Artificial Intelligence and Statistical Techniques in Short-Term Load Forecasting: a Review

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