Abstract
As a clean and renewable energy, wind power plays an increasingly significant role in the power system. And wind power prediction is crucial for the operation planning and cost control of power plants. In wind power prediction, the wind-related information such as wind speed, wind direction, air pressure and temperature will affect the accuracy of prediction. However, most of the existing models either use only one kind of information or fail to effectively integrate a variety of information. Considering these problems, a new convolutional neural network model is proposed by integrating multiple information based on spatio-temporal features, called FB-CNN (Feature Block CNN). Obtain the output of each convolution layers in the whole neural network, then integrate these outputs, the prediction results are obtained through the full connection layers in the end. Compared with the current convolutional network with the highest accuracy, the proposed FB-CNN combining various features can excellently fit the actual change of wind power data. And the Mean Square Errors (MSE) on two data sets are reduced by 9.53% and 7.13% respectively.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant No. 61877043, 61976155), Major Scientific and Technological Projects for A New Generation of Artificial Intelligence of Tianjin (Grant No. 18ZXZNSY00300), and Key Project for Science and Technology Support from Key R&D Program of Tianjin (Grant No. 18YFZCGX00960).
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Chen, S. et al. (2019). A Wind Power Prediction Method Based on Deep Convolutional Network with Multiple Features. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_22
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DOI: https://doi.org/10.1007/978-3-030-36808-1_22
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