Energy-saving effect assessment of various factors in container plant factories: A data-driven random forest approach

Kunlang Bu , Zhitong Yu , Dayi Lai , Hua Bao
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Abstract

Plant factory is one of the controlled environment agriculture forms with huge potential to alleviate food crisis, but the high energy cost restricts its widespread adoption. Numerous researches have explored various factors for energy-saving in plant factories in their settings, but there is a lack of analysis of the importance of these factors in energy saving. In this work, the energy-saving effect assessment of various factors in the container plant factory is investigated. Four cities (Harbin, Taiyuan, Shanghai, and Guangzhou), three plant densities (cultivation area: floor area=100 %, 150 %, and 200 %), and two temperature/humidity setpoints (20/22 ℃, 60/70 %, and 16/22 ℃, 50/95 %) are selected as operating conditions to cover different weather conditions and plant heat loads. The energy-saving effect of each factor is calculated using a random forest algorithm based on large amounts of energy simulation data. We identify that envelope overall heat transfer coefficient (U), air conditioner coefficient of performance (COP), and light efficacy (Efficacy) are three factors that have the largest impact on energy-saving in plant factories, in which light efficacy is the most important factor. Simultaneous optimization of these three factors could possibly reduce electricity consumption by ∼50 % compared to the base case. Finally, employing weight-light intensity correlation, the minimum specific energy consumption is approximately 4.76 kWh/kg lettuce fresh weight. This study utilizes advanced machine learning methods to sort out important factors and shows that significant energy reduction may be achieved by optimizing dominant factors, which gives a general guidance for future designers to build energy-efficient plant factories.

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集装箱工厂各种因素的节能效果评估:数据驱动的随机森林方法
植物工厂是一种可控环境农业形式,具有缓解粮食危机的巨大潜力,但高昂的能源成本限制了其广泛应用。已有大量研究探讨了植物工厂设置中的各种节能因素,但缺乏对这些因素在节能中重要性的分析。在这项工作中,研究了集装箱工厂中各种因素的节能效果评估。选取四个城市(哈尔滨、太原、上海和广州)、三种工厂密度(栽培面积:占地面积=100%、150% 和 200%)和两种温度/湿度设定值(20/22 ℃,60/70% 和 16/22 ℃,50/95%)作为运行条件,以涵盖不同的天气条件和工厂热负荷。在大量能源模拟数据的基础上,使用随机森林算法计算了各因素的节能效果。我们发现,围护结构整体传热系数(U)、空调性能系数(COP)和光效(Efficacy)是对工厂节能影响最大的三个因素,其中光效是最重要的因素。与基本情况相比,同时优化这三个因素可能会使耗电量减少 50%。最后,利用重量-光照强度相关性,最低具体能耗约为 4.76 千瓦时/千克生菜鲜重。本研究利用先进的机器学习方法对重要因素进行了梳理,结果表明,通过优化主导因素,可以显著降低能耗,这为未来设计人员建造节能型工厂提供了总体指导。
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