Value-oriented policy distillation for interpretable wind farm power control
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
The paper introduces MTD3-BC, a model‑free offline reinforcement learning algorithm that optimizes yaw control for wind farms amid changing wind directions. By learning from a pre‑collected dataset and incorporating an action consistency term, it reduces the need for extensive simulator interactions. Experimental wind‑tunnel tests show that MTD3‑BC improves farm‑level power output by about 10% compared to a greedy baseline and matches a model‑based benchmark, all while cutting training costs dramatically.
arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.
The paper presents a tailored GradientSHAP method to explain multivariate, multistep wind power forecasts, addressing the opacity of AI models in renewable energy. It introduces new visualizations that reveal local and global relationships among environmental factors, site conditions, and time dynamics. The study claims to be the first to apply GradientSHAP to wind power forecasting, highlighting its potential for day‑ahead grid management while noting implementation challenges.
This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios.
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
arXiv:2602. 13010v2 Announce Type: replace Abstract: Accurate production forecasts are essential for the integration of renewable energy sources into the power grid.