Review on the State of the Art of Electricity Load Forecasting Methodologies in Developing and Newly-Industrialised Countries: An Initiative to Establish an Effective Load Forecasting Model

Quality of Load Data in DNI Countries

Noises (outliers and missing values) in the recorded electricity data is reported in several studies to be a prominent issue facing the DNI countries, and primarily affect the forecasting accuracy of a method (Fahad & Arbab, 2014). Moreover, the outliers and missing values in the data is reported to be caused by inefficient and aging technologies in transmission and distribution infrastructure (Bhattacharyya & Timilsina, 2010). Therefore, a forecasting technique in the DNI countries need to embrace practices to consider the inclusion of data cleansing mechanisms such as those presented in (Chen et al., 2010; Hafeez et al., 2020; Jeenanunta et al., 2018; Prakash et al., 2015; Xie & Hong, 2016; Yu et al., 2016).