How connected cars could capture cloud dynamics -- first evidence from two simulation scenarios

TL;DR

This paper explores using automotive light sensors as a scalable sensor network to estimate cloud movement, demonstrating promising results in simulation scenarios with potential for improving cloud dynamics estimation.

Contribution

It introduces a novel approach of using vehicle-based sensors for cloud motion estimation, expanding beyond traditional local sensor networks.

Findings

Sensor networks can compensate for cloud dynamics over larger areas.

Estimation accuracy decreases with lower vehicle penetration rates.

Root mean square errors remain below 5 m/s at 40% vehicle penetration.

Abstract

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image-based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal-based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40 %, the root mean square errors for both sensor networks are still below 5 m/s. In conclusion, the spatio-temporal characteristics of a vehicle network offer some potential for estimating cloud movements.