一种基于改进CNN-LSTM预测与可调相对熵驱动混沌演化的柔性互联规划优化方法
An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution.
摘要(中文译文)
随着电力系统向更高智能化和灵活性方向发展，柔性互联技术已成为提升运行可靠性和经济性的关键手段。本文提出一种数据-模型双驱动的柔性互联系统规划方法，将用于负荷预测的多尺度时空交叉增强CNN-LSTM模型与混沌进化优化（CEO）算法相结合以优化系统设计。所提框架首先构建改进的CNN-LSTM混合架构，基于历史负荷数据和模拟特征集进行训练，以预测未来负荷曲线。引入一种新的可调相对熵（TRE）度量作为互补性量化指标，构建融合系统平衡性、可靠性、经济性和时空互补性的多目标函数。随后采用CEO算法求解优化模型，确定最优系统配置和运行参数。实验评估表明，预测模块具有较高精度，均方误差（MSE）为0.000368，平均绝对误差（MAE）为0.006334。此外，TRE指标使互补效率提升3.8%。通过利用混合神经网络的预测能力和CEO算法的优化效能，所提方法不仅降低了负荷波动指标，还提升了规划效率和运行经济性，为智能电力系统发展提供了可行路径。
Abstract
As power systems evolve towards greater intelligence and flexibility, flexible interconnection technology has emerged as a critical means to enhance operational reliability and economic performance. This paper presents a data-model dual-driven planning methodology for flexible interconnection systems, integrating a multi-scale spatio-temporal cross-enhanced CNN-LSTM model for load forecasting with a Chaotic Evolutionary Optimization (CEO) algorithm to optimize system design. The proposed framework first constructs an improved CNN-LSTM hybrid architecture, trained on historical load data and simulated feature sets, to predict future load profiles. A novel Tunable Relative Entropy (TRE) metric is introduced as a complementarity quantification index, forming a multi-objective function that incorporates system balance, reliability, economy, and spatio-temporal complementarity. The CEO algorithm is then employed to solve the optimization model, determining the optimal system configuration and operational parameters. Experimental evaluations demonstrate that the forecasting module achieves high accuracy, with a Mean Squared Error (MSE) of 0.000368 and a Mean Absolute Error (MAE) of 0.006334. Moreover, the TRE index improves complementarity efficiency by 3.8%. By leveraging the predictive capability of the hybrid neural network and the CEO algorithm's optimization efficacy, the proposed approach not only reduces load fluctuation indices but also enhances planning efficiency and operational economy, offering a viable pathway for intelligent power system development.
如何引用
AMA
Xiaoyan Zhao, Xubin Xing, Xiaoyan Guo, Jian Chao, Dapeng Hu, Xiangtao Zhuan. An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution.. PloS one. 2026; doi:10.1371/journal.pone.0351324.
APA
Xiaoyan Zhao, Xubin Xing, Xiaoyan Guo, Jian Chao, Dapeng Hu, Xiangtao Zhuan (2026). An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution.. PloS one. https://doi.org/10.1371/journal.pone.0351324
GB/T 7714
Xiaoyan Zhao, Xubin Xing, Xiaoyan Guo, Jian Chao, Dapeng Hu, Xiangtao Zhuan. An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution.[J]. PloS one, 2026 doi:10.1371/journal.pone.0351324.
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