Abstract
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.
Highlights
A large proportion of electricity is generated using fossil fuels in the current energy structure and induced air pollution are observed globally
Since the power output of wind farms is directly related to the wind speeds, accurate wind speed forecasting is highly desired in the wind energy industry
WORK In this paper, we proposed a convolutional neural network (CNN)-based model using transfer learning to address the issue that some newly constructed farms do not have sufficient historical wind speed data to train a well-performed model
Summary
A large proportion of electricity is generated using fossil fuels in the current energy structure and induced air pollution are observed globally. Owning the advantages of less pollution, high efficiency and environmental friendliness, wind energy has been considered as one of the most important renewable energy sources recently [5], and numerous wind farms are being constructed all over the world. Since the power output of wind farms is directly related to the wind speeds, accurate wind speed forecasting is highly desired in the wind energy industry. Various methods were proposed to forecast the wind speed, and they can be generally classified into the following five categories [6], [7]: (a) physical models, (b) statistical models, (c) spatial correlation models, (d) artificial intelligence models, (e) hybrid forecasting models
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