Authors:
Jan Corfixen Sørensen
1
;
Katrine Heinsvig Kjaer
2
;
Carl-Otto Ottosen
2
and
Bo Nørregaard Jørgensen
1
Affiliations:
1
University of Southern Denmark, Denmark
;
2
Aarhus University, Denmark
Keyword(s):
Multi-Objective Optimization, Greenhouse Climate Control, Supplemental Light, Weather Forecast, Electricity Cost, Decision Support, Energy Saving.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
The Danish greenhouse horticulture industry utilized 0.8 % of the total national electricity consumption in 2009 and it is estimated that 75 % of this is used for supplemental lighting. The increase in energy prices is a challenge for growers, and need to be addressed by utilizing supplemental light at low prices without compromising the growth and quality of the crop. Optimization of such multiple conflicting objectives requires advanced strategies that are currently not supported in existing greenhouse climate control systems. To incorporate advanced optimization functionality into existing systems is costly as the software is not designed for such changes. The growers can not afford to buy new systems or new hardware to address the changing objectives. DynaGrow is build on top of existing climate computers to utilize existing infrastructure. The greenhouse climate control problem is characterized by nonlinearity, stochasticity, non-convexity, high dimension of decision variables a
nd an uncertain dynamic environment. Together, these mathematical properties are handled by applying a Multi-Objective Evolutionary Algorithm (MOEA) for discovering and exploiting critical trade-offs when optimizing the greenhouse climate. To formulate advanced objectives DynaGrow integrates local climate data, electricity energy price forecasts and outdoor weather forecasts. In spring 2015 one greenhouse experiment was executed to evaluate the effects of DynaGrow. The experiment was run as four treatments in four identical greenhouse compartments. One treatment was controlled by a standard control system and the other three treatments were controlled by different DynaGrow configurations. A number of different plant species and batches were grown in the four treatments over a season. The results from DynaGrow treatment demonstrated that it was clearly possible to produce a number of different sales-ready plant species and at the same time optimize the utility of supplemental light at low electricity prices without compromising quality.
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