorithm,” Electric Power Systems Research, vol. 146, no. 2017, pp. 270–285, 2017.10.1016/j.epsr.2017.01.035
  10. [10] Ü. B. Filik, Ö. N. Gerek, and M. Kurban, “A Novel Modeling Approach for Hourly Forecasting of Long-Term Electric Energy Demand,” Energy Conversion and Management, vol. 52, no. 2011, pp. 199–211, 2011.10.1016/j.enconman.2010.06.059
  11. [11] R. Gordillo-Orquera, L. M. Lopez-Ramos, S. Muñoz-Romero, P. Iglesias-Casarrubios, D. Arcos-Avilés, A. G. Marques, and J. L. Rojo-Álvarez, “Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings,” Energies, vol. 11, no. 493, 2018.10.3390/en11030493
  12. [12] L. Friedrich and A. Afshari, “Short-Term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables,” presented at the 7th International Conference on Applied Energy (ICAE), 2015.10.1016/j.egypro.2015.07.616
  13. [13] N. Abu-Shikhah and F. Elkarmi, “Medium-Term Electric Load Forecasting Using Singular Value Decomposition,” Energy Conversion and Management, vol. 36, no. 7, pp. 4259-4271, 2011.10.1016/j.energy.2011.04.017
  14. [14] R. Wanga, J. Wangb, and Y. Xu, “A Novel Combined Model Based on Bybrid Optimization Algorithm for Electrical Load Forecasting,” Applied Soft Computing Journal, vol. 82, no. 2019, p. 105548, 2019.10.1016/j.asoc.2019.105548
  15. [15] C. Kuster, Y. Rezgui, and M. Mourshed, “Electrical Load Forecasting Models: A Critical Systematic Review,” in Sustainable Cities and Society, ed, 2017.10.1016/j.scs.2017.08.009
  16. [16] H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: Literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23–34, 2002.10.1080/00207720110067421
  17. [17] C. Kuster, Y. Rezgui, and M. Mourshed, “Electrical load forecasting models: A critical systematic review,” Sustainable Cities and Society, vol. 35, pp. 257–270, 2017, Art. no. Pii: s2210670717305899.10.1016/j.scs.2017.08.009
  18. [18] A. K. Singh, Ibraheem, S. Khatoon, M. Muazzam, and D. K. Chaturvedi, “Load forecasting techniques and methodologies: A review,” in 2012 2nd International Conference on Power, Control and Embedded Systems, 2012, pp. 1–10.10.1109/ICPCES.2012.6508132
  19. [19] S. Fallah, M. Ganjkhani, S. Shamshirband, and K.-w. Chau, “Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview,” Energies, vol. 12, no. 3, p. 393, 2019, Art. no. PII: en12030393.10.3390/en12030393
  20. [20] W. C. Hong, M. W. Li, and G. F. Fan, Short-Term Load Forecasting by Artificial Intelligent Technologies. MDPI AG, 2019.
  21. [21] I. A. b. W. A. Razak, S. b. Majid, M. S. b. M. Aras, and A. b. Ahmad, “Electricity Load Forecasting Using Data Mining Technique,” ed: IntechOpen, 2012.
  22. [22] F. Su, Y. Xu, and X. Tang, “Short-and mid-term load forecasting using machine learning models,” in 2017 China International Electrical and Energy Conference (CIEEC), 2017, pp. 406–411.10.1109/CIEEC.2017.8388482
  23. [23] S. HemaChandra, V. Harish, C. R. Kumar, and V. Nagarjuna, “A Review of Long Term Electrical Load Forecasting Methods,” (in English), Artificial Intelligent Systems and Machine Learning, vol. 4, no. 10, pp. 566–569, 2012.
