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首页> 外文期刊>Energy and Buildings >Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study
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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

机译：建筑能量数据噪声消除适用于建筑负荷预测的时频分析技术的比较：实木案例研究

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摘要

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.
机译：在时间和频域中分解信号的时频分析是用于建立能量分析的重要支持技术，例如数据驱动建筑负荷预测中的噪声消除。文献中存在与比较各种时间频率分析技术，特别是离散小波变换（DWT）和经验模式分解（EMD）相关的差距，以指导数据驱动中的时间频率分析技术的选择和调整建筑负荷预测。本文提供了一个框架，可以在负载预测建模任务中具有各种参数的十三个DWT / EMD技术进行全面比较。真正的校园大楼被用作例证案例研究。在各种数据驱动建模算法下也比较DWT和EMD技术，用于建立负载预测。在案例研究中的结果表明，在看不见的数据下，噪声消除能量数据培训的负荷预测模型的培训模型将其准确性提高到9.6％的9.6％。本研究还表明，DWT / EMD技术的有效性取决于用于负载预测建模和训练数据的数据驱动算法。因此，基于DWT / EMD的噪声消除需要定制选择和调整，以优化它们对数据驱动建筑负荷预测建模的性能。 （c）2020 Elsevier B.v.保留所有权利。

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