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

doi: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

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

^*Corresponding author for this work

Aarhus University

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	Computational Intelligence : International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers
Editors	Julian Merelo, Fernando Melicio, José M. Cadenas, António Dourado, Kurosh Madani, António Ruano, Joaquim Filipe
Publisher	Springer
Publication date	2019
Pages	25-44
ISBN (Print)	978-3-319-99282-2
ISBN (Electronic)	978-3-319-99283-9
DOIs	https://doi.org/10.1007/978-3-319-99283-9_2
Publication status	Published - 2019
Event	International Joint Conference on Computational Intelligence, IJCCI 2016 Porto - Porto, Portugal Duration: 9. Nov 2016 → 11. Nov 2016

Conference

Conference	International Joint Conference on Computational Intelligence, IJCCI 2016 Porto
Country/Territory	Portugal
City	Porto
Period	09/11/2016 → 11/11/2016

Series	Studies in Computational Intelligence
Volume	792
ISSN	1860-949X

Keywords

Multi-objective optimization

Documents & Links

10.1007/978-3-319-99283-9_2

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 J. Merelo, F. Melicio, J. M. Cadenas, A. Dourado, K. Madani, A. Ruano, & J. Filipe (Eds.), Computational Intelligence: International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers (pp. 25-44). Springer. https://doi.org/10.1007/978-3-319-99283-9_2

Sørensen, Jan Corfixen ; Kjaer, Katrine Heinsvig ; Ottosen, Carl Otto et al. / DynaGrow : Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses. Computational Intelligence: International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers. editor / Julian Merelo ; Fernando Melicio ; José M. Cadenas ; António Dourado ; Kurosh Madani ; António Ruano ; Joaquim Filipe. Springer, 2019. pp. 25-44 (Studies in Computational Intelligence, Vol. 792).

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title = "DynaGrow: Next generation software for multi-objective and energy cost-efficient control of supplemental light in greenhouses",

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.",

keywords = "Multi-objective optimization",

author = "S{\o}rensen, {Jan Corfixen} and Kjaer, {Katrine Heinsvig} and Ottosen, {Carl Otto} and J{\o}rgensen, {Bo N{\o}rregaard}",

year = "2019",

doi = "10.1007/978-3-319-99283-9_2",

language = "English",

isbn = "978-3-319-99282-2",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "25--44",

editor = "Merelo, {Julian } and Fernando Melicio and Cadenas, {Jos{\'e} M.} and Ant{\'o}nio Dourado and Kurosh Madani and Ant{\'o}nio Ruano and Joaquim Filipe",

booktitle = "Computational Intelligence",

address = "Germany",

note = "International Joint Conference on Computational Intelligence, IJCCI 2016 Porto, IJCCI ; Conference date: 09-11-2016 Through 11-11-2016",

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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 J Merelo, F Melicio, JM Cadenas, A Dourado, K Madani, A Ruano & J Filipe (eds), Computational Intelligence: International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers. Springer, Studies in Computational Intelligence, vol. 792, pp. 25-44, International Joint Conference on Computational Intelligence, IJCCI 2016 Porto, Porto, Portugal, 09/11/2016. 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 et al.
Computational Intelligence: International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers. ed. / Julian Merelo; Fernando Melicio; José M. Cadenas; António Dourado; Kurosh Madani; António Ruano; Joaquim Filipe. Springer, 2019. p. 25-44 (Studies in Computational Intelligence, Vol. 792).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - DynaGrow

T2 - International Joint Conference on Computational Intelligence, IJCCI 2016 Porto

AU - Sørensen, Jan Corfixen

AU - Kjaer, Katrine Heinsvig

AU - Ottosen, Carl Otto

AU - Jørgensen, Bo Nørregaard

PY - 2019

Y1 - 2019

N2 - 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.

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.

KW - Multi-objective optimization

U2 - 10.1007/978-3-319-99283-9_2

DO - 10.1007/978-3-319-99283-9_2

M3 - Article in proceedings

AN - SCOPUS:85054367072

SN - 978-3-319-99282-2

T3 - Studies in Computational Intelligence

SP - 25

EP - 44

BT - Computational Intelligence

A2 - Merelo, Julian

A2 - Melicio, Fernando

A2 - Cadenas, José M.

A2 - Dourado, António

A2 - Madani, Kurosh

A2 - Ruano, António

A2 - Filipe, Joaquim

PB - Springer

Y2 - 9 November 2016 through 11 November 2016

ER -

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 Merelo J, Melicio F, Cadenas JM, Dourado A, Madani K, Ruano A, Filipe J, editors, Computational Intelligence: International Joint Conference, IJCCI 2016 Porto, Portugal, November 9–11, 2016 Revised Selected Papers. Springer. 2019. p. 25-44. (Studies in Computational Intelligence, Vol. 792). doi: 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

Abstract

Conference

Keywords

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