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

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.

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

“…In the context of the worldwide ever-increasing electricity demand, Mir et al present an insight of the forecasting approaches designed in the scientific literature in order to estimate future electricity demands in the cases of countries having low and middle incomes [19]. The devised study takes into account different prediction methodologies from the body of knowledge, targeting various time horizons, and remark that in the cases when forecasting for long [20] or medium time [21] intervals, the models based on time series have been mainly used, while for short term intervals [22], the techniques have made use of artificial intelligence (AI).…”

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

“…The influence of the economic sector is also considered in some works [28]. The combined influence of various factors on the forecasting of energy demand in the short, medium or long term is analyzed in [29], [30]. The analysis of the electricity demand profiles is mainly based on hourly data obtained through automatic meter readings [31].…”

The Impact of Particles Comminution on Mechanical Durability of Wheat Straw Briquettes

“…The forecast for the coming future indicates that by 2040, the global electricity demand will grow by 2.1% annually [1]. The increasing electricity demand resulting from the economic development of many countries globally and the significant growth of the population forces the use of more raw materials to meet these needs [2]. In terms of energy, the European Union (EU) strategy recommends 2 of 14 increasing the share of renewables, especially biomass, which is the third-largest energy source in the world [3].…”