电力系统及其自动化学报2025，Vol.37Issue(2)：1-9,9.DOI:10.19635/j.cnki.csu-epsa.001406
计及动态时空相关性的多风电场短期功率预测
Short-term Power Forecasting of Multiple Wind Farms Considering Dynamic Spatial-temporal Correlations
摘要
Abstract
Aimedat the complex and dynamic spatial-temporal correlations between the output of multiple wind farms in the same region,a short-term power forecasting model of multiple wind farms based on the attention based spatial-tem-poral synchronous graph convolutional networks is proposed.First,an attention mechanism is introduced to quantify the impact of weather features on wind power,the local spatial-temporal graphs of wind power are constructed in three adja-cent time steps,and the local spatial-temporal features are extracted through convolutional calculation.Then,the input window's overall spatial-temporal features are aggregated using the spatial-temporal synchronous graph convolutional layer.Finally,the power prediction results of multiple wind farms in the future are output through nonlinear mapping.The results of an actual example show that the proposed modelcan improve the day-ahead power forecasting accuracy of-multiple wind farms by 2.10%—13.94%through learning the dynamic spatial-temporal evolution law of wind power un-der different weather conditions.关键词
深度学习/风电功率/相关性/时空同步图卷积网络/功率预测Key words
deep learning/wind power/correlation/spatial-temporal synchronous graph convolutional networks/power forecasting分类
信息技术与安全科学引用本文复制引用
李丹,黄烽云,杨帆,唐建,罗娇娇,方泽仁..计及动态时空相关性的多风电场短期功率预测[J].电力系统及其自动化学报,2025,37(2):1-9,9.基金项目
国家自然科学基金资助项目(51807109). （51807109）
