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Ultra-short-term wind power forecasting method based on multi-variable joint extraction of spatial-temporal features
Accurate and reliable wind power forecasting is imperative for wind power stations' stable and efficient operation. Information such as wind speed and wind direction in the same wind field has spatial-temporal differences. Considering the spatial-temporal changes in wind fields can improve model prediction accuracy. However, existing methods suffer from limited ability to capture correlation features among variables, information loss in spatial-temporal feature extraction, and neglect short-term temporal features. This paper introduces a novel ultra-short-term wind power forecasting method based on the combination of a deep separable convolutional neural network (DSCNN) and long- and short-term time-series network (LSTNet), incorporating maximum information coefficient (MIC) to realize multi-variable joint extraction of spatial-temporal features. The method utilizes MIC to jointly analyze and process the multi-variate variables before spatial-temporal feature extraction to avoid information redundancy. The spatial features between input variables and wind power are extracted by deep convolution and pointwise convolution in DSCNN. Then, a convolutional neural network and gated recurrent unit in LSTNet are combined to capture long-term and short-term temporal features. In addition, an autoregressive module is employed to accept features extracted by MIC to enhance the model's learning of temporal features. Based on real datasets, the performance of models is validated through comprehensive evaluation experiments such as comparison experiments, ablation experiments, and interval prediction methods. The results show that the proposed method reduces mean absolute error by up to 4.66% and provides more accurate prediction intervals, verifying the accuracy and effectiveness of the proposed method.
Ultra-short-term wind power forecasting method based on multi-variable joint extraction of spatial-temporal features
Accurate and reliable wind power forecasting is imperative for wind power stations' stable and efficient operation. Information such as wind speed and wind direction in the same wind field has spatial-temporal differences. Considering the spatial-temporal changes in wind fields can improve model prediction accuracy. However, existing methods suffer from limited ability to capture correlation features among variables, information loss in spatial-temporal feature extraction, and neglect short-term temporal features. This paper introduces a novel ultra-short-term wind power forecasting method based on the combination of a deep separable convolutional neural network (DSCNN) and long- and short-term time-series network (LSTNet), incorporating maximum information coefficient (MIC) to realize multi-variable joint extraction of spatial-temporal features. The method utilizes MIC to jointly analyze and process the multi-variate variables before spatial-temporal feature extraction to avoid information redundancy. The spatial features between input variables and wind power are extracted by deep convolution and pointwise convolution in DSCNN. Then, a convolutional neural network and gated recurrent unit in LSTNet are combined to capture long-term and short-term temporal features. In addition, an autoregressive module is employed to accept features extracted by MIC to enhance the model's learning of temporal features. Based on real datasets, the performance of models is validated through comprehensive evaluation experiments such as comparison experiments, ablation experiments, and interval prediction methods. The results show that the proposed method reduces mean absolute error by up to 4.66% and provides more accurate prediction intervals, verifying the accuracy and effectiveness of the proposed method.
Ultra-short-term wind power forecasting method based on multi-variable joint extraction of spatial-temporal features
Lei, Zhengling (author) / Wang, Caiyan (author) / Liu, Tao (author) / Wang, Fang (author) / Xu, Jingxiang (author) / Yao, Guoquan (author)
2024-07-01
17 pages
Article (Journal)
Electronic Resource
English
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