An Enhanced Very Short-Term Load Forecasting Scheme Based on Activation Function

In this paper, we proposed a framework for accurate load forecasting which consists of two stage processes; feature engineering and classification. Feature engineering consists of feature selection and extraction. Relevant features are selected by combining Decision Tree (DT) and Recursive Feature Elimination (RFE) techniques. Moreover, Linear Discriminant Analysis (LDA) technique is used to further improve the selected features in terms of redundancy and dimensionality reduction. To forecast the electricity load, an improved feedforward multilayer perceptron classifier is applied. Half a day ahead forecasting experiment is conducted by using the proposed framework. At the end, forecasting performance is examined by using Root Mean Square Error, Mean Absolute Error, Mean Square Error and Mean Absolute Percentage Error. Simulation results show higher accuracy of our proposed scheme with 1.397% as compared to the existing scheme.