  24. [24] S. K. Panda, S. N. Mohanty, and A. K. Jagadev, “Long Term Electrical Load Forecasting: An Empirical Study across Techniques and Domains,” Indian Journal of Science and Technology, vol. 10, no. 26, pp. 1–16, 2017.10.17485/ijst/2017/v10i26/115372
  25. [25] M. Jacob, C. Neves, and D. Vukadinović Greetham, “Short Term Load Forecasting,” in FORECASTING AND ASSESSING RISK OF INDIVIDUAL ELECTRICITY PEAKS, vol. 33, M. N. C. V. G. D. Jacob, Ed. (Mathematics of Planet Earth, [S.l.]: SPRINGER NATURE, 2019, pp. 15–37.10.1007/978-3-030-28669-9_2
  26. [26] R. Weron, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. Wiley, 2007.10.1002/9781118673362
  27. [27] B. Yildiz, J. I. Bilbao, and A. B. Sproul, “A review and analysis of regression and machine learning models on commercial building electricity load forecasting,” Renewable and Sustainable Energy Reviews, vol. 73, pp. 1104–1122, 2017, Art. no. Pii: s1364032117302265.10.1016/j.rser.2017.02.023
  28. [28] E. A. Feinberg and D. Genethliou, “Load Forecasting,” in Applied Mathematics for Restructured Electric Power Systems.Optimization, Control, and Computational Intelligence, J. H. Chow, F. F. Wu, and J. A. Momoh, Eds. Dordrecht: Springer-Verlag New York Inc, 2006, pp. 269-285.10.1007/0-387-23471-3_12
  29. [29] S. A.-h. Soliman and A. M. Al-Kandari, Electrical Load Forecasting: Modeling and Model Construction, 1st ed. Butterworth–Heineman, 2010.10.1016/B978-0-12-381543-9.00001-4
  30. [30] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. OTexts: Melbourne, Australia, 2018.10.32614/CRAN.package.fpp2
  31. [31] R.M.Dawes, “Clinical versus Actuarial Prediction,” International Encyclopedia of the Social & Behavioral Sciences, pp. 2048-2051, 2001. Elsevier Ltd.10.1016/B0-08-043076-7/01296-1
  32. [32] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: Forecasting and control / George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, 4th ed. ed. (Wiley series in probability and statistics). Oxford: Wiley, 2008.
  33. [33] P. S. P. Cowpertwait and A. V. Metcalfe, Introductory Time Series with R. Springer New York, 2009.10.1007/978-0-387-88698-5_1
  34. [34] A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, Power Generation, Operation, and Control. Wiley, 2013.
  35. [35] Casals, Jose, Garcia-Hiernaux, Alfredo, Jerez, Miguel, Sotoca, Sonia, Trindade, and A. Alexandre, “State-Space Methods for Time Series Analysis: Theory, Applications and Software.”
  36. [36] J. J. F. Commandeur and S. J. Koopman, An introduction to state space time series analysis (Practical econometrics). Oxford; New York: Oxford University Press, 2007, pp. xiv, 174.
  37. [37] J. Durbin and S. J. Koopman, Time series analysis by state space methods, 2nd ed. ed. (Oxford statistical science series, no. 38). Oxford: Oxford University Press, 2012, pp. xxi, 346.10.1093/acprof:oso/9780199641178.001.0001
  38. [38] L. Huang, Y. Yang, H. Zhao, X. Wang, and H. Zheng, “Time series modeling and filtering method of electric power load stochastic noise,” Protection and Control of Modern Power Systems, vol. 2, no. 1, p. 7, 2017, Art. no. Pii: 59.10.1186/s41601-017-0059-8
  39. [39] S. Markoulakis, “Short-term load forecasting based on the Kalman filter and the neural-fuzzy network (ANFIS).”
  40. [40] M. Gaur and A. Majumdar, “One-Day-Ahead Load Forecasting using nonlinear Kalman filtering algorithms,” 2016.
  41. [41] J. Cheng, W. Xiong, and L. Ai, “Electric Load Forecasting Based on Improved Grey Neural Network,” in Recent advances in computer science and information engineering, vol. 124, Z. Qian, Ed. (Lecture Notes in Electrical Engineering, no. 124-129) Heidelberg: Springer, 2012, pp. 651–655.10.1007/978-3-642-25781-0_95
  42. [42] C. Herui, B. Tao, and L. Yanzi, “Short-term Power Load Forecasting Based on Gray Theory,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 11, 2013.10.11591/telkomnika.v11i11.3547
  43. [43] M. Jin, X. Zhou, Z. M. Zhang, and M. M. Tentzeris, “Short-term power load forecasting using grey correlation contest modeling,” Expert Systems with Applications, vol. 39, no. 1, pp. 773–779, 2012, Art. no. Pii: s0957417411010347.10.1016/j.eswa.2011.07.072
  44. [44] J. A. S. Kelso, P. Érdi, K. J. Friston, H. Haken, J. Kacprzyk, J. Kurths, L. E. Reichl, P. Schuster, F. Schweitzer, D. Sornette, S. Liu, and Y. Lin, Grey Systems (no. 68). Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, p. 401.
