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1篇 您的检索式：作者名="Daichi Watari"

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		题名	作者	年代	出处	被引量
	1	Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and Long Short-Term Memory显示文摘Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.	Koki Iwabuchi Kenshiro Kato Daichi Watari Ittetsu Taniguchi Francky Catthoor Elham Shirazi Takao Onoye	2022	Energy and AI2022，10，4：	0

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