To address the uncertainty caused by integrating wind power into the electricity grid, accurate wind speed forecasting is highly desired. However, historical wind speed data of new wind farms may be insufficient for training a well-performed forecasting model. To address this issue, short-term wind speed forecasting with convolutional neural network (CNN) based on information of neighboring wind farms is studied in this paper. In the proposed approach, the CNN is employed to migrate the intrinsic features of wind speed changes to newly built wind farms. To evaluate the performance of the proposed approach, wind speed data collected from three wind farms in China is utilized and multi-step-ahead forecasting is considered. The computational results prove the proposed approach outperforms benchmarking methods Support Vector Regression, Kernel Ridge Regression, and CNN by only considering data of the target wind farm. INDEX TERMS Wind speed forecasting, transfer learning, neural networks, wind energy.

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This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.

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