DynaGrow – Multi-Objective Optimization for Energy Cost-efficient
Control of Supplemental Light in Greenhouses
Jan Corfixen Sørensen
1
, Katrine Heinsvig Kjaer
2
, Carl-Otto Ottosen
2
and Bo Nørregaard Jørgensen
1
1
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
2
Department of Horticulture, Aarhus University, Kirstinebjergvej 10, 5792 Aarslev, Denmark
Keywords:
Multi-Objective Optimization, Greenhouse Climate Control, Supplemental Light, Weather Forecast, Electric-
ity Cost, Decision Support, Energy Saving.
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 com-
promising 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. It is costly
to incorporate advanced optimization functionality into existing systems 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 non-linearity , stochasticity, non-convexity, high di-
mension of decision variables and 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
three treatments in three identical greenhouse compartments. One treatment was controlled by a standard con-
trol system and the other three treatments were controlled by different DynaGrow configurations. A number
of different plant species and batches were grown in the three 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 product quality.
1 INTRODUCTION
In 2009, Danish horticulture industry consumed 0.8
% of the total national electricity and 75 % of this
consumed energy was estimated to come from sup-
plemental light alone.
Several countries are at present time in a transition
towards non-fossil renewable energy sources such as
wind turbines. The large contribution of energy from
renewable energy sources results in irregular electric-
ity production, that leads to fluctuation in the elec-
tricity prices. An increased electricity price is a chal-
lenge for the horticulture industry that is positioned in
a highly competitive market. A spot market electricity
price structure has been introduced in the Scandina-
vian countries to provide an incentive for industry to
utilize energy in cost effective hours where the supply
of energy is plentiful (Nord Pool, 2016).
Existing fixed rate supplemental light control
strategies are in contradiction to the flexible price
structure. Fixed rate light-plans often consume en-
ergy at hours that are costly. The fixed rate strate-
gies only plan according to the fixed time periods
and do not take price structures into account. How-
ever, changing the light patterns may have severe ef-
fects on how the cultivar reacts. For example, neg-
ative effects could be bud dormancy, delayed leaf
development, stem elongation, late seed germination
and early flower initiation (Thomas and Vince-Prue,
1997). Hence, there is a need to optimize the util-
ity of supplemental light to ensure that the light-plans
represent the cheapest electricity prices and promote
Sørensen, J., Kjaer, K., Ottosen, C-O. and Jørgensen, B.
DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses.
DOI: 10.5220/0006047500410048
In Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) - Volume 1: ECTA, pages 41-48
ISBN: 978-989-758-201-1
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
41
a high quality of the produced cultivar.
This paper proposes a system DynaGrow that in-
tegrates local climate data, electricity price and out-
door weather forecasts to formulate advanced control
objectives. The core of the system is a customized
Multi-Objective Evolutionary Algorithm (MOEA),
that searches for coordinated Pareto-optimal light-
plans that fulfil the specified climate control objec-
tives. DynaGrow is a features-oriented software sys-
tem divided into a number of features. Each feature
encompasses an individual unit of software function-
ality and is implemented as loosely coupled plug-in
modules. The feature-oriented separation of Dyna-
Grow makes it relatively strait-forward to integrate to
existing hardware devices and configure the system to
optimize different objectives. Each set of objectives
are strictly separated and can be configured to fulfill
the specific climate control requirements given by the
problem domain. Present time, DynaGrow supports
two different climate control hardware platforms and
support 37 different objectives (Sørensen et al., 2011).
Related work is shortly summarized in Section 2
Section 3 describes the different elements of Dyna-
Grow and how they are connected. The most impor-
tant control objectives optimized in the presented ex-
periment, are formulated and detailed in Section 4.
How the DynaGrow core is implemented as a genetic
MOEA is described in Section 5. Section 6 describes
the experiment that evaluate DynaGrow by growing
three different cultivars based on three different cli-
mate control settings. The results of the experiment is
described in Section 7. Next, the discussion reflects
on how well DynaGrow optimized the identified ob-
jectives of the climate control problem. Last, Sec-
tion 9 summarizes the article.
