Comparison of empirical modal decomposition class techniques applied in noise cancellation for building heating consumption prediction based on time-frequency analysis

Abstract

Empirical Modal Decomposition (EMD), and improved or modified techniques derived from EMD, collectively referred to as Empirical Modal Decomposition class (EMDC) techniques. EMDC techniques have a wide range of applications in building energy analysis, especially time–frequency analysis based noise cancellation in data-driven building energy prediction. However, there is a gap in the literature related to the choice of EMDC techniques in data-driven models. This paper provides a framework for a comprehensive comparison of EMD, Ensemble Empirical Mode Decomposition (EEMD), Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) techniques for building heat consumption prediction modeling. A real building is used as an example to compare the noise cancellation potential of these techniques and the prediction accuracy under various data-driven models. The results demonstrated that noise cancellation using the EMDC techniques significantly improves Signal-Noise Ratio, regularity, and consistency with the original signal trend. The prediction models trained using the noise-cancelled data have the Root Mean Squared Error and the Mean Absolute Error reductions of 22.5 % and 31.3 % on average, respectively. Meanwhile, the predicted signals of the models inherit the noise cancellation benefits of the noise-cancelled training data.