An Agent-Based Extensible Climate Control System for Sustainable Greenhouse Production

Abstract

Abstract. The slow adoption pace of new control strategies for sustainable greenhouse climate control by industrial growers, is mainly due to the complexity of identifying and resolving potentially conflicting climate control requirements. In this paper, we present a multi-agent-based climate control system that allows new control strategies to be adopted without any need to identify or resolve conflicts beforehand. This is achieved by representing the climate control requirements as separate agents. Identifying and solving conflicts then becomes a negotiation problem among agents sharing the same controlled environment. Negotiation is done using a novel multi-objective negotiation protocol that uses a generic algorithm to find an optimized solution within the search space. The multi-agent-based control system has been empirically evaluated in an ornamental floriculture research facility in Denmark. The evaluation showed that it is realistic to implement the climate control requirements as individual agents, thereby opening greenhouse climate control systems for integration of independently produced control strategies.

Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

“…To show their properties in the context of resource allocation, we considered a case with three commercial greenhouse growers with independent energy demands in a setting with a resource domain responsible for allocating energy to each of these entities. We formulated this as a multi-objective optimization problem and solved the problem with Controleum [21,22], a multi-objective optimization framework.…”

“…The multi-objective optimization problem described in Section 5.1 was solved using Controleum [21,22]. Controleum is an object-oriented genetic algorithm framework.…”

Review of nature and biologically inspired metaheuristics for greenhouse environment control

“…In Ghoreishi et al (2015), a comparison of some well-known MOGA is presented for non-linear greenhouse climate control, namely NSGAII, ϵ -NSGA-II, ϵ -MOEA, PAES, PESAII, and SPEAII. The MOEA framework 2.3 and Controleum software (Sørensen et al, 2011) were used to optimise three objective functions considering six decision variables in a dynamic environment. To evaluate the energy cost objective, the forecast data was downloaded automatically from external database servers.…”

Simulation of greenhouse energy use: an application of energy informatics

“…We need to elaborate the simulation model to embrace more decision parameters, such as those considered in prior research (e.g., Clausen et al 2015;Kjaer et al 2011Kjaer et al , 2012Maersk-Møller and Jørgensen 2011;Markvart et al 2007;Rytter et al 2012;Sørensen et al 2011;Sørensen et al 2016), and apply photosynthesis theories that take account of light wavelength and the capability of controlling the wavelength of LED lighting.…”