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Found 8 papers, 1 papers with code
HomeLabGym: A real-world testbed for home energy management systems
We present an overview of HomeLabGym, and demonstrate its usefulness to researchers in a comparison between real-world and simulated environments in controlling a residential battery in response to real-time prices.
Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
We aim to address this challenging problem and introduce a reinforcement learning-based approach using differentiable decision trees.
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers
Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) source of energy flexibility.
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks
Thus, we study MCTS specifically for building demand response.
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
Specifically, we train an advanced forecasting model (a temporal fusion transformer) using data from multiple different households, and then finetune this global model on a new household with limited data (i. e. only a few days).
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households
Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid.
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control
As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning.
Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
To combine the interpretability of white/gray box physics models and the expressive power of neural networks, we propose a physics informed neural network approach for this modeling task.
