Abstract
Knowledge about residential load profile is a key information to smart grid allowing to control and minimize residential energy consumption. This paper presents a method to nonlinear residential load identification based on Artificial Neural Networks (ANN) and Principal Component Analysis (PCA). The automatic characterization, monitoring and control of loads is a main requirement to smart homes. The proposed method uses ANNs as pattern classifier and PCA is applied to choose the number of features considered in the classifier, thus reducing system complexity. An experimental test bed composed by different lighting loads has been assembled in order to validate the method that attains a 85 % of correct classification of loads.
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