World Academy of Science, Engineering and Technology
Published October 27, 2011 | Version 12881
Journal article Open

Development of Neural Network Prediction Model of Energy Consumption

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In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

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References

  • H. R.Maier and G. C.Dandy, "Neural network for the prediction and forecasting of water resources variables: a review of modelling issues and applications," Environmental Modelling & Software, vol. 15, 2000, pp. 101-124.
  • P. Benardos and G. Vosniakos, "Optimizing feedforward artificial neural network architecture," Engineering Applications of Artificial Intelligence, vol. 20, Apr. 2007, pp. 365-382.
  • M. N Jamal, M. E Ibrahim, and A. N Salam, "Multilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale," World Applied Sciences Journal, vol. 5, 2008, pp. 546-552.
  • D. Ivezić, "Short-Term Natural Gas Consumption Forecast," FME Transactions, vol. 34, 2006, pp. 165-169.
  • R. Kizilaslan and B. Karlik, "Comparison neural networks models for short term forecasting of natural gas consumption in Istanbul," Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008., 2008, pp. 448-453.
  • K. Kavaklioglu, H. Ceylan, H.K. Ozturk, and O.E. Canyurt, "Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks," Energy Conversion and Management, vol. 50, Nov. 2009, pp. 2719-2727.
  • D. Peharda, M. Delimar, and S. Loncaric, "Short term hourly forecasting of gas consumption using neural networks," Information Technology Interfaces, 2001. ITI 2001. Proceedings of the 23rd International Conference on, 2001, pp. 367-371.
  • A. Khotanzad and H. Elragal, "Natural gas load forecasting with combination of adaptive neural networks," Neural Networks, 1999. IJCNN '99. International Joint Conference on, 1999, pp. 4069-4072 vol.6.
  • R. Brown and I. Matin, "Development of artificial neural network models to predict daily gas consumption," Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on, 1995, pp. 1389-1394 vol.2. [10] P. Musilek, E. Pelik├ín, T. Brabec, and M. Simunek, "Recurrent Neural Network Based Gating for Natural Gas Load Prediction System," Neural Networks, IJCNN'06, 2006, pp. 3736-3741. [11] Nguyen Hoang Viet and J. Mandziuk, "Neural and fuzzy neural networks for natural gas consumption prediction," Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on, 2003, pp. 759-768. [12] R. Brown, P. Kharouf, Xin Feng, L. Piessens, and D. Nestor, "Development of feed-forward network models to predict gas consumption," Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, 1994, pp. 802-805 vol.2. [13] A. Khotanzad, H. Elragal, and T. Lu, "Combination of artificial neuralnetwork forecasters for prediction of natural gas consumption," Neural Networks, IEEE Transactions on, vol. 11, 2000, pp. 464-473. [14] L. Xu, W. Zhou, X. Li, and S. Tang, "Wet Gas Metering Using a Revised Venturi Mete and Soft-Computing Approximation Techniques," IEEE Transactions on Instrumentation and Measurement, vol. 60, Mar. 2011. [15] J. Fidalgo and M. Matos, "Forecasting Portugal Global Load with Artificial Neural Networks," Artificial Neural Networks - ICANN 2007, Springer Berlin / Heidelberg, 2007, pp. 728-737. [16] A. Bakirtzis, J. Theocharis, S. Kiartzis, and K. Satsios, "Short term load forecasting using fuzzy neural networks," Power Systems, IEEE Transactions on, vol. 10, 1995, pp. 1518-1524. [17] Z. Li, G. Zhang, D. Li, X. Liu, S. Mei, and J. Wu, "Neural network prediction of energy demand and supply in China," Proceedings of the Institution of Civil Engineers - Energy, vol. 160, 2007, pp. 145-149. [18] L.M. Saini, "Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks," Electric Power Systems Research, vol. 78, Jul. 2008, pp. 1302-1310. [19] B. Kermanshahi and H. Iwamiya, "Up to year 2020 load forecasting using neural nets," International Journal of Electrical Power & Energy Systems, vol. 24, Nov. 2002, pp. 789-797. [20] H. Hippert, C. Pedreira, and R. Souza, "Neural networks for short-term load forecasting: a review and evaluation," Power Systems, IEEE Transactions on, vol. 16, 2001, pp. 44-55. [21] J.J. Moré, "The Levenberg-Marquardt algorithm: Implementation and theory," Numerical Analysis, Springer Berlin / Heidelberg, 1978, pp. 105-116. [22] G. Welch and G. Bishop, An Introduction to the Kalman Filter, University of North Carolina Chapel Hill, 2006. [23] D.J.C. MacKay, "Bayesian Interpolation," Neural Computation, vol. 4, 1992, pp. 415-447. [24] M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: the RPROP algorithm," Neural Networks, 1993., IEEE International Conference on, 1993, pp. 586-591 vol.1. [25] P. Baldi, "Gradient descent learning algorithm overview: a general dynamical systems perspective," Neural Networks, IEEE Transactions on, vol. 6, 1995, pp. 182-195.