影响因子：8.0
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Knowledge-Based Systems
刊物所在地：荷兰
关键字：Uncertainty; Power load forecasting; Deep learning; Intelligent fusion; Statistical measures
摘要：Accurate load forecasting plays an important role in promoting low-carbon energy and high-quality utilization of electricity, as well as carbon reduction and safety in energy and power systems. The complexity of power system trends leads to widespread uncertainty in load forecasting. This article analyzes the internal mechanisms of load and meteorological factors at the level of multidimensional uncertainty and proposes a bidirectional memory feature hybrid model based on a new intelligent optimization method. The VMD optimized by gray wolf decomposes the electricity load data into different information modal components. The decomposed components are statistically analyzed with multidimensional uncertainty analysis to extract statistical patterns. The GTO part is optimized according to the fitness value to update the population and global optimal solution. Furthermore, convolutional neural networks (CNN) are used to extract potential features from load and meteorological data and enhance the correlation between input and output data. Then, short-term load forecasting is realized by using bi-directional short-term memory neural network (BiLSTM). The results show that the proposed model has smaller errors than several commonly used models, and has higher prediction accuracy. It can provide some new ideas for relevant departments to carry out power economic dispatch work in low-carbon modes.
合写作者：Ying-Ying Han,Jing-Jing Wang,Hao-Li Jia,彭丽玲,Hsin-Pou Huang
第一作者：范国峰
论文类型：期刊论文
通讯作者：洪维强
论文编号：111034
学科门类：工学
文献类型：J
卷号：280
ISSN号：0950-7051
是否译文：否
发表时间：2023-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0950705123007840?via%3Dihub