Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

• Published in: Engineering applications of artificial intelligence Vol. 87; p. 103323
• Main Authors: Qiao, Weibiao, Yang, Zhe, Kang, Zhangyang, Pan, Zhen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2020
• Subjects: Chaotic character recognition; Forecast; Improved whale optimization algorithm; Phase space reconstruction; Short-term natural gas consumption; Volterra adaptive filter; Improved whale optimization algorithm; Phase space reconstruction; Short-term natural gas consumption; Chaotic character recognition; Forecast; Volterra adaptive filter
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2019.103323

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. [Display omitted] •The ability obtained the global optimal solution by applying IWOA is better.•The exploration in IWOA is the main operating factor.•The developed hybrid prediction model has higher forecasting accuracy.