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Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study
Journal article   Open access   Peer reviewed

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Liang Zhang, Mahmoud Alahmad and Jin Wen
Energy and buildings, v 231, 110592
15 Jan 2021
DOI: https://doi.org/10.1016/j.enbuild.2020.110592
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.enbuild.2020.110592View
Accepted (AM)Open Access (Publisher-Specific),  Open

Abstract

Building load forecasting Data-driven modeling Discrete wavelet transform Empirical mode decomposition Noise cancellation Time–frequency analysis
•Compare multiple time–frequency-analysis techniques for building load forecasting.•Including discrete wavelet transform (DWT) and empirical mode decomposition (EMD).•A real-building case study is conducted under a developed comparison framework.•An average improvement of 9.6% of model accuracy is observed when using DWT and EMD.•The tuning of DWT and EMD parameters are essential to optimize model performance. Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

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10 Record Views
25 citations in Web of Science
31 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#7 Affordable and Clean Energy

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Collaboration types
Domestic collaboration
Web of Science research areas
Construction & Building Technology
Energy & Fuels
Engineering, Civil
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