A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

Aneeque A. Mir, Mohammed Alghassab, Kafait Ullah, Zafar A. Khan, Yuehong Lu and Muhammad Imran
Additional contact information
Aneeque A. Mir: U.S. Pakistan Centers for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Mohammed Alghassab: Department of Electrical and Computer Engineering, Shaqra University, Riyadh 11911, Saudi Arabia
Kafait Ullah: U.S. Pakistan Centers for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Zafar A. Khan: Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan
Yuehong Lu: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Muhammad Imran: School of Engineering and Applied Science, Mechanical Engineering and Design, Aston University, B4 7ET Birmingham, UK

Sustainability, 2020, vol. 12, issue 15, 1-35

Abstract: With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.

Keywords: demand determinants; forecasting horizon; load forecasting; low and middle income countries (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/15/5931/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/15/5931/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:15:p:5931-:d:388506

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().