A multi-objective optimization platform for artificial lighting system in commercial greenhouses

Ying Qu,Anders Clausen,Bo Nørregaard Jørgensen

doi:10.1186/s42162-021-00162-8

Ying Qu, Anders Clausen + Show 1 more

Open Access

https://doi.org/10.1186/s42162-021-00162-8

Copy DOI

Abstract

Limited natural daylight in Nordic Countries means artificial lighting is a critical factor in industrial plant production. The electricity cost of artificial lights accounts for a large percentage of the overall cost of plant production. The optimal use of artificial lighting in plant production can be formulated as a multi-objective problem (MOP) to achieve optimal plant growth while minimizing electricity cost. In previous work, for solving this MOP, a Genetic Algorithm (GA) was used to create a Pareto Frontier (PF), which contains solutions representing a trade-off for using artificial lighting against plant production objectives. The PF was updated immediately once a non-dominated child-solution was found by comparing the dominance with solutions in the PF. Besides, in addition to the PF, the initial random population is also reused as a parent source in the evolution process. When the genetic evolution process terminated, a priority-based selection mechanism was used to select a final solution from the PF. In this paper, an alternative evolution strategy is proposed and compared with the previous GA evolution strategy. By this alternative strategy, all child-solutions are only compared with their parents during the evolution process, and the non-dominated child-solutions are collected into a candidate list. The PF is then updated at the end of each generation by comparing solutions on the PF with the collected candidate solutions. In this alternative strategy, the PF is the only source of parent-solution during the evolution process. In addition, a posterior normalization is implemented in the dominance evaluation, and social welfare metrics (SWs) are applied as an alternative to the priority-based selection mechanism to avoid the explicit ranking of objectives. The experimental results show that the proposed alternative evolution strategy outperforms the previous strategy on dramatically avoiding local minima.

Highlights

Greenhouses are complex systems that can provide the required environment for plant production
For solving this multi-objective problem (MOP), a Genetic Algorithm (GA) was used to create a Pareto Frontier (PF), which contains solutions representing a trade-off for using artificial lighting against plant production objectives
A posterior normalization is implemented in the dominance evaluation, and social welfare metrics (SWs) are applied as an alternative to the priority-based selection mechanism to avoid the explicit ranking of objectives

Summary

Introduction

Greenhouses are complex systems that can provide the required environment for plant production. There are two types of energy consumption in a greenhouse system – the basic energy consumption and the actuator driving consumption. The basic energy consumption, including artificial lighting, dehumidification, heating, cooling, etc., accounts for more than 90% of the total energy consumption in the greenhouse (Kjaer et al 2011; Hemming et al 2017). Aside from this, artificial lighting systems consume approximately 75% of the total energy consumption (Sørensen et al 2016a; Kjaer et al 2012). This makes the energy-efficient operation of artificial light critical in commercial plant production

Results

Discussion

Conclusion