Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addi...Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.展开更多
基金the Science and Technology Project of State Grid Fujian Electric Power Co.,Ltd.(Project No.B31300240001)with the project title“Research on Key Technologies for Load Forecasting and Regulation Capability Evaluation of Regional Power Grid Taking into AccountWide Area Distributed New Energy Access”.
文摘Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.
