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Junaid Alam Memon

Personal Details

First Name:Junaid
Middle Name:Alam
Last Name:Memon
Suffix:
RePEc Short-ID:pme491
[This author has chosen not to make the email address public]

Affiliation

Pakistan Institute of Development Economics

Islamabad, Pakistan
http://www.pide.org.pk/

: (92)(51)9248051
(92)(51)9248065
P.O.Box 1091, Islamabad-44000
RePEc:edi:pideipk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Junaid Alam Memon & Gopal B. Thapa, 2015. "Explaining the de facto Open-access of Public Property Commons," PIDE-Working Papers 2015:114, Pakistan Institute of Development Economics.

Articles

  1. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
  2. Junaid Alam Memon & Usman Mustafa, 2012. "Emerging Issues in The Implementation of Irrigation and Drainage Sector Reforms in Sindh, Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 51(4), pages 289-301.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.

    Cited by:

    1. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    2. Khan, Muhammad Arshad & Abbas, Faisal, 2016. "The dynamics of electricity demand in Pakistan: A panel cointegration analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1159-1178.
    3. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    4. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-21, April.
    5. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-16, October.
    6. Cheng-Wen Lee & Bing-Yi Lin, 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting," Energies, MDPI, Open Access Journal, vol. 10(11), pages 1-18, November.
    7. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-22, November.
    8. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    9. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    10. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, Open Access Journal, vol. 10(11), pages 1-23, November.

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