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首页> 外文期刊>ACM transactions on sensor networks >OPTICS: OPTimizing Irrigation Control at Scale
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OPTICS: OPTimizing Irrigation Control at Scale

机译：光学：在规模上优化灌溉控制

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摘要

Lawns, also known as turf, cover an estimated 128,000 km(2) in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or do not revise the strategy over time in response to collected data. In this work, we introduce OPTICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that OPTICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state of the art. Despite using less water, OPTICS also was found to improve quality of service by a factor of 4.0x compared to industry best and 2.5x compared to academic state of the art.
机译：亦称草坪的草坪单独覆盖北美估计的128,000公里（2）厘米，景观要求代表住宅领域的30％淡水。随着这种消费，具有大量的环境，经济和社会动力，使草皮灌溉系统尽可能高。最近的工作介绍了灌溉系统中分布式控制的概念，但现有的控制策略不利用分布式控制，或者在响应收集的数据时不随时间修改策略。在这项工作中，我们引入了光学元件，一种数据驱动的控制策略，即自我改善随着时间的推移，适应本地特定条件和天气变化，并且在设置和维护中几乎没有人类输入提供即插即用系统这需要最小的预部署努力。除了在易用性的大量改进之外，我们发现在大规模灌溉系统部署的4周内发现，与工业最佳和3.3％相比，光学器件与工业最佳效率相比，光学系统效率提高了12.0％。尽管使用较少的水，但光学也被发现将服务质量提高到4.0倍与行业最佳和2.5倍，与学术艺术相比。

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