Edge computing with artificial intelligence: A machine learning perspective
Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …
providing sufficient data for model training and inference, IoT has promoted the development …
Energy management in power distribution systems: Review, classification, limitations and challenges
MS Alam, SA Arefifar - IEEE Access, 2019 - ieeexplore.ieee.org
Energy management in distribution systems has gained attention in recent years.
Coordination of electricity generation and consumption is crucial to save energy, reduce …
Coordination of electricity generation and consumption is crucial to save energy, reduce …
Optimal energy management strategies for energy Internet via deep reinforcement learning approach
This paper investigates the energy management problem in the field of energy Internet (EI)
with interdisciplinary techniques. The concept of EI has been proposed for a while. However …
with interdisciplinary techniques. The concept of EI has been proposed for a while. However …
Data-driven dynamical control for bottom-up energy Internet system
With the increasing concern on climate change and global warming, the reduction of carbon
emission becomes an important topic in many aspects of human society. The development …
emission becomes an important topic in many aspects of human society. The development …
Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization
Environmental and climate change concerns are pushing the rapid development of new
energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality …
energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality …
A distributed double-Newton descent algorithm for cooperative energy management of multiple energy bodies in energy internet
This article investigates the problem of distributed cooperative energy management of
multiple energy bodies with the consideration of both the optimal energy generation …
multiple energy bodies with the consideration of both the optimal energy generation …
A CNN and LSTM-based multi-task learning architecture for short and medium-term electricity load forecasting
S Zhang, R Chen, J Cao, J Tan - Electric power systems research, 2023 - Elsevier
Electricity load forecasting is the forecast of power load in the future period based on
historical load and its related factors. It is of great importance for power system planning …
historical load and its related factors. It is of great importance for power system planning …
Soft actor-critic–based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energy
As an essential development direction of energy internet, integrated energy system with
interdisciplinary techniques is of great significance to promote multi-energy cooperation …
interdisciplinary techniques is of great significance to promote multi-energy cooperation …
Privacy preserving load control of residential microgrid via deep reinforcement learning
Demand side management has been proved to be effective in improving the operating
efficiency of microgrids, while posing a severe threat to user privacy. This paper proposes a …
efficiency of microgrids, while posing a severe threat to user privacy. This paper proposes a …
[HTML][HTML] Software-defined control of an emulated hydrogen energy storage for energy internet ecosystems
The increasing heterogeneity and scalability of the Internet of everything, especially, the
Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure …
Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure …