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Electricity Forecasting for Small Scale Power System Using Artificial Neural Network

Khamis, M.F.I. and Baharudin , Z. and Hamid, N.H. and Abdullah, M. F. and Solahuddin, S. (2011) Electricity Forecasting for Small Scale Power System Using Artificial Neural Network. In: Fifth International Power Engineering and Optimization Conference (PEOCO2011), 6th - 7th June 2011, Shah Alam, Selangor Malaysia.

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Abstract

Short term load forecasting (STLF) method is the basis of efficient operation for power system. It has an important role in planning and operation of power system. In this paper, a practical STLF using artificial neural network method (ANN) for Gas District Cooling (GDC) power plant of Universiti Teknologi PETRONAS (UTP) is presented. As a sole customer of GDC power plant, the load data from 2006 till 2009 are gathered and utilized for model developments. The developed models forecast electricity load for one week ahead. The paper proposes a method of a multilayer perceptron neural network and it is trained and simulated by using MATLAB. The models have been tested with the actual load data and perform relatively good results.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Sustainable Building
ID Code:6743
Deposited By: Dr Zuhairi Baharudin
Deposited On:21 Nov 2011 06:29
Last Modified:19 Jan 2017 08:22

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