FORECASTING OF MAXIMUM PEAK LOAD DEMAND OF ABUJA REGION INTERCONNECTED POWER NETWORK USING ARTIFICIAL NEURAL NETWORK

Abstract

This paper presents an artificial neural network (ANN) based approach for long-term Maximum Load Demand Forecasting (MLDF) of 33KV substations of Abuja region interconnected network. Historical data were collected from the nine sub stations under Abuja region between 2013 to 2017. The data collated were analyzed using feed forward back propagation of ANN in training, testing and forecasting of load demand. The performance accuracy of ANN was assessed using Mean Square Error (MSE). The simulation results revealed that ANN shows quite good performance as a forecasting tool due to the trends and the large data set available. Further results showed an increase in load demand for all feeders except for Akwanga feeder within 2021-2022. Meanwhile, Apo feeder will have the highest MLDF of 176.43MW while Akwanga will have the lowest MLDF of 24.94MW by the year 2022. Keywords: Abuja, ANN, Forecasting, Load, Network.

Keywords

• ANN
• Forecasting
• Load
• Network.
• Abuja

References

1. Adepoju, G. A., Ogunjujibe, S. O. A, and Alawode, K. O., “Application of Neural Network to Load Forecasting in Nigerian Electrical Power System”, Pacific Journal of Science and Technology, 8(1) pp. 68-72, 2007.
2. Airoboman AE and Idiagi NS “Reliability Prediction in the Nigerian Power Industry using Neural Network” Journal of Electrical and Electronics Engineering (JEEE). Vol. 13, No.2, pp. 17-22, 2020.
3. Airoboman, AE. Ahmad, AA. Idiagi, NS and Aderibigbe, A “A Medium Term Prediction of Feeder Trip Profile on Power Systems Network using Artificial Intelligenc” IEEE PES Power Africa Conference, Nairobi, Kenya. pp. 1-5, 2020.
4. Alfares, H.K., and Nazeeruddin, M., “Electric Load Forecasting” Literature survey and classification of methods”, International Journal of System Science, Vol. 33(1), pp. 23-34, 2002
5. Arroyo, D. O., Skov, M.K., and Huynh, Q., “Accurate Electricity Load Forecasting with Artificial Neural Networks”, Proceedings of the 2005 International Conference on Computational Intelligence for modelling, control and Automation, and International conference on Intelligent Agents, Web Technologies and Internet Commerce, 2005.
6. Bakirtzis, A. G., Petridis, V., Kiartzis, S. J., Alexiadis, M. C., and Maissis, A. H.: “A neural Network Short-Term Load Forecasting Model for the Greek Power System’, IEEE Transactions on Power Systems, 11, pp.858-863, 1996.
7. Bassi, D., and Olivares, O., “Medium Term Electric Load Forecasting Using TLFN Neural Networks”, International Journal of Computers, Communications and Control Vol. 1 N0. 2. pp. 23-32., 2006.
8. Brockmann, W and Kuthe, S “Different Models to Forecast Electricity Loads”, EUNITE competition. Available: http://neuron.tuke.sk/competition/index.php

9. Bunn, D.W, and Farmer, E. D., Review of load Forecasting Methods in the Electric Power Industry, New York: Wiley, pp13-30, 2008.
10. Chakrabarti, A., and Halder, S., Power system Analysis. Operation and Control, (2ndedu), PHI learning Private Ltd, New Delhi, 2008.
11. Chakraborty, K., Mehrota, K., Mohan, C. K., and Ranka, S., Forecasting the Behaviour of Multivariate Time Series Using Neural Networks, vol5 pp.961-970, 2002.
12. Douglas V. H., Microprocessor and Interfacing: Programming and Hardware, (2ndedn.), Tata McGraw-Hill Publishing Company Ltd, New Delhi, 2010.
13. Feinberg, E. A., Hajagos, J. T., and Genethliou, D., “Statistical Load Modelling”, Proceedings of the 7th IASTED International Conference: Power and Energy Systems, pp.88-99, Palm Springs, CA, 2003.
14. Haida, T., and Muto, S., to Electric Regression based Peak Load Forecasting Using a Transformation Technique, IEEE Transactions on Power Systems, Vol.9, pp.1788-1794, 2005
15. Hongzhan Nie, Guohui Liu, Xiaoman Liu, Yong Wang, "Hybrid of ARIMA and SVMs for Short-Term Load Forecasting", 2012 International Conference on Future Energy, Environment, and Materials, Energy Procedia 16 (2012) 1455–1460
16. Kalaitzakis, K., Stavrakakis, G. S., Anagnostakis, E. M. “short-term load forecasting Based on artificial neural networks parallel implementation”, Electric Power Systems Research 63, pp.185-196, 2002.
17. Khotanzad A, Zhou E, Elragal H. A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment. IEEE Trans Power System 2002; 17:1273–82.
18. Kolarik, T., and Rudorfer, G., Time Series Forecasting Using Neural Networks,Proceedings of the International Conference on APL., Antwerp, Belgium, pp.86-94, 2007.
19. Sarangi, P.K., Singh, N., Chanhan, R. K., and Singh, R., “Short Term Load Forecasting Using Artificial Neural Network: A Comparison with Genetic Algorithm Implementation”, Asian Research Publishing Network (ARPN) Journal of Engineering and Applied Sciences Vol. 4 (9), November 2009.
20. Srinivasan, D., and Lee, M. A., Survey of Hybrid Fuzzy Neural Approaches to Electric Load Forecasting, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Part 5, Vancouver, BC, pp. 4004-4008, 2005.
21. Stelios A Markoulakis, George S Stavrakakis, Triantafyllia G Nikolaou, "Short-term load Forecasting based on the Kalman filter and the neural-fuzzy network (ANFIS)", Proceedings of the 2006 IASME/WSEAS International Conference on Energy & Environmental Systems, Chalkida, Greece, May 810, 2006 (pp189-193)
22. Steven Mill Publish Articles on blog on Electrical load forecasting 2016
23. Tripathi M, Upadhyay K, Singh S. Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market.Electr J 2008;21:24–34
24. Vadhera, S.S., Power System Analysis and Stability, Khana Publishers, NaiSarak, Delhi, 2004. . Wu J, Wang J, Lu H, Dong Y, Lu X. Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model. Energy Convers Manag 2013; 70:1–9. Yang K, Zhao L. Load forecasting model based on amendment of mamdani fuzzy system. In: Proceedings of 5th international conference on wireless communications, networking and mobile computing, WiCom'09: IEEE; 2009.p. 1–4
25. Yi, M.M., Linn, K.S., and Kyaw, M., “Implementation of Neural Network Based Electricity Load Forecasting”, World Academy of Science, Engineering and Technology, 42, 2008