CEEMD-FFT-BiLSTM based model for power line loss prediction

Abstract

Abstract Line loss rate in transmission lines is an important and inevitable metric to directly affects the economy and efficiency of power supply, which exhibits characteristics of nonlinearity, non-stationary and randomness. To improve the prediction performance of line loss rate, in this paper, a prediction model for line loss based on complete ensemble empirical mode decomposition (CEEMD), fast Fourier transform (FFT), and bidirectional long short-term memory network (BiLSTM) is proposed, denoted as CEEMD-FFT-BiLSTM. The model first performs CEEMD to decompose raw signals into various intrinsic mode functions. FFT then transforms them from the time domain to the frequency domain. BiLSTM is used to learn the long-term dependence, extract features from the frequency domain, and finally achieve a prediction. Experimental results are provided to show that the prediction accuracy of the proposed prediction method is substantially improved, as compared to other methods such as BiLSTM and FFT-BiLSTM. Therefore, the proposed prediction model has a good predictive ability and can effectively improve the prediction accuracy.