影响因子：3.6
DOI码：10.1002/cse3.1465
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Energy Science & Engineering
刊物所在地：英国
关键字：electricity usage behavior; generalized regression neural network (GRNN); short‐term load forecasting; sparrow search algorithm (SSA); wavelet decomposition model (EWT)
摘要：With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short-term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti-forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT-SSA-GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy.
合写作者：Yun Li,Xin-Yan Zhang,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
学科门类：工学
文献类型：J
卷号：11
期号：7
页面范围：2444-2468
ISSN号：2050-0505
是否译文：否
发表时间：2023-01-01
收录刊物：SCI
发布期刊链接：https://scijournals.onlinelibrary.wiley.com/doi/10.1002/ese3.1465