Digital technologies for a net-zero energy future: A comprehensive review
The energy sector plays a vital role in achieving a sustainable net-zero future, and the
adoption of advanced technologies such as AI, blockchain, quantum computing, digital twin …
adoption of advanced technologies such as AI, blockchain, quantum computing, digital twin …
Distributed intelligence for IoT-based smart cities: a survey
The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of
distributed intelligence are promising research paradigms in the design of smart cities. IoT …
distributed intelligence are promising research paradigms in the design of smart cities. IoT …
Reinforcement learning for sustainable energy: A survey
K Ponse, F Kleuker, M Fejér, Á Serra-Gómez… - arXiv preprint arXiv …, 2024 - arxiv.org
The transition to sustainable energy is a key challenge of our time, requiring modifications in
the entire pipeline of energy production, storage, transmission, and consumption. At every …
the entire pipeline of energy production, storage, transmission, and consumption. At every …
Secure short-term load forecasting for smart grids with transformer-based federated learning
J Sievers, T Blank - 2023 International Conference on Clean …, 2023 - ieeexplore.ieee.org
Electricity load forecasting is an essential task within smart grids to assist demand and
supply balance. While advanced deep learning models require large amounts of high …
supply balance. While advanced deep learning models require large amounts of high …
Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis
Data-driven machine learning-based methods have provided immense capabilities,
revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the …
revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the …
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with
solving sequential decision-making problems by a learning agent that interacts with the …
solving sequential decision-making problems by a learning agent that interacts with the …
Federated double DQN based multi-energy microgrid energy management strategy considering carbon emissions
Y Yang, T Ma, H Li, Y Liu, C Tang, W Pei - Global Energy Interconnection, 2023 - Elsevier
Multi-energy microgrids (MEMG) play an important role in promoting carbon neutrality and
achieving sustainable development. This study investigates an effective energy …
achieving sustainable development. This study investigates an effective energy …
Advancing Accuracy in Energy Forecasting using Mixture-of-Experts and Federated Learning
J Sievers, T Blank, F Simon - Proceedings of the 15th ACM International …, 2024 - dl.acm.org
Accurate forecasting of load, photovoltaic (PV), and prosumption is essential for energy
systems to reliably plan and operate smart grids, improve energy storage optimization, or …
systems to reliably plan and operate smart grids, improve energy storage optimization, or …
Generalized Policy Learning for Smart Grids: FL TRPO Approach
The smart grid domain requires bolstering the capabilities of existing energy management
systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable …
systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable …
Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning
L Peng, F He, G Hasegawa, Y Cheng… - 2023 IEEE 29th …, 2023 - ieeexplore.ieee.org
In recent years, there's been a surge in the popularity and affordability of distributed power
generation equipment, such as photovoltaic systems (PV) and energy storage systems. At …
generation equipment, such as photovoltaic systems (PV) and energy storage systems. At …