Recursive Self-Optimizing Neural Architectures for Ultra-High-Resolution Wind Speed Forecasting and Autonomous Grid-Stabilization Protocols Under Extreme Uncertainty

Abstract

Wind power is inherently intermittent, and the value of any grid integration strategy is bounded by how well short horizon wind behaviour can be predicted and how safely that prediction is turned into an operating decision. This paper presents a recursive self-optimizing neural forecasting architecture, referred to as RSO-Net, for ultra-high-resolution wind speed prediction at sub-minute granularity, paired with a multi-agent software system that converts probabilistic forecasts into grid-stabilization recommendations under a mandatory human approval gate. The term recursive self-optimizing describes a bounded, logged, and reversible outer-loop controller that adjusts learning hyperparameters between short adaptation windows based on rolling validation performance; it does not describe, and this paper does not propose, a system that acts on the grid without human authorization. We detail the theoretical background of temporal convolution and self-attention based probabilistic forecasting, review the OpenAI Agents SDK as an example orchestration substrate, describe a six-agent architecture covering ingestion, anomaly detection, forecasting, knowledge retrieval, stabilization proposal, and human approval, and report experimental comparisons on synthetic high-resolution wind data against persistence, ARIMA, and standard LSTM baselines. The proposed RSO-Net achieves a root mean square error of 0.71 m/s versus 1.10 m/s for a standard LSTM, and simulated frequency response shows materially reduced deviation when agent proposed actions are reviewed and approved before dispatch. We conclude with a discussion of the safety, auditability, and human oversight properties that we consider prerequisites for any such system prior to field deployment.