Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants

TL;DR

This paper presents a novel multi-robot coverage control method using dissimilarity maps to dynamically position sensors, significantly enhancing solar irradiance prediction accuracy in power plants.

Contribution

It introduces a dissimilarity-based persistent coverage algorithm that guides mobile sensors to optimize prediction accuracy in real time.

Findings

Achieves more accurate irradiance predictions than baseline methods.

Effectively guides robots to regions needing additional data.

Improves forecasting with fewer sensors.

Abstract

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.