基于DWT-Informer的台区短期负荷预测

Short-term substation load forecasting based on DWT-Informer model

电力负荷预测是确保电力系统安全高效运行的关键任务,针对台区短期电力负荷预测这一关键问题,该文章研究了电气特性数据处理和Informer模型优化的新方法。文章通过离散小波变换(DWT)对电流数据进行降噪处理,同时使用Prophet模型提取时序特征优化输入数据;并采用Informer的稀疏自注意力机制和自注意力蒸馏,增强了模型的特征捕捉和预测速度。实例数据验证表明,经过DWT和Prophet特征提取后的模型在各项相同的指标下均优于原始模型,验证了DWT-Informer模型在数据预处理和模型优化方面均取得了显著的性能提升。

Electricity load forecasting is an important task in ensuring safety and efficiency of power system.Aiming at the pivotal issue of short-term electricity load forecasting in distribution areas,this paper presents a novel approach that combines electrical feature data processing and Informer model optimization.The study employs discrete wavelet transform(DWT)for denoising current data while utilizing Prophet model for extracting temporal features to enhance input data quality.Additionally,the method incorporates ProbSparse self-attention mechanism and self-attention distillation,thereby bolstering feature capture and prediction speed within the model.The model extracted by DWT and Prophet features is superior to the original model under the same indices.Validation with instance data demonstrates that the DWT-Informer model,enriched through both data preprocessing and model optimization,outperforms the baseline model across various performance metrics.