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Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction
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Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction
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Journal Article
Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction
2024
Overview
The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid. In this work, a deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet. The dataset was collected in Natal. The six algorithms employed predicted the value of wind power for the coming day. Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics. The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error.
Publisher
Taylor & Francis,Taylor & Francis Ltd,Taylor & Francis Group
