Abstract
Artificial intelligence is applied in smart grids to improve efficiency, reliability, and the integration of conventional and renewable energy sources. A state of the art review of artificial intelligence methods in smart grids is presented. A methodology is used for resource identification and systematic review. A taxonomy is proposed to classify machine learning models by method and application domain. Models are compared based on accuracy and computational efficiency. Key applications such as demand response, energy forecasting, fault detection, and grid optimization are analyzed. Artificial neural networks, decision trees, long short term memory networks, support vector machines, convolutional neural networks, and random forest models are identified as the most used approaches. The best performance is reported for convolutional neural network based and random forest based models. Load forecasting and energy management are identified as the most common application areas.