Accurate Real-Time Wind Power Forecasting with TTP-Net: Leveraging Temporal and Spatial Modeling for Enhanced Prediction
DOI:
https://doi.org/10.13052/dgaej2156-3306.4017Keywords:
Deep learning, multi-source data fusion, deep belief network (DBN), feature extraction, image classification, machine learningAbstract
This paper proposes the TTP-Net model, which integrates the Temporal Convolutional Network (TCN), Transformer, and Particle Swarm Optimization (PSO) algorithm for realtime wind power forecasting. The TCN module efficiently captures short-term dependency features, the Transformer module excels at modeling long-term dependencies, and PSO significantly enhances the model’s performance and generalization through global optimization. Experimental results on two public datasets validate the superior performance of the model. On the Wind Turbine Data dataset, TTP-Net achieved MSE of 0.098, MAPE of 6.85%, and of 0.965. On the KDD Cup 2022 Dataset, the MSE and MAPE were 0.126 and 8.40%, respectively, with an R2 of 0.960, significantly outperforming other baseline models. This study not only validates the effectiveness of TTP-Net in wind power forecasting but also provides an innovative approach for modeling complex time-series data, offering significant implications for improving wind power forecasting accuracy and optimizing dispatch.
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