Predicting energy consumption through the LEAP model based on LMDI technique along with economic analysis: A case study

被引:5
作者
Abbas, Zeshan [1 ]
Waqas, Muhammad [2 ]
Lun, Zhao [1 ,7 ]
Saleem Khan, Saad [3 ]
Amjad, Mohsin [4 ]
Larkin, Stephen [5 ,6 ]
机构
[1] Shenzhen Polytech Univ, Inst Ultrason Technol, Shenzhen, Peoples R China
[2] Pakistan Inst Engn & Technol, Mech Engn Dept, Multan, Pakistan
[3] United Arab Emirates Univ, Al Ain, U Arab Emirates
[4] Natl Transmiss & Despatch Co Ltd, Islamabad, Pakistan
[5] Omega Aviat Ltd, Leicester, England
[6] Africa New Energies Ltd, London, England
[7] Shenzhen Polytech Univ, Inst Ultrason Technol, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
LMDI energy decomposition; LEAP model; qualitative analysis; quantitative investigation; economic analysis; LONG-TERM ENERGY; CO2; EMISSIONS; CARBON EMISSIONS; DRIVING FORCES; POWER SECTOR; TECHNOLOGIES; REDUCTION; PATHWAYS; DRIVERS; IMPACTS;
D O I
10.1177/01445987231202802
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
This research reports the implementation of logarithmic mean Divisia index (LMDI) and categorizes the growth of total energy usage in three different industrial sectors for the years of 2010 to 2021. Furthermore, it classifies and evaluates the factors influencing on energy consumption in Punjab province thru a sustainable way. The growth consumption is classified into scale influence, structure influence, and efficiency influence. Likewise, the long-term energy alternatives planning-Punjab model is executed with the energy consumption, scale impact, structure impact, and efficiency impact. Besides, comprehensive adjustment scenarios are also introduced to examine the impact of three different factors on overall energy usage. The results from the qualitative decomposition of LMDI indicate that the high scale can lead to high-energy consumption in Punjab Province. However, it can be reduced by high-efficiency reinforcement. The total energy consumption in 2024, 2036, and 2044 under reference scenario is 304.12, 460.01, and 590.04 million tons compared to structure influence analysis for slow terminology (SIAS) and comprehensive scenario. For that reason, it can predict and provide earlier energy management planning for the province. Conversely, the structure factor does not display obvious effect on the energy use. Equally, the quantitative results of the long-term energy alternative planning (LEAP) model are relatively consistent with those of LMDI model, whose advantageous impact on the structure influence is reasonably extrapolated. This phenomenon indicates that the structure influence and efficiency influence will maintain the disruptive impact on increasing overall energy use for the future perspectives. Consequently, the LEAP model predicts the energy consumption of Punjab Province among the years of 2020 to 2040 under medium-term development framework.
引用
收藏
页码:1919 / 1941
页数:23
相关论文
共 53 条
[11]   Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review [J].
Daut, Mohammad Azhar Mat ;
Hassan, Mohammad Yusri ;
Abdullah, Hayati ;
Rahman, Hasimah Abdul ;
Abdullah, Md Pauzi ;
Hussin, Faridah .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 70 :1108-1118
[12]  
Dong X., 2020, IOP C SERIES EARTH E, V514
[13]   Modeling alternative scenarios for Egypt 2050 energy mix based on LEAP analysis [J].
El-Sayed, Ahmed Hassan A. ;
Khalil, Adel ;
Yehia, Mohamed .
ENERGY, 2023, 266
[14]   A techno-economic and environmental assessment of long-term energy policies and climate variability impact on the energy system [J].
Emodi, Nnaemeka Vincent ;
Chaiechi, Taha ;
Beg, A. B. M. Rabiul Alam .
ENERGY POLICY, 2019, 128 :329-346
[15]   Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem [J].
Goli, Alireza ;
Ala, Ali ;
Hajiaghaei-Keshteli, Mostafa .
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
[16]   A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty [J].
Goli, Alireza ;
Ala, Ali ;
Mirjalili, Seyedali .
ANNALS OF OPERATIONS RESEARCH, 2023, 328 (01) :493-530
[17]   RETRACTED: Two-echelon electric vehicle routing problem with a developed moth-flame meta-heuristic algorithm [J].
Goli, Alireza ;
Golmohammadi, Amir-Mohammad ;
Verdegay, Jose-Luis .
OPERATIONS MANAGEMENT RESEARCH, 2022, 15 (3-4) :891-912
[18]   Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain [J].
Goli, Alireza ;
Mohammadi, Hatam .
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2022, 24 (09) :10540-10569
[19]   An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study [J].
Goli, Alireza ;
Khademi-Zare, Hasan ;
Tavakkoli-Moghaddam, Reza ;
Sadeghieh, Ahmad ;
Sasanian, Mazyar ;
Malekalipour Kordestanizadeh, Ramina .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2021, 32 (01) :1-35
[20]   Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study: The dairy products industry [J].
Goli, Alireza ;
Zare, Hasan Khademi ;
Tavakkoli-Moghaddam, Reza ;
Sadeghieh, Ahmad .
COMPUTERS & INDUSTRIAL ENGINEERING, 2019, 137