A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
[HTML][HTML] A systematic review of machine learning techniques related to local energy communities
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 …
processes in several sectors, as in the case of electrical power systems. Machine learning …
[HTML][HTML] Artificial intelligence for electricity supply chain automation
Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …
processes ranging from production to transportation and consumption of electricity. The …
Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach
With the advancements in renewable energy and energy storage technologies, the energy
hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task …
hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task …
Convergence of photovoltaic power forecasting and deep learning: State-of-art review
Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a
promising research direction to intelligentize energy systems. With the massive smart meter …
promising research direction to intelligentize energy systems. With the massive smart meter …
[HTML][HTML] When edge intelligence meets cognitive buildings: The COGITO platform
Future buildings are complex systems that aim at improving the quality of life of their
inhabitants and increasing safeness, security, and efficiency. In order to reach these goals …
inhabitants and increasing safeness, security, and efficiency. In order to reach these goals …
[HTML][HTML] Twin-delayed deep deterministic policy gradient algorithm for the energy management of microgrids
D Domínguez-Barbero, J García-González… - … Applications of Artificial …, 2023 - Elsevier
The microgrid market is growing significantly due to several drivers, such as the need to
lower greenhouse gas emissions by integrating higher shares of distributed renewable …
lower greenhouse gas emissions by integrating higher shares of distributed renewable …
Data-driven online energy scheduling of a microgrid based on deep reinforcement learning
Y Ji, J Wang, J Xu, D Li - Energies, 2021 - mdpi.com
The proliferation of distributed renewable energy resources (RESs) poses major challenges
to the operation of microgrids due to uncertainty. Traditional online scheduling approaches …
to the operation of microgrids due to uncertainty. Traditional online scheduling approaches …
Optimizing scheduling policy in smart grids using probabilistic Delayed Double Deep Q-Learning (P3DQL) algorithm
High penetration of smart devices in IoE-enabled smart grids besides decentralization
originated from employing renewable resources face the power system with intricate …
originated from employing renewable resources face the power system with intricate …
Artificial intelligence-based control and coordination of multiple PV inverters for reactive power/voltage control of power distribution networks
The integration of Renewable Energy Resources (RERs) into Power Distribution Networks
(PDN) has great significance in addressing power deficiency, economics and environmental …
(PDN) has great significance in addressing power deficiency, economics and environmental …