# ELECTRICITY CONSUMPTION PREDICTION SYSTEM USING A RADIAL BASIS FUNCTION NEURAL NETWORK

### Abstract

The observed poor quality of service being experienced in the power sector of Nigeria economy has been traced to non-availability of adequate model that can handle the inconsistencies associated with traditional statistical models for predicting consumers electricity need, so as to bridge the gap between the demand and supply of the energy. This research presents Electricity Consumption Prediction System (ECPS) based on the principle of radial basis function neural network to predict the countrys electricity consumption using the historical data sourced from Central Bank of Nigeria (CBN) annual statistical bulletin. The entire datasets used in the study were divided into train, validation and test sets in the ratio of 13:3:4. By the above, 65% of the entire data were used for the training, 15% for validation and 20% for testing. The train data was presented to the constructed models to approximate the function that maps the input patterns to some known target values. The models were also used to simulate both validation and the test datasets as case data on the consistency of results obtained from the training session through the train data. Experimental results showed that RBF network model performs better than equivalent Backpropagation (BP) network models that were compared with it and provides the best platform for developing a forecast system.

### References

Azadeh, A., Ghaderi, S.F., Sohrabkhani, S. 2008. Annual Electricity Consumption Forecasting by Neural Network in High Energy Consuming Industrial Sectors. Energy Conversion and management, 49(8):2272-2278.

Bernander, O. 2006. Neural Network. Microsoft Encarta® 2006 DVD. Redmond, Microsoft Encarta®, © Microsoft Corporation., pp. 1993-2005.

Boehm, B., 1986. A Spiral Model for Software Development and Enhancement. ACM Software Engineering Note, pp. 14-24.

Bonanno, F., Capizzi, G., Napoli, C., Graditi, G., Tina, G.M. 2012. A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Applied Energy (ELSEVIER), 97:956-961.

Broomhead, D.S., Lowe, D., 1988. Multi-variable Functional Interpolation and Adaptive Networks. Complex Systems, 2: 321-355.

Central Bank of Nigeria Statistical Bulletin 2006. CBN Press, Abuja September, 2006. www.cenbank.org/.../STAT BULETIN/...

Cho, S.H., Kim, W.T., Tae, C.S. and Zaheeruddin, M., 2004. Effect of length of measurement period on accuracy of predicted annual heating energy consumption of buildings. Energy Conversion and Management, 45(18-19):2867 – 2878.

Caudill, M., C. Butler, 1992. Understanding Neural Networks: v1 and 2, Cambridge, Massachusetts MIT Press, pp. 354.

Doraisamy H., Daniel H., Halleck, P.M., 2000. The American Association of Geologists. AAPG Bulletin, 84 (12): 1895-1904.

Folorunso, O., Akinwale, A.T., Asiribo, O.E., Adeyemo, T.A., 2010. Population Prediction Using Artificial Neural Network. African Journal of Mathematics and Computer Science Research, 3(8): 155-162.

Frimpong, E.A., Okyere, P.Y., 2012. Forecasting Daily Peak Load of Ghana using Radial Basis Function Neural Network and Wavelet Transform. Journal of Electrical Engineering. www.jee.ro, pp.1-4.

Ghaderi S.F., Azadeh, M.A., Mohammadzadeh S., 2006. Electricity Demand Function for the Industries of Iran. Information Technology Journal 5(3):401-404.

Ghods, L., Kalantar, M., 2010. Long-term peak demand forecasting by using radial basis function neural networks. Iranian Journal of Electrical & Electronic Engineering, 6(3):320-328, September, 2010.

Gonzalez, P.A., Zamarreno, J.M., 2005. Prediction of Hourly Energy Consumption in Buildings based on a Feedback Artificial Neural Network. Energy and Buildings, 37(6):595-601.

Grando, N., Centeno, T.M., Botelho, S.S.C., Fontoura, F.M., 2011. Forecasting Electric Energy Demand Using a Predictor Model based on Liquid State Machine. International Journal of Artificial intelligence and Expert Systems (IJAE), 1(2): 40-53.

Haykin, S. 1999. Neural Networks: A Comprehensive Foundation (2nd Edition). Macmillan College Publishing Company, New York.

Hoffman, A. J. 1998. Peak demand control in commercial buildings with target peak adjustment based on load forecasting. In Proceedings of the 1998 IEEE International Conference on Control Applications, 2:1292 – 1296.

