用于短期风功率预测的历史数据深度迁移模型

A short-term wind power prediction model based on deep transfer learning of historical data

随着全球化石燃料短缺日益严重,可再生能源的开发与利用愈发得到重视。风能是被广泛使用的清洁能源之一,在生产工作中,风力发电作为风能的主要利用形式,需要对其功率进行预测。依托风场日常记录的历史数据,传统学习模型可对风功率进行短期预测,但往往仅使用自己域内的历史数据作为分析对象,该类算法导致结果片面,局限性大,不能有效使用类数据中的隐含联系,抑制原始数据缺失或异常值引起的模型性能下降问题。笔者设计一种基于历史数据深度迁移的短期风功率预测模型。首先,使用带降噪处理的自动编码机构建深度神经网络模型。其次,应用深度迁移方法共享隐藏层,挖掘特征之间的隐含联系。最后,从具有相似特征和地理位置的风场数据中迁移重要知识,提高模型准确率和可靠性。实验结果表明,研究方法较之未使用迁移的方法更充分利用现有数据,预测准确率显著提高。

With more and more serious global shortage of fossil fuels, the development and utilization of renewable energy has attracted more and more attention. Wind energy is one of the most widely used clean energy sources. As the main utilization form of wind energy, wind power needs to be predicted in the production work, which can be done in the short term based on the historical data recorded in daily wind field. However, the existing methods often only use the historical data in their own domain, resulting in one-sided results and large limitations. They fail to effectively use the implicit connections in the data, and are unable to suppress the model performance degradation caused by the loss of original data or outliers. To address these challenges, this paper proposes a short-term wind power prediction model based on deep migration of historical data. Firstly, the deep neural network model is built by using the automatic coding mechanism with noise reduction processing. The hidden layer is then shared by the deep migration method, and the hidden links between features are mined. Finally, the important knowledge is transferred from the wind field data with similar features and geographical locations, so as to improve the accuracy and reliability of the model. The experimental results show that the proposed method can make full use of the existing data and improve the prediction accuracy significantly.