LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

• Main Authors: HUSSEIN A. ABDULQADER, SWAROOP R.
• Format: Article
• Language: English
• Published: Taylor's University 2012-08-01
• Series: Journal of Engineering Science and Technology
• Subjects: Load forecasting; Neural network; Power system; Back propagation; Energy consumption
• Online Access: http://jestec.taylors.edu.my/Vol%207%20Issue%204%20August%2012/Vol_7_4_498-504_SWAROOP%20R.pdf

Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.