Enhancing the performance of smart electrical grids using data mining and fuzzy inference engine

Abstract

This paper is about enhancing the smart grid by proposing a new hybrid feature-selection method called feature selection-based ranking (FSBR). In general, feature selection is to exclude non-promising features out from the collected data at Fog. This could be achieved using filter methods, wrapper methods, or a hybrid. Our proposed method consists of two phases: filter and wrapper phases. In the filter phase, the whole data go through different ranking techniques (i.e., relative weight ranking, effectiveness ranking, and information gain ranking) The results of these ranks are sent to a fuzzy inference engine to generate the final ranks. In the wrapper phase, data is being selected based on the final ranks and passed on three different classifiers (i.e., Naive Bayes, Support Vector Machine, and neural network) to select the best set of the features based on the performance of the classifiers. This process can enhance the smart grid by reducing the amount of data being sent to the cloud, decreasing computation time, and decreasing data complexity. Thus, the FSBR methodology enables the user load forecasting (ULF) to take a fast decision, the fast reaction in short-term load forecasting, and to provide a high prediction accuracy. The authors explain the suggested approach via numerical examples. Two datasets are used in the applied experiments. The first dataset reported that the proposed method was compared with six other methods, and the proposed method was represented the best accuracy of 91%. The second data set, the generalization data set, reported 90% accuracy of the proposed method compared to fourteen different methods.