A HIERARCHICAL SELF - ORGANIZING MAP MODEL IN SHORT - TERM LOADFORECASTINGOt

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets | one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them. I INTRODUCTION With power systems growth and the increase in their complexity, many factors have become innu-ential to the electric power generation and consumption (load management, energy exchange, spot pricing, independent power producers, non-conventional energy, generation units, etc.); therefore, the forecasting process has become even more complex, and more accurate forecasts are needed. The relationship between the load and its exogenous factors is complex and non-linear, making it quite diicult to model through conventional techniques, such as time series and linear regression analysis. Besides not giving the required precision, most of the traditional techniques are not robust enough. They fail to give accurate forecasts when quick weather changes occur. Other problems include noise immunity, portability and maintenance 1]. Neural networks (NNs) have succeeded in several power system problems, such as: planning; control; analysis; protection; design; load forecasting; security analysis; and fault diagnosis. The last three are the most popular 2]. The NN ability in mapping complex non-linear relationships is responsible for the growing number of its application to the short-term load forecasting (STLF) 3, 4, 5, 6]. Several electric utilities over the world have been applying NNs for load forecasting in an experimental or operational basis 1, 2, 4]. So far, the great majority of proposals on the application of NNs to STLF use the multilayer perceptron trained with error backpropagation. Besides the high computational burden for supervised training, multilayer perceptrons do not have a good ability to detect data outside the