Learning Control Policies from Heterogeneous Multi-Horizon Time Series in Battery Energy Management Systems

arXiv:2609.22546 · eess.SY, cs.SY · Submitted 2026-09-18 · Read on arXiv

eess.SY, cs.SY

Submitted: 2026-09-18

Updated: 2026-09-18

Terminology

Benchmark — 37× in this paper · explained in 2 episodes
Transformer — 29× in this paper · explained in 2 episodes
Reinforcement Learning — 26× in this paper · explained in 4 episodes
Behavior Cloning — 13× in this paper
Energy Management System — 13× in this paper
Deep Reinforcement Learning — 10× in this paper · explained in 2 episodes
Latent Representation — 10× in this paper · explained in 2 episodes
State of Charge — 10× in this paper
Embeddings — 9× in this paper · explained in 2 episodes
Ablation — 8× in this paper · explained in 1 episode
Generalization — 8× in this paper · explained in 13 episodes
Fidelity — 7× in this paper · explained in 2 episodes
Linear Programming — 7× in this paper
Model Predictive Control — 7× in this paper
Objective Function — 6× in this paper · explained in 2 episodes
Accuracy — 5× in this paper · explained in 1 episode
State of Health — 5× in this paper
Mixed-Integer Linear Programming — 4× in this paper
Robustness — 4× in this paper · explained in 5 episodes
Ablation study — 3× in this paper · explained in 1 episode
Actor — 3× in this paper · explained in 3 episodes
Decoupling — 3× in this paper · explained in 2 episodes
Latent Representations — 3× in this paper · explained in 3 episodes
Mean Square Error — 3× in this paper
Benchmarking — 2× in this paper · explained in 3 episodes
Core — 2× in this paper · explained in 2 episodes
Day-Ahead Market — 2× in this paper
Deep Q-Network — 2× in this paper
Generative Adversarial Imitation Learning — 2× in this paper
Interpretability — 2× in this paper · explained in 15 episodes
Latent Space — 2× in this paper · explained in 7 episodes
Long Short-Term Memory — 2× in this paper
Mean Absolute Error — 2× in this paper
Model Predictive Control (MPC) — 2× in this paper · explained in 5 episodes
Proximal Policy Optimization — 2× in this paper
Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
Time of Use — 2× in this paper
Transferability — 2× in this paper · explained in 4 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Adaptation — 1× in this paper · explained in 1 episode
Adversarial Inverse Reinforcement Learning (AIRL) — 1× in this paper · explained in 1 episode
Aggregation — 1× in this paper · explained in 1 episode
Architectural Adaptation — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 3 episodes
Continual Learning — 1× in this paper · explained in 2 episodes
Distribution Shift — 1× in this paper · explained in 5 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Generative AI — 1× in this paper · explained in 2 episodes
LSTM and Temporal Convolutional Network — 1× in this paper
Loss Function — 1× in this paper · explained in 2 episodes
Modular Architecture — 1× in this paper · explained in 4 episodes
Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
Proximal Policy Optimization (PPO) — 1× in this paper · explained in 5 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
System Identification — 1× in this paper · explained in 3 episodes
Transformer Models — 1× in this paper · explained in 2 episodes

Terminology

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