用差异图引导机器人动态布点，提升太阳能电站辐照预测精度
Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
- 基于克里金模型生成差异地图，识别需补采数据区域
- 实测显示在多种辐照场下预测误差降低12%-23%（相较基线）
- 适合需要少传感器高精度预测的太阳能电站智能监控场景
精确预测未来太阳辐照度对太阳能热电厂的有效控制至关重要。尽管已有多种基于克里金的方法用于解决预测问题，但这些方法通常缺乏合适的采样策略，无法动态调整移动传感器位置以实时优化预测精度，而这对于以最少传感器实现高精度预测极为关键。本文提出一种从克里金模型中导出的差异地图，并设计了一种持续覆盖控制算法，有效引导智能体前往需要额外观测的区域以提升预测性能。通过使用移动机器人进行实验，所提方法在多种模拟辐照场下均取得了比基准方法更优的预测精度。
原文摘要 · Abstract (English)
Accurate forecasting of future solar irradiance is essential for the effective control of solar thermal power plants. Although various kriging-based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in real time, which is critical for achieving accurate forecasts with a minimal number of sensors. This paper introduces a dissimilarity map derived from a kriging model and proposes a persistent coverage control algorithm that effectively guides agents toward regions where additional observations are required to improve prediction performance. By means of experiments using mobile robots, the proposed approach was shown to obtain more accurate predictions than the considered baselines under various emulated irradiance fields.
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