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Development of Neural Network Prediction Model of Energy Consumption

Authors: Rosdiazli Ibrahim, Maryam Jamela Ismail, Idris Ismail


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.

Keywords: levenberg-marquardt, Energy Prediction, Multilayer Feedforward, Root Mean Square Error (RMSE)

Digital Object Identifier (DOI):

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