A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

Autor: Huibin Zeng, Bilin Shao, Genqing Bian, Dan Song, Xiaojun Li
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: IEEE Access, Vol 8, Pp 222824-222840 (2020)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3044052
Popis: With economic development and scientific and technological progress, people's requirements for the ecological environment are increasing day by day. As a clean energy, natural gas is favored by many countries all over the world. As an important part of natural gas industry planning, load forecasting plays a vital role in the optimal dispatching and operation of the natural gas network. From the perspective of prediction technology, this paper selects the literature related to natural gas load prediction from the Web of Science and CNKI database as the research object. Firstly, the literature chronology distribution and background, research institutions, and academic communities were analyzed by CiteSpace scientific knowledge mapping software. Secondly, the research hotspots and cutting-edge technologies were presented and analyzed visually, and the hot spot migration process in this field was summarized. In the end, the paper puts forward some problems that should be paid attention to in natural gas load prediction, including data processing in prediction, prediction of prediction model, adaptive prediction method and combination prediction method, which are also the direction of development in the future.
Databáze: Directory of Open Access Journals