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The effect of dimensionality reduction methods in short-term load forecasting performance

The number and types of inputs of the prediction system is crucial to a load forecasting task. In many cases, the number of inputs is increased, a fact that may lead to poor forecasting accuracy. The scope of the present paper is to examine the effect of dimensionality reduction methods in the forecasting performance of Feed Forward Neural Networks (FFNN). The test case refers to the day-ahead forecasting of the loads connected to a high-to-medium voltage transformer of the transmission system of Greece.