Forecasting electricity consumption in Pakistan: the way forward

被引:131
|
作者
Hussain, Anwar [1 ]
Rahman, Muhammad [2 ]
Memon, Junaid Alam [1 ]
机构
[1] PIDE, Quaid E Azam Univ Campus,POB 1091, Islamabad 44000, Pakistan
[2] Islamia Coll Univ, Peshawar, Pakistan
关键词
Projections; Energy; Forecasting model; Forecast evaluation; Sectorial energy consumption; NEURAL-NETWORK; TIME-SERIES; ENERGY-CONSUMPTION; DEMAND; REGRESSION; MODEL; ERROR; INTEGRATION; ACCURACY; SYSTEM;
D O I
10.1016/j.enpol.2015.11.028
中图分类号
F [经济];
学科分类号
02 ;
摘要
Growing shortfall of electricity in Pakistan affects almost all sectors of its economy. For proper policy formulation, it is imperative to have reliable forecasts of electricity consumption. This paper applies Holt-Winter and Autoregressive Integrated Moving Average (ARIMA) models on time series secondary data from 1980 to 2011 to forecast total and component wise electricity consumption in Pakistan. Results reveal that Holt-Winter is the appropriate model for forecasting electricity consumption in Pakistan. It also suggests that electricity consumption would continue to increase throughout the projected period and widen the consumption-production gap in case of failure to respond the issue appropriately. It further reveals that demand would be highest in the household sector as compared to all other sectors and the increase in the energy generation would be less than the increase in total electricity consumption throughout the projected period. The study discuss various options to reduce the demand-supply gap and provide reliable electricity to different sectors of the economy. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:73 / 80
页数:8
相关论文
共 50 条
  • [1] Forecasting of electricity consumption in Pakistan based on integrating machine learning algorithms and Monte Carlo simulation
    Nazir, Muhammad Umair
    Li, Jinchao
    ELECTRICAL ENGINEERING, 2025,
  • [2] Forecasting electricity consumption of OECD countries: A global machine learning modeling approach
    Sen, Doruk
    Tunc, K. M. Murat
    Gunay, M. Erdem
    UTILITIES POLICY, 2021, 70
  • [3] A meta-heuristic framework for forecasting household electricity consumption
    Azadeh, A.
    Faiz, Z. S.
    APPLIED SOFT COMPUTING, 2011, 11 (01) : 614 - 620
  • [4] A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption
    Zhang, Xi
    Li, Rui
    FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [5] Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions
    Kazmi, Majida
    Khan, Hashim Raza
    Lubaba
    Bin Khalid, Mohammad Hashir
    Qazi, Saad Ahmed
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (02): : 2351 - 2370
  • [6] Pakistan's electrical energy crises, a way forward towards 50% of sustain clean and green electricity generation
    Tao, Jinsong
    Waqas, Muhammad
    Ali, Muhammad
    Umair, Muhammad
    Gan, Wangwei
    Haider, Hussain
    ENERGY STRATEGY REVIEWS, 2022, 40
  • [7] Intelligent techniques for forecasting electricity consumption of buildings
    Amber, K. P.
    Ahmad, R.
    Aslam, M. W.
    Kousar, A.
    Usman, M.
    Khan, M. S.
    ENERGY, 2018, 157 : 886 - 893
  • [8] Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
    Divina, Federico
    Gilson, Aude
    Gomez-Vela, Francisco
    Torres, Miguel Garcia
    Torres, Jose E.
    ENERGIES, 2018, 11 (04)
  • [9] Unlocking Household Electricity Consumption in Pakistan
    Amber, Khuram Pervez
    Ahmad, Rizwan
    Farmanbar, Mina
    Bashir, Muhammad Anser
    Mehmood, Sajid
    Khan, Muhammad Sajid
    Saeed, Muhammad Umer
    BUILDINGS, 2021, 11 (11)
  • [10] Electricity consumption forecasting in Brazil: A spatial econometrics approach
    Cabral, Joilson de Assis
    Loureiro Legey, Luiz Fernando
    de Freitas Cabral, Maria Viviana
    ENERGY, 2017, 126 : 124 - 131