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Found 11 papers, 1 papers with code
Smart Energy Network Digital Twins: Findings from a UK-Based Demonstrator Project
Power meter data and a network model are shown to be necessary for developing algorithms that enable decision-making that is robust to real-world uncertainties, with possibilities and challenges of Digital Twin development clearly demonstrated.
Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand using Deep Reinforcement Learning
Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production.
The role of living laboratories in unlocking the potential of low-carbon energy technologies on the journey to net-zero
We demonstrate the potential role of one of the largest at scale multi-vector Smart Energy Network Demonstrator (SEND).
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning
In this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading.
Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints
In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading.
The energy revolution: cyber physical advances and opportunities for smart local energy systems
SLES are often developed for a specific range of use cases and functions, and these match the specific requirements and needs of the community, location or site under consideration.
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy.
Federated Learning for Short-term Residential Load Forecasting
Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid.
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations.
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour.
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion -- as is typical in real world situations -- the joint model produced by FL suffers in terms of test set accuracy and/or communication costs compared to training on iid data.
