Medium Term Load Forecasting Using Statistical Feature Self Organizing Maps (SOM)


  • N.N. Atira Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Malaysia
  • I. Azmira Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Malaysia
  • Z.H. Bohari Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Malaysia
  • N.A. Zuhari Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Malaysia
  • N.F.M. Ghazali Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Malaysia


Artificial Neural Network, Load Forecasting, Medium Term, Self-Organizing Maps,


Load forecasting is an essential tool for power system activity and planning. With the increase in development and the expansion of power system, it is important for the electrical utility to make a decision in ensuring that there would be enough supply of electricity to deal with the increasing demand. This research presents the Medium Term Load Forecasting using the artificial neural networks: Kohonen’s Self-organizing Maps. The main purpose of this paper was to understand the ability of Self-Organizing Maps in forecasting the load demand and to train and test via SelfOrganizing Maps method using the selected features. Using data provided by the Global Energy Forecasting Competition (GEFCom2012), this paper focused on the missing data from the year 2005 and 2006 for the load forecasting. The loaded data were trained, tested, and forecasted using SOM Toolbox in MATLAB software. The accuracy of the forecasted data was determined by calculating the error of each forecasted data by comparing them with the actual data. Then, the Mean Absolute Percentage Error was computed to determine the accuracy of the results.


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How to Cite

Atira, N., Azmira, I., Bohari, Z., Zuhari, N., & Ghazali, N. (2019). Medium Term Load Forecasting Using Statistical Feature Self Organizing Maps (SOM). Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 11(2), 25–29. Retrieved from