DynaGrow

Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses

Jan Corfixen Sørensen*, Katrine Heinsvig Kjaer, Carl Otto Ottosen, Bo Nørregaard Jørgensen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

It is not possible for growers to compromise product quality by saving energy but the increasing electricity prices challenge the growers economically. Optimization of such multiple conflicting goals requires advanced strategies that are currently not supported in existing greenhouse climate control systems. DynaGrow is built on top of the existing climate control computers and utilizes the existing hardware. By integrating with exiting hardware it is possibly to support advanced multi-objective optimization of climate parameters without investing in new hardware. Furthermore, DynaGrow integrates with local climate data, electricity price forecasts and outdoor weather forecasts, in order to formulate advanced control objectives. In September 2014 and February 2015 two greenhouse experiments were run to evaluate the effects of DynaGrow. By applying multi-objective optimization, it was possible to produce a number of different cultivars and save energy without compromising quality. The best energy savings were achieved in the February 2015 experiment where the contribution from natural light was limited.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer VS
Publication date1. Jan 2019
Pages25-44
DOIs
Publication statusPublished - 1. Jan 2019
SeriesStudies in Computational Intelligence
Volume792
ISSN1860-949X

Fingerprint

Climate control
Greenhouses
Multiobjective optimization
Energy conservation
Electricity
Hardware
Computer hardware
Costs
Experiments
Control systems

Keywords

  • Multi-objective optimization

Cite this

Sørensen, J. C., Kjaer, K. H., Ottosen, C. O., & Jørgensen, B. N. (2019). DynaGrow: Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. In Studies in Computational Intelligence (pp. 25-44). Springer VS. Studies in Computational Intelligence, Vol.. 792 https://doi.org/10.1007/978-3-319-99283-9_2
Sørensen, Jan Corfixen ; Kjaer, Katrine Heinsvig ; Ottosen, Carl Otto ; Jørgensen, Bo Nørregaard. / DynaGrow : Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. Studies in Computational Intelligence. Springer VS, 2019. pp. 25-44 (Studies in Computational Intelligence, Vol. 792).
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Sørensen, JC, Kjaer, KH, Ottosen, CO & Jørgensen, BN 2019, DynaGrow: Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. in Studies in Computational Intelligence. Springer VS, Studies in Computational Intelligence, vol. 792, pp. 25-44. https://doi.org/10.1007/978-3-319-99283-9_2

DynaGrow : Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. / Sørensen, Jan Corfixen; Kjaer, Katrine Heinsvig; Ottosen, Carl Otto; Jørgensen, Bo Nørregaard.

Studies in Computational Intelligence. Springer VS, 2019. p. 25-44 (Studies in Computational Intelligence, Vol. 792).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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AB - It is not possible for growers to compromise product quality by saving energy but the increasing electricity prices challenge the growers economically. Optimization of such multiple conflicting goals requires advanced strategies that are currently not supported in existing greenhouse climate control systems. DynaGrow is built on top of the existing climate control computers and utilizes the existing hardware. By integrating with exiting hardware it is possibly to support advanced multi-objective optimization of climate parameters without investing in new hardware. Furthermore, DynaGrow integrates with local climate data, electricity price forecasts and outdoor weather forecasts, in order to formulate advanced control objectives. In September 2014 and February 2015 two greenhouse experiments were run to evaluate the effects of DynaGrow. By applying multi-objective optimization, it was possible to produce a number of different cultivars and save energy without compromising quality. The best energy savings were achieved in the February 2015 experiment where the contribution from natural light was limited.

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Sørensen JC, Kjaer KH, Ottosen CO, Jørgensen BN. DynaGrow: Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. In Studies in Computational Intelligence. Springer VS. 2019. p. 25-44. (Studies in Computational Intelligence, Vol. 792). https://doi.org/10.1007/978-3-319-99283-9_2