Ai-generated content (aigc): A survey
J Wu, W Gan, Z Chen, S Wan, H Lin - arXiv preprint arXiv:2304.06632, 2023 - arxiv.org
To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …
[HTML][HTML] Intelligent energy management systems: a review
Climate change has become a major problem for humanity in the last two decades. One of
the reasons that caused it, is our daily energy waste. People consume electricity in order to …
the reasons that caused it, is our daily energy waste. People consume electricity in order to …
[HTML][HTML] Edge computing for iot-enabled smart grid: The future of energy
The explosive development of electrical engineering in the early 19th century marked the
birth of the 2nd industrial revolution, with the use of electrical energy in place of steam …
birth of the 2nd industrial revolution, with the use of electrical energy in place of steam …
[HTML][HTML] Systematic review on deep reinforcement learning-based energy management for different building types
A Shaqour, A Hagishima - Energies, 2022 - mdpi.com
Owing to the high energy demand of buildings, which accounted for 36% of the global share
in 2020, they are one of the core targets for energy-efficiency research and regulations …
in 2020, they are one of the core targets for energy-efficiency research and regulations …
[HTML][HTML] Convolutional neural network-based personalized program recommendation system for smart television users
KV Dudekula, H Syed, MIM Basha, SI Swamykan… - Sustainability, 2023 - mdpi.com
The smart home culture is rapidly increasing across the globe and driving smart home users
toward utilizing smart appliances. Smart television (TV) is one such appliance that is …
toward utilizing smart appliances. Smart television (TV) is one such appliance that is …
Energy efficient behavior modeling for demand side recommender system in solar microgrid applications using multi-agent reinforcement learning model
Electricity consumers are often faced with challenges relating to the choice of an optimal
energy saving plan. Increasing integration of transient renewable energy sources promises …
energy saving plan. Increasing integration of transient renewable energy sources promises …
Meta-reinforcement learning-based transferable scheduling strategy for energy management
In Home Energy Management System (HEMS), the scheduling of energy storage equipment
and shiftable loads has been widely studied to reduce home energy costs. However …
and shiftable loads has been widely studied to reduce home energy costs. However …
Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies
The increasingly popular electric vehicles (EVs) are changing the control paradigm of the
power grid due to their uncoordinated charging behaviors. However, if well coordinated …
power grid due to their uncoordinated charging behaviors. However, if well coordinated …
The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting
M Chen, Z Xie, Y Sun, S Zheng - Applied Energy, 2023 - Elsevier
As a promising technology for replacing the rule-based decision-making in region heating
systems (RHS), deep reinforcement learning (DRL) is a practical solution to identify the …
systems (RHS), deep reinforcement learning (DRL) is a practical solution to identify the …
Multi-objective energy management of a smart home in real time environment
In home energy management, the occupants schedule the operating appliances to achieve
lowest optimal energy cost with minimum discomfort. Smart home energy management turns …
lowest optimal energy cost with minimum discomfort. Smart home energy management turns …