  45. [45] S. Liu and Y. Lin, Grey systems: Theory and applications / Sifeng Liu and Yi Lin (Understanding complex systems). Berlin: Springer Verlag, 2010.10.1007/978-3-642-16158-2_1
  46. [46] S. Liu, Y. Yang, and J. Forrest, Grey data analysis: Methods, models and applications / Sifeng Liu, Yingjie Yang, Jeffrey Forrest (Computational risk management). Singapore: Springer, 2017.10.1007/978-981-10-1841-1
  47. [47] Y. Lu, Y. Teng, and H. Wang, “Load Prediction in Power System with Grey Theory and its Diagnosis of Stabilization,” Electric Power Components and Systems, vol. 47, no. 6-7, pp. 619–628, 2019.10.1080/15325008.2019.1587648
  48. [48] J. Mi, L. Fan, X. Duan, and Y. Qiu, “Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model,” Mathematical Problems in Engineering, vol. 2018, no. 1, pp. 1–11, 2018, Art. no. Pii: 3894723.10.1155/2018/3894723
  49. [49] T. Ozcan, T. Küçükdeniz, and F. H. Sezgin, “Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting,” in Nature inspired computing, vol. 1, I. R. Management Association, Ed. Hershey PA: IGI Global, 2017, pp. 1161–1183.10.4018/978-1-5225-0788-8.ch044
  50. [50] H. Zhao and S. Guo, “An optimized grey model for annual power load forecasting,” Energy, vol. 107, pp. 272–286, 2016, Art. no. Pii: s0360544216304066.10.1016/j.energy.2016.04.009
  51. [51] P. Ji, D. Xiong, P. Wang, and J. Chen, “A Study on Exponential Smoothing Model for Load Forecasting,” presented at the Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2012.27-29 March 2012, Shanghai, China; proceedings, Piscataway, NJ, 2012. Available: http://ieeexplore.ieee.org/document/6307555/
  52. [52] D. Dragan, T. Kramberger, and M. Intihar, “A comparison of Methods for Forecasting the Container Throughput in North Adriatic Ports,” presented at the IAME 2014, Norfolk, 2014.
  53. [53] D. Dragan, A. Keshavarzsaleh, T. Kramberger, B. Jereb, and M. Rosi, “Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains,” Logistics & Sustainable Transport, vol. 10, no. 1, pp. 11–30, 2019.10.2478/jlst-2019-0002
  54. [54] R. J. Hyndman, Forecasting with exponential smoothing. Berlin; London: Springer, 2008.10.1007/978-3-540-71918-2
  55. [55] R. Weron, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. England: John Wiley & Sons Ltd, 2006.10.1002/9781118673362
  56. [56] R. Adhikari and R. K. Agrawal, An Introductory Study on Time Series Modeling and Forecasting. LAP Lambert Academic Publishing, 2013.
  57. [57] J. G.Jetcheva, MostafaMajidpour, and Wei-PengChen, “Neural Network Model Ensembles for Building Level Electricity Load Forecasts,” Energy andBuildings, vol. 84, no. 2014, pp. 214–223, 2014.10.1016/j.enbuild.2014.08.004
  58. [58] M. Sarhani and A. E. Afia, “Electric Load Forecasting Using Hybrid Machine Learning Approach Incorporating Feature Selection,” in International Conference on Big Data Cloud and Applications, Tetuan, Morocco, 2015.
