A Multi-layer Artificial Neural Network Architecture Design for Load Forecasting in Power Systems

Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

Abstract:

In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059529

References:

[1] Hadi Saadat, "Power System Analysis", Tata McGraw-Hill Publishing Company Limited, New Delhi, India.
[2] Kothari D.P., and I.J. Nagrath, "Modern Power System Analysis", Tata McGraw-Hill Publishing Company Limited, Third Edition, New Delhi, India. 2003.
[3] Robert Louis Stevenson, "Power System Analysis", Mc. Graw Hill, Singapore.
[4] Simpson P.K., "Fuzzy min-max neural networks - Part I : Classification", IEEE Trans. Neural Networks, Vol. 3, pp. 776-786, Sep. 1992.
[5] Guo Y, Hill D J, Wang Y, "Global transient stability and voltage regulation for power systems", IEEE Transactions on Power Systems, Vol.16, No. 4, pp. 678-688, Nov. 2001.
[6] Yanjun Li, David J. Hill, Tiejun Wu, "Optimal Coordinated Voltage Control of Power Systems - An Immune Algorithm Solution", Proc. Fifth Asian Control Conference 2004, Paper P206, Melbourne, Australia, 21 Jul-24 Jul. 2004.
[7] D.C. Park, M.A El-Sharkawi, R.J. Marks II, L.E. Atlas and M.J. Damborg, "Electric Load Forecasting Using An Artificial Neural Network", IEEE Transactions on Power Systems, Vol. 6, No. 2, May 1991, pp. 442-449.
[8] K.Y. Lee, Y.T. Cha, and J.H. Park, "Short-term load forecasting using an artificial neural network", IEEE Transactions on Power Systems, Vol. 7, No. 1, February 1992, pp. 125-132.
[9] S.-T. Chen, D.C. Yu, and AR. Moghaddamjo, "Weather sensitive short-term load forecasting using nonfully connected artificial neural network", IEEE Transactions on Power Systems, Vol. 7, No. 3, August 1992, pp. 1098-1105.
[10] C.N. Lu, H.T. Wu, and S. Vemuri, "Neural Network Based Short-term Load Forecasting", IEEE Transactions on Power Systems, Vol. 8, No. 1, February 1993, pp. 336-342.
[11] A.G. Bakirtzis, V. Petridis, S.J. Klartzis, M.C. Alexiadis and AH. Maissis, "A Neural Network Short Term Load Forecasting Model for the Greek Power System", IEEE Transactions on Power Systems, Vol. 11, No. 2, May 1996, pp. 858-863.
[12] I. Drezga and S. Rahman, "Input Variable Selection for ANN-based Short term Load Forecasting", IEEE Transactions on Power Systems, Vol. 13, No. 4, November 1998, pp. 1238-1244.
[13] Grene, S., I. Dobson, F.L. Alvardo, "Sensitivity of the loading margin of the voltage collapse w.r.t. arbitrary parameters," IEEE Transactions on Power Systems, Vol. 11, No. 1, Feb. 1997, pp. 262-272.
[14] Wenxin Liu, Ganesh K., Venayagamoorthy and Donald C. Wunsch, II, "Design of an adaptive neural network based power system stabilizer", Trans. Neural Networks, Vol. 16, Issues 5-6, June-July 2003, Pages 891-898.
[15] Srinivas Pillutla and Ali Keyhani , "Power system stabilization based on modular neural network architecture", International Journal of Electrical Power & Energy Systems, Vol. 19, Issue 6, August 1997, pp. 411-418.
[16] José A.L. Barreiros, André M.D. Ferreira, Carlos Tavares-da-Costa, Jr., Walter Barra, Jr. and Jo├úo A.P. Lopes, "A neural power system stabilizer trained using local linear controllers in a gain-scheduling scheme", International Journal of Electrical Power & Energy Systems, Volume 27, Issue 7, September 2005, pp. 473-479.
[17] Srinivas Pillutla and Ali Keyhani , "Power system stabilization based on modular neural network architecture", International Journal of Electrical Power & Energy Systems, Vol. 19, Issue 6, August 1997, pp. 411-418.
[18] Shu-Rong Li; Hai-Tao Shi, "Neural network based on excitation controller design of power systems via backstepping", Proc. 2003 International Conference on Machine Learning and Cybernetics, Vol. 2, Issue 6, 2-5 Nov. 2003, pp. 934 - 939.
[19] Ruzic, S.; Vuckovic, A.; Nikolic, N, "Weather sensitive method for short term load forecasting in Electric Power Utility of Serbia", IEEE Transactions on Power Systems, Vol. 18, Issue: 4, pp. 1581- 1586, Nov. 2003.
[20] Loi Lei Lai, "Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks", John Wiley, New York.