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Proposed Power and Energy System Master Plan (PESMP): Perspective on Analytical Frame, Methodology and Influencing Factors on Demand Forecasting

Author

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  • Khondaker Golam Moazzem
  • Helen Mashiyat Preoty

Abstract

The new Power and Energy System Master Plan (PESMP) is on the process of drafting by the Ministry of Power Energy and Mineral Resources (MoPEMR). The new PSEMP aims to promote a low or zero-carbon transformation of the total energy supply and demand system. The successive PSMPs (2005, 2010 and 2016) have been criticised to have an inappropriate demand projection which led to different types of challenges. The paper reviews the successive PSMPs (PSMP 2005, 2010 and 2016) to find out the methodological weaknesses and suggests the alternative methodology for demand-side analysis of the power sector for the new plan. Based on the literature of developing countries and the findings of the key informant interviews (KIIs) the paper finds that Bangladesh needs to consider a sound methodology for proper forecasting of electricity demand. A number of methods which are methodologically well-recognised and applied to different countries such as bottom-up approach which could be more appropriate in the context of Bangladesh to forecast the power demand in the PESMP 2021. This paper concludes with a number of recommendations for the next PESMP.

Suggested Citation

  • Khondaker Golam Moazzem & Helen Mashiyat Preoty, 2021. "Proposed Power and Energy System Master Plan (PESMP): Perspective on Analytical Frame, Methodology and Influencing Factors on Demand Forecasting," CPD Working Paper 139, Centre for Policy Dialogue (CPD).
    • Handle: RePEc:pdb:opaper:139
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    References listed on IDEAS

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    1. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    2. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    3. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
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    More about this item

    Keywords

    PESMP; Power Sector; Power and Energy; Renewable Energy; Clean Energy; Rental Power; COVID-19;
    All these keywords.

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