Residential power consumption forecasting in the smart grid using ANFIS system

This paper offers a form of filtration based on moving average filter and KNN imputation method, for pre-processing hourly electricity load data for Short-Term Load Forecasting (STLF). The STLF is developed by the Adaptive Network Based Fuzzy Inference System (ANFIS). There is a lack of data pre-processing related to load forecasting, especially STLF. Unlike previous studies, to enhance the accuracy of forecasting, the current study considers data pre-processing as well. We propose a machine learning model using the ANFIS to forecast short-term load. The electricity load data are used for training and testing the proposed model. The predictor's outputs show that the model able to forecast electricity load in an accurate way. We believe the proposed pre-processing method can be used in the future studies to increase forecast accuracy.