电器与能效管理技术Issue(11)：42-50,9.DOI:10.16628/j.cnki.2095-8188.2025.11.006
基于ADKDE-LSTM的变电站短期负荷功率区间预测研究
Research on Short-Term Load Power Interval Prediction for Substations Based on ADKDE-LSTM
摘要
Abstract
To address the challenges of poor nonlinear adaptability and inaccurate interval estimation in substation short-term load prediction,an interval prediction method integrating adaptive diffusion kernel density estimation(ADKDE)with long short-term memory networks(LSTM)is proposed.Historical load and meteorological data are fused,where ADKDE method analyzes error distributions and LSTM network temporal features to construct prediction intervals at a 95％confidence level.Experimental results based on a 220 kV substation dataset demonstrate that the proposed model achieves an average prediction interval coverage probability(PICP)of 0.914 across four datasets,while reducing the prediction interval average width(PIAW)by20％-30％compared to the comparison models.The proposed method effectively quantifies load uncertainty,providing reliable interval predictions to support power grid planning.关键词
数据融合/ADKDE-LSTM/区间负荷预测/自适应扩散核密度估计Key words
data fusion/ADKDE-LSTM/interval load prediction/adaptive diffusion kernel density estimation(ADKDE)分类
信息技术与安全科学引用本文复制引用
包育德,邱润韬,许博智..基于ADKDE-LSTM的变电站短期负荷功率区间预测研究[J].电器与能效管理技术,2025,(11):42-50,9.基金项目
国家自然科学基金资助项目(52307080) （52307080）
