A contrastive learning-based framework for wind power forecast

Nanyang Zhu

doi:10.1016/j.eswa.2023.120619

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A contrastive learning-based framework for wind power forecast

2023-11-01

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Nanyang Zhu

张

张凯锋 (Kaifeng Zhang) *

DOI：10.1016/j.eswa.2023.120619

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Abstract

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The feature representation of wind power sequences is crucial in the modeling of short-tern wind power forecast, but the existing feature representation methods mostly depend on the end-to-end model based on supervised learning, ignoring the superiority of self-supervised learning in the feature distribution of similar and non-similar sequences in the feature space. Therefore, this paper applies contrastive learning that is a type of self-supervised learning for the feature representation of wind power sequences, and proposes a two-stage framework for the wind power forecast (CLFNet). Specifically, the proposed framework is composed of the pre-training stage and the regression stage. The pre-training stage uses contrastive learning to supply a support for the optimization of the parameters of the network architectures, allowing the trend consistency sequences to obtain more correlated feature representations; the regression stage takes a projection layer, connected after the well-trained network architecture in the pre-training stage, to output wind power, and then fine-tunes the parameter of this network architecture using MSE loss. The proposed framework is applied for various network architectures including LSTM, CNN, and Transformer, and the results of RMSE, MAE, R2 confirm that the proposed framework is widely suitable for various basis network architectures and can outperform the initial basis network architectures by a mean increase rate of 8.94%, 8.5%, and 8.5%, respectively, among forecast steps. In addition, compared the proposed method with state-of-the-art schemes in the wind power forecasting system, the results show that the proposed framework can generate improved performance with accuracy and robustness.

Keywords:

Contrastive learning

Similar and non -similar sequences

Feature representation

Wind power forecast