Abstract
Artificial intelligence has substantially improved the efficiency of data utilization across various sectors. However, the insufficient filtering of low-quality data poses challenges to uncertainty management, threatening system stability. In this study, we introduce a data-valuation approach employing deep reinforcement learning to elucidate the value patterns in data-driven tasks. By strategically optimizing with iterative sampling and feedback, our method is effective in diverse scenarios and consistently outperforms the classic methods in both accuracy and efficiency. In China's wind-power prediction, excluding 25% of the overall dataset deemed low-value led to a 10.5% improvement in accuracy. Utilizing just 42.8% of the dataset, the model discerned 80% of linear patterns, showcasing the data's intrinsic and transferable value. A nationwide analysis identified a data-value-sensitive geographic belt across 10 provinces, leading to robust policy recommendations informed by variances in power outputs and data values, as well as geographic climate factors.