电器与能效管理技术Issue(11)：26-31,58,7.DOI:10.16628/j.cnki.2095-8188.2025.11.004
融合稀疏编码器和改进蚁群算法的电力系统负荷预测算法设计
Design of Power System Load Forecasting Algorithm Integrating Sparse Encoder and Improved Ant Colony Algorithm
史盛亮1
作者信息
- 1. 国网河北省电力有限公司石家庄供电分公司,河北石家庄 050011
- 折叠
摘要
Abstract
To address the issue that the traditional load forecasting algorithm is insufficient in handling the complex nonlinear relationship and uncertain factors in the grid-connected environment of new energy,a new intelligent load forecasting model for power system based on a sparse encoder and an improved ant colony algorithm is proposed.Firstly,a sparse encoder with regularization constraints is constructed to effectively extract the deep features of load data and enhance the generalization ability of the encoder.Then,the dynamic domain search mechanism is introduced to form an improved ant colony algorithm,which significantly improves the global search capability and convergence efficiency in complex scenarios.Finally,the model is trained and compared based on the historical load data.Experimental results show that the load prediction accuracy of the proposed algorithm can reach 97.9％,which is about 20％higher than that of traditional methods,providing effective technical support for smart grid scheduling and sustainable development of energy Internet.关键词
负荷预测/稀疏编码器/改进蚁群算法/历史数据/实验对比Key words
load forecasting/sparse encoder/improve ant colony algorithm/historical data/experimental comparison分类
信息技术与安全科学引用本文复制引用
史盛亮..融合稀疏编码器和改进蚁群算法的电力系统负荷预测算法设计[J].电器与能效管理技术,2025,(11):26-31,58,7.
