影响因子：9.4
DOI码：10.1016/j.eswa.2023.122012
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Expert Systems with Applications
刊物所在地：美国
关键字：Convolutional Neural Network (CNN); Feature extraction; Recurrent neural network (RNN); Power consumption; Long short-term memory network (LSTM)
摘要：Accurate and reliable load forecasting can ensure the safety and economy of power system operation. To improve the accuracy of short-term power load forecasting, this paper adopts feature extraction combined with a variety of machine learning methods,(Empirical wavelet decomposition (EWT), Convolutional neural network (CNN) model, Recurrent neural network (RNN) model, Long short-term memory network (LSTM) model, Bayesian optimization (BOA) algorithm). A new hybrid forecasting model, the EWT-CNN-S-RNN + LSTM model is proposed to predict power consumption. In this method, the fixed mode of EWT decomposition is used to extract statistical features, and the LSTM/RNN model is selected according to the statistical features. This method uses the Bayesian optimization (BOA) algorithm to optimize the parameters to solve the model gradient explosion problem. Based on the electricity consumption data of 2007 (Australian Energy Market Operator) and a total of 336 power load data, a good load forecasting result is obtained, which can provide indirect support for power regulation and sustainable development of electricity.
合写作者：韩莹莹,李金伟,彭丽玲,Yi-Hsuan Yeh
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：122012
学科门类：工学
文献类型：J
卷号：238
ISSN号：0957-4174
是否译文：否
发表时间：2024-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0957417423025149?via%3Dihub