Extending the CityLearn Framework with Electric Vehicle Simulation
This paper introduces EVLearn, a simulation module for researching Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) energy management strategies, and integrates it with the existing CityLearn framework to provide a comprehensive testbed for developing and evaluating energy management algorithms in the context of Energy Communities.
Generalized Policy Learning for Smart Grids: FL TRPO Approach
Applying Federated Learning with Trust Region Policy Optimization (FL TRPO) enhances smart grid policy models, reducing emissions and costs effectively.
Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions
Implementing resource-efficient energy management systems requires identifying meaningful configuration concepts through concept identification techniques.
Distill2Explain: Differentiable Decision Trees for Explainable Reinforcement Learning in Energy Application Controllers
提案された方法は、説明可能な決定木を使用してRLベースの制御ポリシーを獲得し、満足できる制御性能と説明可能性を達成します。
Explainable Reinforcement Learning-based Home Energy Management Systems Using Differentiable Decision Trees
Introducing explainable RL-based controllers for efficient home energy management.
Differentiable Decision Trees for Explainable Reinforcement Learning in Energy Application Controllers
Proposing differentiable decision trees for explainable RL policies in home energy management.
Non-Intrusive Load Monitoring with Tensor Decomposition
The author introduces a PID-incorporated Non-negative Latent Factorization of Tensors (PNLFT) model to address missing data in Non-Intrusive Load Monitoring (NILM) efficiently.
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