风能作为一种无污染可再生能源，风力发电的比例在全球范围内逐年增加。针对风力发电存在出力波动大，从而导致电网电力不稳定的问题，提出基于集成多尺度长短时记忆网络（LSTM， long short-term memory）的短时风功率预测模型。利用LSTM对序列数据的特殊处理能力，集成多个基预测模型对不同尺度时间数据的预测结果，共同进行短时风功率预测。风功率的精确预测有利于电力资源的全面掌控和调度。采用中国东北地区风力发电真实数据集对模型进行验证，结果证实研究方法预测精度较高，有很好的稳定性。

Wind energy is a pollution-free renewable energy, and the proportion of wind power is increasing year by year globally. In view of the large fluctuations in the output of wind power generation, which leads to the instability of the grid power, a short-term wind power prediction model based on integrated multi-scale long short-term memory (LSTM) is proposed. By using LSTM’s special processing capabilities for sequence data, combined with the information contained in different scales’ time data, to predict short-term wind power after integration. It is conducive to comprehensive control and dispatch of power resources. Experiments are conducted on the real data set of wind power generation in the northeast of our country, and the results prove that the method in this paper has high prediction accuracy and strong stability.

TP391.4

国家自然科学基金面上项目资助（62076047）；国家电网有限公司科技项目资助（52992620003L）。