A Review on Forecasting Models of Natural Gas

Authors

  • Dr. Meenakshi Thalor  HOD, Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India
  • Ritesh Choudhary  Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India
  • Ajay Jangid  Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India
  • Deep Gandhecha  Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India
  • Rishab Bhat  Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/IJSRST218375

Keywords:

Natural gas, Artificial Neural Networks, Time Series, Statistical Models

Abstract

This work gives away a survey of different published papers related to forecasting techniques in natural gas demand and consumption. Demand forecasting plays an important role in the company's decision making and inventory management process. It is important for local natural gas distributors to accurately predict the natural gas needs of their customers. While classifying the recent natural gas demand forecasting techniques, we have taken into account their methodologies, approach, data size, performance, results and limitations. Aim of this survey paper is to present a classified study of liquefied petroleum gas and natural gas related assets forecasting algorithms. This study provides readers with an understanding of the recent research in the natural gas supply-demand forecasting techniques.

References

  1. Aldina Correia, Cristina Lopes, Eliana Costae Silva, Magda Monteiro,Rui Borges Lopes.,"A multi-model methodology for forecasting sales and returns of liquefied petroleum gas cylinders",Springer-Verlag London Ltd.,2020.
  2. Aldina Correia, Eliana Costae Silva, Cristina Lopes, Claudio Henriques, Fabio Henriques, Mariana Pinto, Magda Monteiro, Rui Borges Lopes and Ana Sapata., "LPG Demand Forecast using Time Series",Proceedings of the 17 th International Conference on Computational and Mathematical Methods in Science and Engineering,CMMSE 2017 4–8 July,2017,Page 656 of 2288
  3. Athanasios Anagnostis, Elpiniki Papageorgiou, Vasileios Dafopoulos, Dionysios Bochtis.,"Applying Long Short-Term Memory Networks for natural gas demand prediction", 10th International Conference on Information, Intelligence, Systems and Applications (IISA) 2019.
  4. Horacio Paggi,Franco Robledo.,"A Neural Networks Based Model For The Prediction Of The Bottled Propane Gas Sales",International Conference on Mathematics and Computers in Sciences and in Industry,2014,Page 69-74.
  5. Junhui Guo.,"Oil price forecast using deep learning and ARIMA", 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI),2019,Page 241-247.
  6. Katarzyna Poczeta,Elpiniki I. Papageorgiou., "Implementing Fuzzy Cognitive Maps with neural networks for natural gas prediction",IEEE 30th International Conference on Tools with Artificial Intelligence,2018,Page 1026-1032.
  7. Michal Kozielski, Zbigniew Laskarzewki, "Matching a Model to a User - Application of Meta-Learning to LPG Consumption Prediction",2019.
  8. Prabodh Kumar Pradhan, Sunil Dhal, Nilayam Kumar Kamila., "Artificial Neural Network Conventional Fusion Forecasting Model for Natural Gas Consumption",2018,Page 2200-2205
  9. Wanshuai Hu,Zeyuan Tao,Dongyu Guo,Zixiao Pan.,"Natural Gas Prediction Model Based on Wavelet Transform and BP Neural Network",The 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) May 18-20, 2018, Nanjing, China,2018,Page 952-955.
  10. L Zlatan Sicanica, Zdravko Oklopcic.,"Countywide Natural Gas Consumption Forecast, a Machine Learning Approach",MIPRO 2018 , May 21-25, 2018, Opatija Croatia,2018,Page 1070-1073.
  11. Fernandez J, Cruz-Ramirez M, “Sensitivity versus accuracy in ensemble models of artificial neural networks”. Neural Computer Application(2018) ,Page:289–305
  12. Domino M, Laskarzewki Z, “Classification of LPG clients using the hurst exponent and the correlation co-efficient.” Theor. Appl. Inf. (2015) Page:27-39
  13. M. Ganjkani, S. N. Fallah, “Computational intelligence on short-term load forecasting: ,” Energies. 2019, Page:109-131
  14. Szoplek, J .“Forecasting of natural gas consumption with artificial neural networks.” Energy.2015, Page:208-220.
  15. Prabodh K, Sunil D, and Nilayam K.“Neural Network based Forecasting for Natural Gas Consumption.” Communications on Applied Electronics 2017,Page:45-80
  16. Bendat I, Moscar R,“A new stochastic multi source approach to improve the accuracy of the sales forecasts.” Foresight,2017,Page:48–64

International Journal of Scientific Research in Science and Technology

Downloads

Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Meenakshi Thalor, Ritesh Choudhary, Ajay Jangid, Deep Gandhecha, Rishab Bhat, " A Review on Forecasting Models of Natural Gas, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 8, Issue 3, pp.251-258, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRST218375