期刊
ENERGY AND BUILDINGS
卷 231, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2020.110592
关键词
Time-frequency analysis; Empirical mode decomposition; Discrete wavelet transform; Building load forecasting; Noise cancellation; Data-driven modeling
资金
- National Renewable Energy Laboratory
- U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office
This study compares thirteen DWT/EMD techniques with various parameters in building load forecasting modeling, showing an average accuracy increase of 9.6% when using noise-cancelled energy data. The effectiveness of DWT/EMD techniques depends on data-driven algorithms and training data, suggesting customized selection and tuning for optimal performance in data-driven building load forecasting modeling.
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. (C) 2020 Elsevier B.V. All rights reserved.
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