The steep rise in reinforcement learning (RL) in various applications in energy as well as the
penetration of home automation in recent years are the motivation for this article. It surveys …

The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy
management systems which employ reinforcement learning-based methods to integrate …

Home energy management (HEM) systems optimize electricity demand of appliances
according to the price-based demand response (DR) programs. Undoubtedly, customer …

Automated indoor environmental control is a research topic that is beginning to receive
much attention in smart home automation. All machine learning models proposed to date for …

In recent years, several studies have been conducted on the dynamic control of traffic signal
durations using deep reinforcement learning with the aim of reducing traffic congestion. The …

Data-driven energy management with flexible appliances in smart buildings is a key towards
power system operational intelligence. However, the low efficiency of existing deep …

With increasing electricity prices, cost savings through load shifting are becoming
increasingly important for energy end users. While dynamic pricing encourages customers …

This thesis focuses on development of efficient data-driven model-based and modelfree
solution methodologies as a key feature to address the challenges of building energy …

Reinforcement learning to improve flexibility of building energy management — Welcome to
DTU Research Database Skip to main navigation Skip to search Skip to main content …

In recent years, Internet of Things (IoT) applications have become across-the-board and are
used by most smart device users. Wired Communication, Bluetooth, radio frequency (RF) …