  59. [59] X. Wang, K. Smith-Miles, and R. Hyndman, “Rule Induction for Forecasting Method Selection: Meta-Learning the Characteristics of Univariate Time Series,” Neurocomputing, vol. 72, no. 10-12, pp. 2581-2594, 2009.10.1016/j.neucom.2008.10.017
  60. [60] M. Intihar, T. Kramberger, and D. Dragan, “Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model,” PROMET - Traffic&Transportation, vol. 29, no. 5, pp. 529–542, 2017.10.7307/ptt.v29i5.2334
  61. [61] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina, Chapel Hill2004, vol. TR 95-041.
  62. [62] E. Kayacan, B. Ulutas, and O. Kaynak, “Grey system theory-based models in time series prediction,” Expert Systems with Applications, vol. 37, no. 2, pp. 1784-1789, 2010.10.1016/j.eswa.2009.07.064
  63. [63] Y. Feng, “Study on Medium and Long Term Power Load Forecasting Based on Combination Forecasting Model,” Chemical Engineering Transactions, vol. 51, no. 2015, pp. 859-864, 2015.
  64. [64] E. Ostertagová and O. Ostertag, “The Simple Exponential Smoothing Model,” presented at the Modelling of Mechanical and Mechatronic Systems 2011: The 4th International conference, Faculty of Mechanical engineering, Technical university of Košice, 2011.
  65. [65] A. Chusyairi, R. N. S. Pelsri, and Bagio, “The Use of Exponential Smoothing Method to Predict Missing Service E-Report,” presented at the Information Systems and Electrical Engineering (ICITISEE): 2nd International Conferences on Information Technology, 2017.10.1109/ICITISEE.2017.8285535
  66. [66] M. A. Momani, W. H. Alrousan, and A. T. Alqudah, “Short-Term Load Forecasting Based on NARX and Radial Basis Neural Networks Approaches for the Jordanian Power Grid “ Jordan Journal of Electrical Engineering, vol. 2, no. 1, pp. 81-93, 2016.
  67. [67] L. C. M. d. Andrade, M. Oleskovicz, A. Q. Santos, D. V. Coury, and R. A. S. Fernandes, “Very short-term load forecasting based on NARX recurrent neural networks,” in 2014 IEEE PES general meetingPiscataway, NJ: IEEE, 2014, pp. 1–5.10.1109/PESGM.2014.6939012
  68. [68] J. Buitrago and S. Asfour, “Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs,” Energies, vol. 10, no. 1, p. 40, 2017, Art. no. PII: en10010040.10.3390/en10010040
  69. [69] W. X. Jiatang Cheng and L. Ai, “LNEE 124 - Electric Load Forecasting Based on Improved Grey Neural Network.”
  70. [70] H. Li, Y. Zhu, J. Hu, and Z. Li, “A localized NARX Neural Network model for Short-term load forecasting based upon Self-Organizing Mapping,” in 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), I. I. F. E. E. Conference, Ed. [Piscataway, NJ]: IEEE, 2017, pp. 749–754.10.1109/IFEEC.2017.7992133
  71. [71] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006.10.1016/j.neucom.2005.12.126
  72. [72] Y. Chen, M. Kloft, Y. Yang, C. Li, and L. Li, “Mixed kernel based extreme learning machine for electric load forecasting,” Neurocomputing, vol. 312, pp. 90-106, 2018.10.1016/j.neucom.2018.05.068
  73. [73] S. K. Dash and D. Patel, “Short-term electric load forecasting using Extreme Learning Machine - a case study of Indian power market,” presented at the 2015 IEEE Power, Communication and Information Technology Conference (PCITC.15-17 October, 2015, Siksha ‘O’ Anusandhan University, Bhubaneswar, India : PCITC-2015 proceedings, [Piscataway, NJ], 2015. Available: http://ieeexplore.ieee.org/document/7438135/10.1109/PCITC.2015.7438135
  74. [74] Ö. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,” International Journal of Electrical Power & Energy Systems, vol. 78, pp. 429-435, 2016.10.1016/j.ijepes.2015.12.006
  75. [75] C. H. Weng, W. Ting, L. Xueyong, and R. Weerasinghe, “Research on short-term electric load forecasting based on extreme learning machine,” E3S Web of Conferences, vol. 53, p. 02009, 2018.
