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: UNSPECIFIED.

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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. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 13; Conference of 2011 5th International Power Engineering and Optimization Conference, PEOCO 2011 ; Conference Date: 6 June 2011 Through 7 June 2011; Conference Code:86270
Uncontrolled Keywords: Artificial Neural Network; Developed model; District cooling; Electric power; Electricity load; Load data; Model development; Multi-layer perceptron neural networks; Multilayer Perceptron; Operation of power system; PETRONAS; Short term load forecasting; Small scale, District heating; Electric power systems; Electricity; Forecasting; Multilayers; Neural networks; Optimization; Power plants; Power transmission, Electric load forecasting
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:50
Last Modified: 09 Nov 2023 15:50
URI: https://khub.utp.edu.my/scholars/id/eprint/1914

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