Forecasting Power load is a prime function of power system planning and management. However, it has proved to be a complicated task because of many unstable factors. There are a substantial growth rate and application levels of neural network (NN) in the power system. This review paper explores the forecasting methodology derived from the specific neural network. The electric power industries are presently undergoing transformations and extraordinary reforms. The most exciting and probably profitable developments in recent times are growing Artificial Intelligence (AI) usage of techniques. Therefore, this paper takes an overview of NN techniques and their application in power sectors. According to NN growth rate statistics, in certain power system issues, this paper considers the load forecasting, security assessment, economic dispatch, fault diagnosis, and harmonic analysis. The various disadvantages and advantages of NN applications in this aspect, envisaging the major challenges in this field are explained while considering NN applications in the power system operations and control. The comparison is made regarding several published IEEE papers from 1990 onwards until the present date, which clearly showed that this subject has attracted the maximum awareness in the last one decade, concerning; Load forecasting; Fault diagnosis and fault location; Economic Dispatch; Security Assessment; and Transient Stability.

Economic dispatch, Fault diagnosis, Harmonic is analyzing, Load forecasting, Neural network, Power system

[1] D.J. Sobajic, Y.H. Pao, "Artificial Neural Net Based Dynamic Security Assessment for Electric Power Systems," IEEE Transactions on Power Systems, Vol. 4, No. 1, pp. 220-228, 1989
[2] R. E., Bourguet, P. J. Antsaklis, "Artificial Neural Networks in Electric Power Industry,” Technical Report of the ISIS (Interdisciplinary Studies of Intelligent Systems) Group, No. ISIS-94-007, Univ of Notre Dame, Vol. 3, pp. 6, 1994.
[3] M. Tektaş, “Weather forecasting using ANFIS and ARIMA models,” Environmental Research, Engineering and Management, Vol. 51, Issue.1, pp. 5-10, 2010.
[4] A. Krenker, Janez Bešter, Andrej Kos, “Introduction to the Artificial Neural Networks,” Artificial Neural Networks, Kenji Suzuki, IntechOpen, Vol. 34, pp. 34-36, 2011.
[5] H.S. Hippert, C.E. Pedreira, R.C. Souza, “Neural networks for short-term load forecasting: a review and evaluation,” IEEE Transactions on Power Systems, Vol. 16, Issue 1, pp. 5-7, 2001.
[6] M. S. Tsai, Y. H. Lin, “Modern development of an adaptive non-intrusive appliance load monitoring system in electrical energy conservation.” Applied Energy Vol. 96, pp. 55–73, 2012.
[7] M. T. Haque, A. M. Kashtiban. Application of Neural Networks in Power Systems; A Review World Academy of Science, Engineering and Technology, Vol.1, No. 6, pp. 889-893, 2007.
[8] M.T., Vakil, N. Pavesic, “Training RBF Network with Selective Backpropagation,” Neurocomputing Elsevier Journal, Vol. 22, pp. 39-64, 2004.
[9] Yu-Hsiu Lin, Y. H. Lin, “Electrical Energy Management Based on a Hybrid Artificial Neural Network,” MDPI Journal, Vol. 12, pp. 236; 2018,
[10] S. Wei, L. Mohan, “Application of improved artificial neural networks in short-term power load forecasting,” Journal of Renewable and Sustainable Energy Vol. 7, pp. 43-46, 2015.
[11] S. J. Lakshmi, V. Balaji, Sarma S. Subramanya, “An Artificial Neural Network Controlled SVC for Dynamic Stability Enhancement of Power Transmission System,” International Journal of Scientific & Engineering Research, Vol. 5, Issue 4, pp. 233, 2014.
[12] R. Sharda, R. Patil, “Neural Networks as forecasting experts: an empirical test,” In Proceedings of the 1990 International Joint Conference on Neural Networks, Vol-I, pp. 491-494, 1990.
[13] A. Pannu, “Artificial Intelligence and its Application in Different Areas,” International Journal of Engineering and Innovative Technology, Vol. 4, pp. 10, 2015.
[14] C. Woodford, “Artificial Neural Networks,” Industrial and Control Engineering Applications, Vol. 22, pp. 12-14, 2018.
[15] T., W Saksornchai, J. Lee,. M., Methaprayoon, J., Liao, “Improve the Unit Commitment Scheduling by Using the Neural Network Based Short Term Load Forecasting,” IEEE Trans. Power Delivery, Vol. 36, pp. 33-39, 2004.
[16] H. S. Hippert, C.E. Pedreira, “Estimating temperature profiles for short-term load forecasting: neural networks compared to linear models,” IEEE Trans. on distribution and Generation Conference, Vol. 44, pp. 543-547, 2004.
[17] Y.H. Lin, Y.C. Hu, “Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: towards edge computing,” Sensors, Vol. 18, pp. 136, 2018.