  76. [76] P. J. Garcia-Laencina, “Improving Predictions Using Linear Combination Of Multiple Extreme Learning Machines,” Information Technology And Control, vol. 42, no. 1, 2013.10.5755/j01.itc.42.1.1667
  77. [77] M. A. A. Albadr and S. Tiun, “Extreme Learning Machine: A Review “ International Journal of Applied Engineering Research, vol. 12, no. 14, pp. 4610-4623, 2017.
  78. [78] W. Ting and L. Xueyong, “Research on short-term electric load forecasting based on extreme learning machine,” E3S Web of Conferences, vol. 53, p. 02009, 2018.10.1051/e3sconf/20185302009
  79. [79] Y. Wei, H. Huang, B. Chen, B. Zheng, and Y. Wang, “Application of Extreme Learning Machine for Predicting Chlorophylla Concentration Inartificial Upwelling Processes,” Mathematical Problems in Engineering, vol. 2019, pp. 1-11, 2019.10.1155/2019/8719387
  80. [80] Z. Yang, T. Zhang, J. Lu, Y. Su, D. Zhang, and Y. Duan, “Extreme learning machines for regression based on V-matrix method,” Cogn Neurodyn, vol. 11, no. 5, pp. 453-465, Oct 2017.10.1007/s11571-017-9444-2
  81. [81] Y. Fu, Z. Li, H. Zhang, and P. Xu, “Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices,” Procedia Engineering, vol. 121, pp. 1016-1022, 2015.10.1016/j.proeng.2015.09.097
  82. [82] W.-C. Hong, “Electric load forecasting by support vector model,” Applied Mathematical Modelling, vol. 33, no. 5, pp. 2444-2454, 2009.10.1016/j.apm.2008.07.010
  83. [83] Z. Hu, Y. Bao, and T. Xiong, “Electricity load forecasting using support vector regression with memetic algorithms,” ScientificWorldJournal, vol. 2013, p. 292575, 2013.10.1155/2013/292575
  84. [84] S. Qiang and Y. Pu, “Short-term power load forecasting based on support vector machine and particle swarm optimization,” Journal of Algorithms & Computational Technology, vol. 13, p. 174830181879706, 2018.10.1177/1748301818797061
  85. [85] S. Maldonado, A. González, and S. Crone, “Automatic time series analysis for electric load forecasting via support vector regression,” Applied Soft Computing, vol. 83, p. 105616, 2019.10.1016/j.asoc.2019.105616
  86. [86] N. Cristianini and J. Shawe-Taylor, An introduction to Support Vector Machines. New York: Cambridge University Press, 2000.
  87. [87] N. Deng, Y. Tian, and C. Zhang, Support vector machines (Chapman & Hall/CRC data mining and knowledge discovery series). Boca Raton: CRC Press, 2013.
  88. [88] I. Steinwart and A. Christmann, Support vector machines (Information science and statistics). New York: Springer, 2008.
  89. [89] J. A. K. Suykens, Least squares support vector machines. New Jersey; London: World Scientific H1 - British Library H2 - DSCm03/18809, 2002.10.1142/5089
  90. [90] A. Abraham and S. Das, Computational Intelligence in Power Engineering. Springer Berlin Heidelberg, 2010.
  91. [91] P. L. Anderson, Business Economics and Finance with MATLAB, GIS, and Simulation Models. CRC Press, 2004.10.1201/9780203494653
  92. [92] M. F. Azeem, Fuzzy Inference System: Theory and Applications. IntechOpen, 2012.10.5772/2341
  93. [93] J. H. Chow, F. F. Wu, and J. A. Momoh, Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence. Springer US, 2004.10.1007/b101578
  94. [94] S. K. Halgamuge and L. Wang, Computational Intelligence for Modelling and Prediction. Springer Berlin Heidelberg, 2005.10.1007/b93960
  95. [95] R. Jensen and Q. Shen, Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley, 2008.10.1002/9780470377888
  96. [96] S. Kalogirou, Artificial Intelligence in Energy and Renewable Energy Systems. Nova Science Publishers, 2007.
