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

Résumé

A high penetration of renewable energy sources such as wind power generation and photo-voltaic generation causes some problems in power systems such as the duck curve and unreliability dueto environmental variability. An effective solution to this problem is Demand Response (DR). ElectricWater Heaters (EWHs) are considered ideal candidates for DR due to their energy storage capability.Due to the benefi?ts, control strategies or techniques for EWHs have received considerable academic attention. The energy sector has recently tapped into the disruptive arti?ficial intelligence world to learn,among other related priorities, how to enhance operations, maintain energy resilience and improve consumer service. Consequently, this paper reviews the use of machine learning (ML) for optimization andscheduling of EWHs. The main contributions of this review paper are, ?firstly, to identify state of the artof energy optimization and scheduling of EWHs. Secondly, to review the current ML models for energyoptimization and scheduling of EWHs in smart grids and smart building environment. While classicalcontrol strategies may deliver substantial improvements, optimum effciency may not be reached. MLhas demonstrated clear advantages over classical control. Based on these conclusions, recommendationsfor further research topics are drawn.

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