A noisy data regression model based on general regression neural networks

Analysis of noisy data gathered from measurement devices is challenging in the power grid. In this study, an effective noisy data regression approach based on general regression neural networks (GRNN) is employed to deal with the problem for remote terminal units (RTU) in power SCADA systems. Experimental results show the proposed model is able to handle noisy data for practical applications, and has good performance in removing the unintended changes to the original data.