Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning

• Limited generation capacity and penetration of renewables need demand response. Energy-storing water heaters are suitable for response with scheduling. Artificial intelligence potentially suited due to patterned hot usage. Review scheduling techniques machine learning optimal Machine clear advantages over classical control strategies. A high renewable energy sources such as wind power photovoltaic causes some problems in systems the duck curve unreliability environmental variability. An effective solution this problem is Demand Response (DR). Electric Water Heaters (EWHs) considered ideal candidates DR their storage capability. Due benefits, strategies or EWHs have received considerable academic attention. The sector has recently tapped into disruptive artificial world learn, among other related priorities, how enhance operations, maintain resilience improve consumer service. Consequently, paper reviews use (ML) optimization EWHs. main contributions review are, firstly, identify state art Secondly, current ML models smart grids building environment. While may deliver substantial improvements, optimum efficiency not be reached. demonstrated control. Based on these conclusions, recommendations further research topics drawn.