Abstract
In substations of the electric power systems, it is necessary to predict load power change accurately to improve their operating efficiency and reliability. This paper investigates an application of the Self-Organizing Map (SOM), an effective technique for classification of multi-dimensional data, to the prediction of load power change at the local substations. The SOMs are constructed by learning data of apparent, effective and reactive powers. Using the SOMs, load power patterns during a day are predicted well separately. To improve the prediction accuracy, the date of maximum and minimum atmospheric temperature is added to the above data of powers. In a short-term prediction, the powers during each four hours in a day are predicted by the SOM learned by the data during each 12 hours This paper also proves the effectiveness of our method with numerical results.
