Peer-to-peer, community or collective self-consumption, and transactive energy markets
offer new models for trading energy locally. Over the past five years, there has been …

Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …

Different fields have been thriving with the advents in mobile communication systems in
recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next …

In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

This comprehensive review paper aims to provide an in-depth analysis of the most recent
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …

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

In the present scenario, the fastest-growing environmental concerns are energy
management and monitoring. In-efficient energy recycling, energy consumption, energy …

The possibility of onsite production and flexible consumption is transforming consumers from
passive users to active service providers in power systems with the large share of renewable …

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 …

Fueled by big data, powerful and affordable computing resources, and advanced algorithms,
machine learning has been explored and applied to buildings research for the past decades …