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.

中文翻译：

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用于建筑负荷预测的建筑能源数据噪声消除中应用的时频分析技术的比较：真实建筑案例研究


在时域和频域中分解信号的时频分析是建筑能源分析的重要支持技术，例如数据驱动的建筑负荷预测中的噪声消除。在比较各种时频分析技术，特别是离散小波变换（DWT）和经验模态分解（EMD）方面，文献中存在空白，以指导数据驱动中时频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，用于在负荷预测建模任务中对 13 种 DWT/EMD 技术与各种参数进行全面比较。以真实的校园建筑作为案例研究进行说明。还在用于构建负荷预测的各种数据驱动建模算法下对 DWT 和 EMD 技术进行了比较。案例研究的结果表明，使用消除噪声的能源数据训练的负载预测模型在未见过的数据下测试的平均准确度已提高到 9.6%。这项研究还表明，DWT/EMD 技术的有效性取决于用于负载预测建模的数据驱动算法和训练数据。因此，基于 DWT/EMD 的噪声消除需要定制选择和调整，以优化数据驱动的建筑负荷预测建模的性能。