[18] K. Warwick, A. Ekwure, R. Aggarwal, “Artificial Intelligence Techniques in Power Systems,” IEEE Power Engineering Series 22, Bookcratt Printed, Vol. 12, pp.. 17-19, 1997.
[19] A. G. Bahbah, A. A. Mathew, “New Method for Generator`s Angles and Angular Velocities Prediction for Transient Stability Assessment of Multi Machine Power Systems Using Recurrent Neural Network,” IEEE Trans of Power System, Vol. 19, pp. 1015-1022, 2006.
[20] M.T. Vakil, N. Pavesic, “Training RBF Network with Selective Backpropagation,” Neurocomputing Elsevier Journal, Vol. 33, pp. 39-64, 2004.
[21] A. K. Sinha, “Short Term Load Forecasting Using Artificial Neural Networks,” IEEE Trans. on Power System Distribution, Vol. 26, pp. 548-553, 2000.
[22] M. S. Kandil, S.M., El-Debeiky, N.E. Hasanien, “Long-term, Load Forecasting for Fast Developing Utility Using a Knowledge-Based Expert System,” IEEE Trans. on Power Systems, Vol. 17, No. 2, pp. 491-496, 2002.
[23] S. K., Mishra Ganapati Panda, Sukadev Meher, "Chebyshev Functional Link Artificial Neural Networks for Denoising of Image Corrupted by Salt and Pepper Noise,” International Journal of Recent Trends in Engineering,Academy publisher, Finland, ISSN: 1797-9617. Vol. 1, No. 1, pp. 413-417, 2009.
[24] A, Khosravi, S. Nahavandi, D. Creighton, “Short term load forecasting using Interval Type-2 Fuzzy Logic Systems, Fuzzy Systems (FUZZ),” IEEE International Conference; Vol. 27-32, pp. 502-508, 2011.
[25] C. Ying, “Short-term Load Forecasting: Similar Day-Based Wavelet Neural Networks,” IEEE Trans. Power System, Vol. 25, pp. 322-330, 2010.
[26] M. Cenek, R. Haro R. B. Sayers, J. Peng, “Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks,” Applied Science, Vol. 8, pp. 749, 2018.
[27] M. A. Sartori, P. J. Antsaklis, “A Simple Method to Derive Bounds on the Size and to Train MultiLayer Neural Networks,” IEEE Trans. On Neural Networks, Vol.2, 4, pp. 467-471, 1991.
[28] P.J. Antsaklis, “Educational Special Issue on Neural Networks in Control Systems,” IEEE Control System Magazine, Vol.12, pp. 8-57, 1992.
[29] A. Jain, E. Srinivas, R. Rauta, “Short term load forecasting using Fuzzy adaptive inference and similarity,” World Congress on Nature & Biologically Inspired Computing; Vol. 9-11, pp. 173-174, 2009.
[30] K. Yang, L. Zhao L, “Application of Mamdani Fuzzy System Amendment on Load Forecasting Model, Symposium on Photonics and Optoelectronics; Vol.4, pp. 1-4, 2009.
[31] A. Singh, H. Chen, A.C. Canizares, “ANN-based short term load forecasting in electricity markets, In Proceedings of the IEEE power engineering society transmission and distribution conference, Vol. 34, pp. 411-415, 2001.
[32] W.J. Taylor, E.P. McSharry, M.L. de Menezes, “A comparison of variation methods used for forecasting electricity demand up to a year ahead,” International Journal of Forecasting, Vol. 22, pp. 1-16, 2006.
[33] N., Saksomchai, W. J. Lee, K. Methaprayoon, J. Liao, “Improve the Unit Commitment Scheduling by Using the Neural Network Based Short Term Load Forecasting,” IEEE, Vol. 4, pp. 33-39, 2004.
[34] L. Zhao, “Load forecasting based on amendment of Mamdani Fuzzy System,” Wireless communications, networking & mobile computing; Vol. 26, pp. 1-4, 2009.
[35] L. Shaikh, K. Sawlani, “A Rainfall Prediction Model Using Articial Neural Network,” IJSRNSC, Network Security and Communication, Review Paper, Vol.5, pp. 1, 2017.
[36] N. S. Lele, “Image Classification Using Convolution Neural Network, “International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp. 22-26, 2018.
[37] C. Hernández-Hernández, F. Rodríguez, J.C. Moreno, da Costa Mendes, P.R. Normey-Rico, J.E.J.L. Guzmán, “The Comparative Study, Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management,” Energies, Vol. 10, pp. 884, 2017.
[38] M. B. Abdul Hamid, T. K., Abdul Rahman, “Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm,” 12th International Conference on Computer Modeling and Simulation (UKSim), Vol. 11, pp.. 408-413, 2010.
[39] M. T. Haque, A. M. Kashtiban, “Application of Neural Networks in Power Systems; A Review,” World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, Vol. 1, No.6, pp. 897-901, 2007.