  97. [97] A. Konar and D. Bhattacharya, Time-Series Prediction and Applications: A Machine Intelligence Approach. Springer International Publishing, 2017.10.1007/978-3-319-54597-4
  98. [98] E. Ogliari and S. Leva, Computational Intelligence in Photovoltaic Systems. Mdpi AG, 2019.
  99. [99] A. K. Palit and D. Popovic, Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer London, 2006.
  100. [100] M. Paulescu, E. Paulescu, P. Gravila, and V. Badescu, Weather Modeling and Forecasting of PV Systems Operation. Springer London, 2012.10.1007/978-1-4471-4649-0
  101. [101] W. Pedrycz and S. M. Chen, Time Series Analysis, Modeling and Applications: A Computational Intelligence Perspective. Springer Berlin Heidelberg, 2012.10.1007/978-3-642-33439-9
  102. [102] S. A. Soliman and A. M. Al-Kandari, Electrical Load Forecasting: Modeling and Model Construction. Elsevier Science, 2010.10.1016/B978-0-12-381543-9.00020-8
  103. [103] Y. H. Song, Modern Optimisation Techniques in Power Systems. Springer Netherlands, 1999.10.1007/978-94-015-9189-8
  104. [104] M. Sudha, Applied Computational Intelligence. Educreation Publishing, 2019.
  105. [105] G. Tayfur, Soft Computing in Water Resources Engineering: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms. WIT Press, 2014.
  106. [106] K. E. Voges and N. Pope, Business Applications and Computational Intelligence. Idea Group Publishing, 2006.10.4018/978-1-59140-702-7
  107. [107] J. Wang, Business Intelligence in Economic Forecasting: Technologies and Techniques: Technologies and Techniques. Information Science Reference, 2010.10.4018/978-1-61520-629-2
  108. [108] L. Wang, C. Singh, and A. Kusiak, Wind Power Systems: Applications of Computational Intelligence. Springer Berlin Heidelberg, 2010.10.1007/978-3-642-13250-6
  109. [109] P. P. Wang, Computational Intelligence in Economics and Finance. Springer Berlin Heidelberg, 2013.
  110. [110] A. T. Ali, E. B. Tayeb, and Z. M. Shamseldin, “Short term electrical load forecasting using fuzzy logic,” International Journal Of Advancement In Engineering Technology, Management and Applied Science (IJAETMAS), vol. 3, 2016.
  111. [111] D. Ali, M. Yohanna, M. I. Puwu, and B. M. Garkida, “Long-term load forecast modelling using a fuzzy logic approach,” Pacific Science Review A: Natural Science and Engineering, vol. 18, no. 2, pp. 123-127, 2016.10.1016/j.psra.2016.09.011
  112. [112] M. Faysal, M. J. Islam, M. M. Murad, M. I. Islam, and M. R. Amin, “Electrical Load Forecasting Using Fuzzy System,” Journal of Computer and Communications, vol. 07, no. 09, pp. 27-37, 2019.10.4236/jcc.2019.79003
  113. [113] M. K. Singla and S. Hans, “Load Forecasting using Fuzzy Logic Tool Box,” Global Research and Development Journal for Engineering, vol. 38, pp. 12-19, 2018.
  114. [114] L. Yao, Y.-l. Jiang, and J. Xiao, “Short-Term Power Load Forecasting by Interval Type-2 Fuzzy Logic System,” in Information Computing and Applications, Berlin, Heidelberg, 2011, pp. 575-582: Springer Berlin Heidelberg.10.1007/978-3-642-27452-7_78
  115. [115] Z. Ismail and R. Mansor, “Fuzzy Logic Approach for Forecasting Half-hourly Electricity Load Demand,” Fuzzy Logic Approach for Forecasting Half-hourly Electricity Load Demand, 09/14 2011.
  116. [116] J. Jamaaluddin, D. Hadidjaja, I. Sulistiyowati, E. A. Suprayitno, I. Anshory, and S. Syahrorini, “Very short term load forecasting peak load time using fuzzy logic,” IOP Conference Series: Materials Science and Engineering, vol. 403, p. 012070, 2018.10.1088/1757-899X/403/1/012070
  117. [117] J. Kaur and Y. S. Brar, “Short term load forecasting using fuzzy logic of 220KV transmission line,” Int. J. Eng. Res. Technol, vol. 3, no. 2278, p. e0181, 2014.
