1 Content Introduction The safe and stable operation of the power system is closely related to the development of the national economy and the safety of personal property. Accurate short-term load forecasting results are important for the power grid to guide the power system in formulating generation plans, coordinating unit operations, scheduling load distribution, and … Read more
ClickBlue Text| Follow“Electrical Engineering” Abstract:Short-term power load is highly random and volatile, making it difficult for traditional load forecasting methods to grasp the patterns of short-term load changes. To improve the accuracy of short-term power load forecasting, we propose a method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory … Read more
Click the blue text| Follow “Electrical Engineering” Abstract:Traditional neural networks have low accuracy in load forecasting with strong temporal correlation. To effectively improve the accuracy of short-term power load forecasting, a load forecasting method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network is proposed. Five-dimensional load feature data … Read more
Click <天津大学学报> to follow us Deng Bin, Zhang Nan, Wang Jiang, Ge Leijiao Tianjin University, School of Electrical Automation and Information Engineering, Tianjin 300072 1 Citation Format Deng Bin, Zhang Nan, Wang Jiang, et al. Medium- and Long-Term Power Load Forecasting Method Based on LTC-RNN Model [J]. Journal of Tianjin University (Science and Technology), 2022, … Read more
Research on CNN-BiLSTM Short-term Power Load Forecasting Model Based on Attention Mechanism and ResNet WANG Lize1,2, XIE Dong1,2*, ZHOU Lifeng1,2, WANG Hanqing1,2 (1.School of Civil Engineering, University of South China, Hengyang, Hunan 421001, China;2.Hunan Engineering Laboratory of Building Environmental Control Technology, University of South China, Hengyang, Hunan 421001, China) Abstract：Short term power load forecasting is … Read more
Meng Lei School of Electrical Engineering, Shaanxi University of Technology /Abstract:/ To timely grasp the changes in power load, accurate forecasting is essential. Therefore, a combined model of CNN and LSTM is explored to predict short-term fluctuations in power load from one day to one week. The CNN model is responsible for feature extraction from … Read more