A Tidal Current Speed Forecasting Model based on Multi-Periodicity Learning

• URL: http://arxiv.org/abs/2410.09718v2
• Date: Tue, 04 Feb 2025 13:44:14 GMT
• Title: A Tidal Current Speed Forecasting Model based on Multi-Periodicity Learning
• Authors: Tengfei Cheng, Yangdi Huang, Yunxuan Dong,
• Abstract summary: The penetration of tidal energy in the electrical grid depends on the accuracy of tidal current speed forecasting. We propose the Wavelet-Enhanced Convolutional Network (WCN) to learn multi-periodicity. Compared with benchmarks, the proposed framework reduces the mean absolute error and mean square error in 10-step forecasting by, at most, 90.36% and 97.56%, respectively.
• Score: 1.1060425537315088
• License:
• Abstract: Tidal energy is one of the key components in increasing the penetration rate of renewable energy. The penetration of tidal energy in the electrical grid depends on the accuracy of tidal current speed forecasting. Modeling inaccuracies hinder forecast accuracy. Previous research has primarily used physical models to forecast tidal current speed. However, tidal current variations influenced by the orbital periods of celestial bodies make accurate physical modeling challenging. Researching the multi-periodicity of tides is crucial for accurately forecasting tidal current speed. In this article, we propose the Wavelet-Enhanced Convolutional Network (WCN) to learn multi-periodicity. The framework embeds intra-period and inter-period variations of one-dimensional tidal current data into the rows and columns of a two-dimensional tensor. Then, the two-dimensional variations of the sequence can be processed by convolutional kernels. We integrate a time-frequency analysis method into the framework to further address local periodic features. Additionally, to enhance the framework's stability, we optimize the framework's hyperparameters with the Tree-structured Parzen Estimator algorithm. The proposed framework avoids the lack of learning multi-periodicity. Compared with benchmarks, the proposed framework reduces the mean absolute error and mean square error in 10-step forecasting by, at most, 90.36% and 97.56%, respectively.

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