Multi Energy

Multi-energy systems research focuses on optimizing the integrated management of electricity, heat, and gas networks, aiming to improve efficiency, sustainability, and economic viability of energy production and distribution. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs) for demand prediction and reinforcement learning (RL) algorithms, often incorporating transformer architectures, for optimal scheduling and control across multiple energy vectors and communities. These advancements are crucial for enabling the transition to cleaner energy systems by improving forecasting accuracy, reducing operational costs, and facilitating the integration of renewable energy sources and carbon capture technologies.

Papers

February 8, 2024

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin
Zero Shot Solar Power Solar Forecasting Multi Energy Heterogeneous Modality

December 24, 2023

Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems
Corneliu Arsene
Deep Convolutional Neural Network Consumption Forecasting Energy System Multi Energy

August 24, 2023

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
Yang Li, Wenjie Ma, Fanjin Bu, Zhen Yang, Bin Wang, Meng Han
Data Driven Multi Agent Deep Reinforcement Learning Deep Reinforcement Energy System Multi Energy

July 11, 2023

Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
Rixin Wu, Ran Wang, Jie Hao, Qiang Wu, Ping Wang
Reinforcement Learning Reservoir System Multi Energy Hydropower Plant Management

April 18, 2023

An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke, Ann Nowé, Maarten Messagie
Reinforcement Learning Algorithm Safe Reinforcement Learning Hard Constraint Safety Layer Multi Energy

January 18, 2023

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
Tobi Michael Alabi, Nathan P. Lawrence, Lin Lu, Zaiyue Yang, R. Bhushan Gopaluni
Deep Reinforcement Learning System Description Energy Prediction Real Time Scheduling Decarbonization Study Carbon Figure Carbon Capture Multi Energy

September 24, 2022

Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints
Aron Brenner, Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin
Application Proficiency Graph Representation Learning Energy Consumption Joint Decision Making Environmental Constraint Multi Energy Energy Dispatch

July 8, 2022

Safe reinforcement learning for multi-energy management systems with known constraint functions
Glenn Ceusters, Luis Ramirez Camargo, Rüdiger Franke, Ann Nowé, Maarten Messagie
Reinforcement Learning Safe Reinforcement Learning Safety Constraint Multi Energy Constraint Function

Multi Energy

Papers

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting

Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning

An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints

Safe reinforcement learning for multi-energy management systems with known constraint functions