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We can see from Fig. 5a that the preprocessed data obtained by linear clustering method are much smoother than the original data in Fig. 4a, making them more suitable for time series modelling.
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The large substation load dataset, comprising load time series with annual interval, is firstly clustered by the proposed linear clustering method.
A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling.
In this paper, we propose a data-driven linear clustering method to solve the long-term system load forecasting problem caused by load fluctuations in some developed cities.
2) A novel linear clustering method is proposed to put complementary substation load curves into the same cluster.
Time variance is implemented by linear cluster movement.
In the proposed linear clustering preprocessing method, the clustering criterion in Step 4 in Sect.
In this paper, a data-driven linear clustering (DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities.
The non-linear unsupervised clustering method, t-SNE [78], has also been widely used in scRNA-seq samples [42, 74].
The training of these models is conducted by an evolving clustering method (adding new local linear models on demand) and a local (weighted) least squares estimation of the consequent parameters, and connected with a wavelength (dimensionality) reduction mechanism.
In this paper, we try to address these problems by developing a clustering method that can group data points with both linear and nonlinear associations.
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