Flexible electricity price forecasting by switching mother wavelets based on wavelet transform and Long Short-Term Memory
Flexible electricity price forecasting by switching mother wavelets based on wavelet transform and Long Short-Term Memory
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DOI：
10.1016/j.egyai.2022.100192
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发表时间：
2022-11-01
期刊：
影响因子：
--
通讯作者：
Onoye, Takao
中科院分区：
文献类型：
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作者：
Iwabuchi, Koki;Kato, Kenshiro;Onoye, Takao
在动态电价模式下，稳定准确的需求侧电价预测对于有效的能源管理至关重要。我们开发了一种新的电价预测模型，可提供持续准确的预测。基本预测模型首先对时间序列进行小波分解，然后利用长短期记忆进行预测。以往使用该模型的研究都是在不改变母小波的情况下，以相同的方式分解时间序列。然而，这使得难以对每天或季节性变化的时间序列的变化做出反应。因此，我们周期性地切换母小波，即，灵活变换时间序列分解方法，实现稳定、高精度的电价预测。在实验中，与使用固定母小波的预测相比，该模型将预测精度提高了42.8%。实验结果表明，所提出的灵活的预测方法可以始终提供高精度的预测。
Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential for efficient energy management. We have developed a new electricity price forecasting model that provides consistently accurate forecasts. The base prediction model decomposes the time series using wavelet transform and then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposed time series in the same way without changing the mother wavelet. However, this makes it difficult to respond to changes 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 price forecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to prediction with a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method can consistently provide highly accurate forecasts.