Comparison of Time-Frequency-Analysis Techniques Applied in Building Energy Data Noise Cancellation for Building Load Forecasting: A Real-Building Case Study: Article No. 110592

Liang Zhang, Mahmoud Alahmad, Jin Wen

Research output: Contribution to journalArticlepeer-review

27 Scopus Citations

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.
Original languageAmerican English
Number of pages11
JournalEnergy and Buildings
Volume231
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5500-79372

Keywords

  • building load forecasting
  • data-driven modeling
  • discrete wavelet transform
  • empirical mode decomposition
  • noise cancellation
  • time-frequency analysis

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