期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 87, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2019.103323
关键词
Short-term natural gas consumption; Chaotic character recognition; Phase space reconstruction; Volterra adaptive filter; Improved whale optimization algorithm; Forecast
类别
资金
- north China university of water resources and electric power [40691]
Short-term natural gas consumption prediction is an important indicator of natural gas pipeline network planning and design, which is of great significance. The purpose of this study is to propose a novel hybrid forecast model in view of the Volterra adaptive filter and an improved whale optimization algorithm to predict the short-term natural gas consumption. Firstly, Gauss smoothing and C-C method is adopted to pretreat and reconstruct short-term natural gas consumption time series; secondly, to improve the performance of whale optimization algorithm, adaptive search-surround mechanism and spiral position and jumping behavior are introduced into it; Thirdly, Volterra adaptive filter is used to predict the short-term natural gas consumption, and the important parameters (e.g. embedding dimension) is optimized by improved whale optimization algorithm. Finally, an actual example is given to test the performance of the developed prediction model. The results indicate that (1) short-term natural gas consumption time series has chaotic characteristics; (2) performance of the improved whale optimization algorithm is better than some comparative algorithms (i.e. cuckoo optimization algorithm, etc. ) based on the different evaluation indicators; (3) exploration factor is the main operational factor; (4) the performance of the proposed prediction model is better than some advanced prediction models (e.g. back propagation neural network). It can be concluded that such an innovative hybrid prediction model may provide a reference for natural gas companies to achieve intelligent scheduling.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据