Noise Level Estimation in Energy Internet Based on Artificial Neural Network

Abstract

The massive data produced in energy Internet (EI) faces the challenge involved by disturbance of noise, especially the measurement noise and noise attacks by hackers. Traditional noise estimation mainly focuses on the non-white additional or multiplicative noise estimation with definite wave types, and others, e.g., the phase noise in direction-of-arrival of antenna array, or the frequency bias in orthogonal frequency division multiplexing, etc., which usually use traditional estimation technologies. In this paper, a novel noise level estimation algorithm is proposed based on artificial neural network prediction. In an EI scenario, the proposed algorithm only needs to know the noise amplitude of historical data when training the model. At the time of execution, the algorithm estimates the noise level of existing data based on the latest noisy historical data, and the algorithm can be used for most noise types. Through numerical simulations, we found that its performance is apparently improved compared to the low passband filter method.