NARX: Contribution-factor-based short-term multinodal load forecasting for smart grid

摘要

A novel multinodal load forecasting method is presented in this paper, which uses smart metered data available from a real-life distribution grid at the NIT Patna campus. Because of the dynamic nature of the load, individual loads need to be predicted simultaneously so that they represent the load of the same instant. The proposed method uses single-stage multinodal forecasting with and without the load contribution factor (LCF), thus reducing the complexity compared to existing two-stage multinodal forecasting methods while improving the forecasting accuracy. It utilizes a nonlinear autoregressive neural network model with exogenous input (NARX-NN), which uses its own predicted output as an input during forecasting; this improves the accuracy of the model and makes it less dependent on external input data compared to other variations of NN. The experimental results show that the proposed method outperforms the existing approaches for multinodal load forecasting of the practical distribution system under consideration. Under different input dataset scenarios, the average mean absolute percentage error (MAPE) of the proposed model is 1.44, which represents the best forecasting performance among the competing models.