1.西安建筑科技大学 建筑设备科学与工程学院;2.西安建筑科技大学 信息与控制工程学院
1.School of Building Equipment Science and Engineering, Xi&2.amp;3.#39;4.&5.an University of Architecture and Technology;6.School of Information and Control Engineering, Xi'7.'
针对区域级电力负荷数据周期性强、波动性高以及预测精度不高等问题，提出了一种融合优化变分模态分解（variational mode decomposition，VMD）与Informer双向长短期记忆网络（informer-bidirectional long short-term memory，Informer-BiLSTM ）的非平稳负荷预测方法。通过引入冠豪猪优化算法（crested porcupine optimizer，CPO）对VMD的模态数量和权重系数进行了优化，有效地将复杂负荷时间序列分解为多个固有模态函数，提取关键的时频特征。随后，利用Informer与BiLSTM构建并行预测模型，对分解后的各个分量进行精准预测，并引入集成算法进一步减小预测误差。实验结果表明，相较于其它组合预测模型，预测精度明显提高。
Aiming at the problems of strong periodicity, high volatility and low prediction accuracy of regional power load data, a non-stationary load forecasting method combining optimized variational mode decomposition(VMD) and Informer-Bidirectional Long Short-Term Memory (Informer-BiLSTM) is proposed. By introducing the crested porcupine optimizer (CPO), the number of modes and weight coefficients of VMD are optimized, and the complex load time series is effectively decomposed into multiple intrinsic mode functions to extract key time-frequency features. Subsequently, a parallel prediction model was constructed using Informer and BiLSTM to accurately predict each component after decomposition, and an integrated algorithm was introduced to further reduce the prediction error. The experimental results show that the prediction accuracy is significantly improved compared with other combined prediction models.
