  Electrical utilities depend on short-term demand forecasting to proactively
adjust production and distribution in anticipation of major variations. This
systematic review analyzes 240 works published in scholarly journals between
2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical,
and hybrid models to short-term load forecasting (STLF). This work represents
the most comprehensive review of works on this subject to date. A complete
analysis of the literature is conducted to identify the most popular and
accurate techniques as well as existing gaps. The findings show that although
Artificial Neural Networks (ANN) continue to be the most commonly used
standalone technique, researchers have been exceedingly opting for hybrid
combinations of different techniques to leverage the combined advantages of
individual methods. The review demonstrates that it is commonly possible with
these hybrid combinations to achieve prediction accuracy exceeding 99%. The
most successful duration for short-term forecasting has been identified as
prediction for a duration of one day at an hourly interval. The review has
identified a deficiency in access to datasets needed for training of the
models. A significant gap has been identified in researching regions other than
Asia, Europe, North America, and Australia.
