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

被引：38
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 9630298, Japan
来源：
ENERGY REPORTS | 2023年 / 9卷
关键词：
Ethanol blending; India; Time series; Forecasting; Regression; ARIMA; CONSUMPTION; MANAGEMENT; FUEL;
D O I：
10.1016/j.egyr.2022.11.038
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/).
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页码：411 / 418
页数：8
相关论文
共 42 条
[1]   IEC 61850 and XMPP Communication Based Energy Management in Microgrids Considering Electric Vehicles [J].
Aftab, Mohd Asim ;
Hussain, S. M. Suhail ;
Ali, Ikbal ;
Ustun, Taha Selim .
IEEE ACCESS, 2018, 6 :35657-35668
[2]   Forecasting Natural Gas Production Using Various Regression Models [J].
Aydin, G. .
PETROLEUM SCIENCE AND TECHNOLOGY, 2015, 33 (15-16) :1486-+
[3]   The Application of Trend Analysis for Coal Demand Modeling [J].
Aydin, G. .
ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2015, 10 (02) :183-191
[4]   The Modeling and Projection of Primary Energy Consumption by the Sources [J].
Aydin, G. .
ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2015, 10 (01) :67-74
[5]   Electric bus migration in Bengaluru with dynamic charging technologies [J].
Begwani A.K. ;
Ustun T.S. .
AIMS Energy, 2017, 5 (06) :944-959
[6]  
Boamah V, 2021, Management Science and Business Decisions, V1, P29, DOI [10.52812/msbd.25, 10.52812/msbd.25, DOI 10.52812/MSBD.25]
[7]   A comparison of time series methods for forecasting container throughput [J].
Chan, Hing Kai ;
Xu, Shuojiang ;
Qi, Xiaoguang .
INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS, 2019, 22 (03) :294-303
[8]  
Chandra A, 2021, Gain report number: IN2021-0072
[9]   Techno-economic assessment and environmental analysis of an optimal hybrid system under novel demand response scheme for a remote region of India [J].
Chauhan, Anurag ;
Khan, Mohd Tauseef ;
Srivastava, Ashish ;
Tripathi, Ashish ;
Hussain, S. M. Suhail ;
Ustun, Taha Selim .
ENERGY REPORTS, 2022, 8 :284-291
[10]   Is the demand for crude oil inelastic for India? Evidence from structural VAR; analysis [J].
Dash, Devi Prasad ;
Sethi, Narayan ;
Bal, Debi Prasad .
ENERGY POLICY, 2018, 118 :552-558
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