Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …

The penetration of renewable energies is increasing in power systems all over the world.
The volatility and intermittency of renewable energies pose real challenges to energy …

Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …

This paper proposes a novel framework for home energy management (HEM) based on
reinforcement learning in achieving efficient home-based demand response (DR). The …

Energy crisis and the global impetus to “go green” have encouraged the integration of
renewable energy resources, plug‐in electric vehicles, and energy storage systems to the …

Home energy management systems (HEMSs) help manage electricity demand to optimize
energy consumption and distributed renewable energy generation without compromising …

Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

This paper presents a self-scheduling model for home energy management systems
(HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed …

The use of machine learning techniques has been proven to be a viable solution for smart
home energy management. These techniques autonomously control heating and domestic …

The ongoing deployment of smart meters and different commercial devices has made
electricity disaggregation feasible in buildings and households, based on a single measure …