Karabulut, K., Alkan, A., Yilmaz, A.S., 2008. Long Term Energy consumption Forecasting Using Genetic Programming. Mathematical and Computational Applications, 13(2):71-78.

Kimbara, A., Kurosu, S., Endo, R., Kamimura, K., Matsuba, T., Yamade, A., 1995. Online Prediction for Load Profile of an Air-conditioning System. ASHREA Transaction, 101(2):198-207.

Lendasse, A., Lee, J., Wertz, V., and Verleysen, M., 2002. Forecasting Electricity Consumption Using Nonlinear Projection and Self-Organizing Maps. Neurocomputing 48: 299-311.

Liang, R.H., Cheng, C.C., 2000. Combined Regression-Fuzzy Approach for Short-term Load Forecasting. IEEE Proceedings-Generation Transmission and Distribution. 147:261–266.

Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., and Loumos, V., 2009. Early and Dynamic Student Achievement Prediction in E-Learning Courses Using Neural Networks. Journal of the American Society for Information Science and Technology, 60(2):372-380.

Ma, Y. J, Yu, Q.,. Yang, C.Y and L. Wang, 2010. Study on power energy consumption model for large-scale public building. In Proceedings of the 2nd International Workshop on Intelligent Systems and Applications, Pp. 1 – 4.

MATLAB, 2008. MATLAB Environment, from http://www.mathworks.com/products/matlab/

Mohamed, Z., Bodger, P. 2005. Forecasting Electricity Consumption in New Zealand using Economic and Demographic Variables. ELSEVIER, Energy 30:1833-1843, doi: 10.10161/j.energy.2004.08.12.

Mulholland, D 2008. State Energy Forecast: An Overview of methods, USEPA, June 19, 2008.

Nizami, S. S. A. K. Javeed., Al-Garni, A. Z. 1995. Forecasting electric energy consumption using neural networks. Energy Policy, 23(12):1097–1104, December.

Noor, I., Omer, H.M., Ahmed, N.A., Aqeel, S.J., Nadheer, A.S., Yasar, N.L., 2011. Fast Prediction of Power transfer Stability Index based on Radial Basis Function Neural Network. International Journal of the Physical Sciences, 6(35):7978-7984, 23 December.

Olanrewaju, A.O., Adisa A.J., Pule, A.K., 2012. Comparing Performance of MLP and FBF Neural Network models for Predicting South Africa’s Energy Consumption. Journal of Energy in Southern Africa, 23(3):40-46, August.

Otavio, A.S., Carpinteiro, A., Agnaldo, J.R., Reis, A., Alexandre P.A., Da Silva, B. 2004. A Hierarchical Neural Model in Short-term Load Forecasting. Applied Soft Computing, 4:405–412.

Papadakis, S.E., Theocharis, J.B., Bakirtzis, A.G. 2003. A Load Curve Based Fuzzy Modeling Technique For Short-term Load Forecasting. Fuzzy Sets and Systems, 135:279–303.

Sambo, A.S., Garba, B., Zarma, I.H., Gaji, M.M., 2007. Electricity Generation and the Present Challenges in the Nigeria Power Sector. Energy Commission of Nigeria, Abuja-Nigeria.

Sarlak, M., Ebrahim, T., Karimi Madahi, S.S. 2012. Enhancement the Accuracy of Daily and Hourly Short-Time Load Forecasting using Neural Network. Journal of Basic and Applied Scientific Research 2(1)247-255.

Ubani, O.J., Umeh, L., Ugwu, L.N., 2013. Analysis of the Electricity Consumption in the South-East Geopolitical Region of Nigeria. Journal of Energy Technologies and Policy, 3(1):20-32, ISSN 2224-3232(Paper), ISSN 2225-0573(Online).

Wu, J., Liu, J., 2012. A Forecasting System for Car Fuel Consumption Using a Radial Basis

Function Neural Network. Expert Systems with Application (ELSEVIER), 39:1883-1888.

Zeng, J., Qiao, W., 2011. Short-Term Solar Power prediction using an RBF neural network. IEEE Power and Energy Society General Meeting, doi: 10.1109/PES.2011.6039204.

Zhangang, Y., Yanbo, C., Chen, K.W., 2007. Genetic algorithm-based RBF neural network load forecasting model. IEEE Xplore 1-4244-1298-6/07.