Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models

被引:0
|
作者
Dey, Bishal [1 ]
Roy, Bidesh [1 ]
Datta, Subir [2 ]
Ustun, Taha Selim [3 ]
机构
[1] Natl Inst Technol Mizoram, Aizawl 796012, Mizoram, India
[2] Mizoram Univ, Aizawl 796004, Mizoram, India
[3] AIST FREA, Fukushima Renewable Energy Inst, Koriyama, Fukushima 9630298, Japan
关键词
Ethanol blending; India; Time series; Forecasting; Regression; ARIMA;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Accurate fuel demand forecast is crucial to meet the demand. India is aiming to roll out 20% ethanol blended gasoline by 2025 and planning to make this transition complete by 2030. But due to lack of ethanol availability this target cannot be achieved yet. So, accurate forecast of ethanol demand based on future gasoline demand is important. In this study, various linear and non-linear regression models and Autoregressive integrated moving average (ARIMA) models are developed and compared for forecasting gasoline demand in India. Historical gasoline consumption data from 1997 to 2021 is used to develop and evaluate these models. ARIMA (1,1,0) model is found to be the most accurate among all the developed models for this specific time series. Forecast made with ARIMA (1,1,0) model shows that ethanol demand for achieving 20% blending will be 9777 million Litres by 2025 and 11247 million Litres by 2030. Hence, considering the result of this study and the present domestic ethanol production it can be said that India will continue to face ethanol shortage to meet future blending targets. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:411 / 418
页数:8
相关论文
共 8 条
  • [1] Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models
    Dey, Bishal
    Roy, Bidesh
    Datta, Subir
    Ustun, Taha Selim
    ENERGY REPORTS, 2023, 9 : 411 - 418
  • [2] Meeting India's ethanol blending goals: forecasting demand and addressing supply shortages
    Dey, Bishal
    Roy, Bidesh
    Datta, Subir
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-ENERGY, 2024,
  • [3] Comparison of Forecasting India's Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model
    Jiang, Feng
    Yang, Xue
    Li, Shuyu
    SUSTAINABILITY, 2018, 10 (07)
  • [4] Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models
    Busari, S. I.
    Samson, T. K.
    SCIENTIFIC AFRICAN, 2022, 18
  • [5] Comparison of Forecasting Models using Multiple Regression and Artificial Neural Networks for the Supply and Demand of Thai Ethanol
    Homchalee, R.
    Sessomboon, W.
    2013 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM 2013), 2013, : 963 - 967
  • [6] Health supply chain forecasting: a comparison of ARIMA and LSTM time series models for demand prediction of medicines
    Mbonyinshuti, Francois
    Nkurunziza, Joseph
    Niyobuhungiro, Japhet
    Kayitare, Egide
    ACTA LOGISTICA, 2024, 11 (02): : 269 - 280
  • [7] Forecasting sector-wise electricity consumption for India using various regression models
    Rekhade, Renuka
    Sakhare, D. K.
    CURRENT SCIENCE, 2021, 121 (03): : 365 - 371
  • [8] A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system
    Runge, Jason
    Saloux, Etienne
    ENERGY, 2023, 269