Publications
2021
Zhang, Liang; Alahmad, Mahmoud; Wen, Jin
In: Energy and Buildings, vol. 231, pp. 110592, 2021, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: Building load forecasting, Data-driven modeling, Discrete wavelet transform, Empirical mode decomposition, Noise cancellation, Time–frequency analysis
@article{ZHANG2021110592,
title = {Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study},
author = {Liang Zhang and Mahmoud Alahmad and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0378778820333788},
doi = {https://doi.org/10.1016/j.enbuild.2020.110592},
issn = {0378-7788},
year = {2021},
date = {2021-01-01},
journal = {Energy and Buildings},
volume = {231},
pages = {110592},
abstract = {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.},
keywords = {Building load forecasting, Data-driven modeling, Discrete wavelet transform, Empirical mode decomposition, Noise cancellation, Time–frequency analysis},
pubstate = {published},
tppubtype = {article}
}
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.