  118. [118] A. Laouafi, M. Mordjaoui, and T. E. Boukelia, “An adaptive neuro-fuzzy inference system-based approach for daily load curve prediction,” Journal of Energy Systems, pp. 115-126, 2018.10.30521/jes.434224
  119. [119] S. K. Patel and S. Sharma, “A Review of very Short-Term Load Forecasting (STLF) using Wavelet Neural Networks,” International Journal of Science, Engineering and Technology Research vol. 4, no. 2, 2015.
  120. [120] M. Mitchell, An introduction to genetic algorithms, 7. print ed. (A Bradford book). Cambridge, Mass., 2001, p. 209.
  121. [121] R. L. Haupt and S. E. Haupt, Practical genetic algorithms, 2nd ed. ed. Hoboken, N.J.; Chichester: Wiley-Interscience, 2004.10.1002/0471671746
  122. [122] J. Carr, “An Introduction to Genetic Algorithms,” 2014.
  123. [123] C.-C. Hsu, C.-H. Wu, S.-C. Chen, and K.-L. Peng, “Dynamically Optimizing Parameters in Support Vector Regression: An Application of Electricity Load Forecasting,” presented at the System Sciences, the 39th Annual Hawaii International Conference, 2006.
  124. [124] D. Beasley, D. R. Bull, and R. R. Martin, “An Overview of Genetic Algorithms : Part 1, Fundamentals,” University Computing, vol. 15, no. 2, pp. 56-69, 1993.
  125. [125] Y. K. Al-Douri, H. Al-Chalabi, and J. Lundberg, “Time Series Forecasting using Genetic Algorithm,” in The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences, 2018.
  126. [126] R. R. B. de Aquino, O. N. Neto, M. M. S. Lira, A. A. Ferreira, and K. F. Santos, “Using Genetic Algorithm to Develop a Neural-Network-Based Load Forecasting,” in Artificial Neural Networks – ICANN 2007, Berlin, Heidelberg, 2007, pp. 738-747: Springer Berlin Heidelberg.10.1007/978-3-540-74695-9_76
  127. [127] A. Gupta and P. K. Sarangi, “Electrical load forecasting using genetic algorithm based back-propagation method,” ARPN Journal of Engineering and Applied Sciences, vol. 7, no. 8, pp. 1017-1020, 2012.
  128. [128] G. M. Khan, F. Zafari, and S. A. Mahmud, “Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN),” in 2013 12th International Conference on Machine Learning and Applications, 2013, vol. 2, pp. 152-155.10.1109/ICMLA.2013.181
  129. [129] F. Li and X. Zhao, “The application of genetic algorithm in power short-term load forecasting,” in 2012 International Conference on Image, Vision and Computing (ICIVC 2012), 2012.
  130. [130] J. I. Silva-Ortegaa, B. Cervantes-Bolivarb, I. A. Isaac-Millanc, Y. Cardenas-Escorciab, and G. Valencia-Ochoad, “Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system,” CHEMICAL ENGINEERING, vol. 67, 2018.
  131. [131] H. Verdejo, A. Awerkin, C. Becker, and G. Olguin, “Statistic Linear Parametric Techniques for Residential Electric Energy Demand Forecasting: A Review and An Implementation to Chile,” Renewable and Sustainable Energy Reviews, vol. 74, no. 2017, pp. 512–521, 2017.10.1016/j.rser.2017.01.110
  132. [132] M. Singla, “Load Forecasting Using Artificial Neural Network,” Thapar Institute, 2018.10.1109/ICIIP.2017.8313703
  133. [133] J. Buitrago, “Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Multivariable Inputs,” Open Access Dissertations, 2017.10.3390/en10010040
  134. [134] M. Shepero, “Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting,” ed: Uppsala universitet, 2018.
Language: English
Page range: 51 - 76
Submitted on: Dec 23, 2019
Published on: Feb 20, 2020
Published by: University of Maribor
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2020 Mahmoud A. Hammad, Borut Jereb, Bojan Rosi, Dejan Dragan, published by University of Maribor
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