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Open AccessReview

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

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U.S. Pakistan Centers for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
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Department of Electrical and Computer Engineering, Shaqra University, Riyadh 11911, Saudi Arabia
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Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan
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Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
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School of Engineering and Applied Science, Mechanical Engineering and Design, Aston University, B4 7ET Birmingham, UK
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Authors to whom correspondence should be addressed.
Sustainability 2020, 12(15), 5931; https://doi.org/10.3390/su12155931
Received: 25 June 2020 / Revised: 16 July 2020 / Accepted: 18 July 2020 / Published: 23 July 2020
(This article belongs to the Special Issue Hybrid Energy System)
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. View Full-Text
Keywords: demand determinants; forecasting horizon; load forecasting; low and middle income countries demand determinants; forecasting horizon; load forecasting; low and middle income countries
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MDPI and ACS Style

Mir, A.A.; Alghassab, M.; Ullah, K.; Khan, Z.A.; Lu, Y.; Imran, M. A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons. Sustainability 2020, 12, 5931.

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