2 RELATED WORK
Research literature describes independent models that
can contribute to an optimized greenhouse production
and cut the energy consumption through development
of intelligent control strategies. Aaslyng et. al created
the foundation for a component-based climate con-
trol system IntelliGrow that optimizes the greenhouse
climate (Aaslyng et al., 1996). The results showed
that it was possible to reduce energy consumption by
more than 20 %. Subsequently, there have been sev-
eral projects in which the models and control strate-
gies have been optimized (Aaslyng et al., 1999; Lund
et al., 2006). The IntelliGrow concept is documented
by Aaslyng et al. in (Aaslyng et al., 2005). Kjaer et
al. developed the DynaLight system that provides a
search-based approach to find the most cost-efficient
use of supplemental light, based on a predefined set-
point of Daily Photosynthesis Integral (DPI), fore-
casted solar light and the spot-market electricity price
(Kjaer and Ottosen, 2011; Kjaer et al., 2012; Clausen
et al., 2012). The DynaLight algorithm is tightly in-
tegrated with the weather and electricity forecast ser-
vices and does not support optimization multiple ob-
jectives.
None of the mentioned approaches support Pareto
optimization of multiple independently developed ob-
jectives, to generate a coordinated set of setpoints that
can be effectuated by the greenhouse climate control
system. DynaGrow is the only approach that supports
optimization of multiple objectives that are based on
weather and electricity price forecasts.
3 DynaGrow
DynaGrow is designed to control climate-related
growth factors by sensing and manipulating the
greenhouse climate through the use of sensors and ac-
tuators. The physical setting of DynaGrow is a com-
bination of a control machine, a number of connection
domains and a controlled domain (Figure 1). The con-
trol machine consists of the DynaGrow software run-
ning on a PC, that is connected to a set of climate con-
trollers. The climate controllers are connected to sen-
sors and actuators (Connection Domains) that interact
with the indoor climate of the greenhouse (Controlled
Domain). A connection domain can act as a sensor
or an actuator. Sensors provide measured input infor-
mation m in form of input variables i to the control
machine. Contrary, actuators influence the physical
phenomenon c in the controlled domain according to
output variables o provided by the machine.
Control Machine
Controlled Domain
Connection Domains
Sensors (S)
Actuators (A)
i
o
Environment (E)
m
c
DynaLight NG
Controller
Figure 1: DynaGrow system overview.
The output of DynaGrow is determined by the
system’s control objectives that typically incorporates
models of the physical environment. For example, a
model of the photosynthesis process of a given plant
in a greenhouse. Control objectives are formulated
over a set of input variables i and output variables
o, connecting the control machine with the connected
domains. The control objectives are evaluated contin-
uously in cycles by the machine, and is integrated into
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
42
the control process. Each cycle is triggered at specific
time intervals. For each control cycle, the system per-
ceives the environment through its sensors variable i,
optimizes the control objectives and changes the en-
vironment through its actuators to obtain the desired
objective of the system. The result of a control cy-
cle is a set of output variables o (setpoints) that are
written to the actuators.
The control objectives are optimized using the
CONTROLEUM-GA to guarantee a Pareto optimal
trade-off between the multiple control objectives
within computational tractable time (Ghoreishi et al.,
2015). The CONTROLEUM-GA is a multi-objective
genetic algorithm that incorporates domain specific
variables and operators to solve dynamic optimiza-
tion problems. The support for domain specific vari-
ables and operators, enable the algorithm to converge
fast enough to optimize a larger number of objectives
within each optimization cycle of DynaGrow.
4 OBJECTIVES
This section describes the most important objectives
optimized in the DynaGrow experiment described in
this article. Each control objective function can be
formulated either as a minimization cost function or
as a constraint. The objective function evaluates op-
tions proposed by the MOEA during the optimization
process. The option argument is passed over by the
MOEA and is a data-structure that contains a set of
input variables i and output variables o that connects
the control machine to the connection domains. In
the pseudo-code, variables starting with an upper case
letter represent information from the option provided
by the MOEA. Lower case variables represent result
variables used in the objective function.
SPARBAL(option)
1 lightPlanSum = CALCPARSUM(LightPlan,LampPAR)
2 balance =
5 ×ParDLI (ParHist + ParFuture + lightPlanSum)
3 return balance
The PAR Light Sum Balance objective
(SPARBAL) minimizes a Photosynthetically Ac-
tive Radiation (PAR) sum balance, see Line 2. PAR
designates the spectral wave band of solar radiation
from 400 to 700 nanometers that photosynthetic
organisms are able to utilize for photosynthesis. The
balance is calculated over a five days time-window
defined by current day, two days in the past and
two days in the future. The PAR Integral Today
and Past Two Days (ParHist) input is derived from
historical data stored by DynaGrow. Data for the
Expected Natural PAR Sum Remaining Day and
Future Two days (ParFuture) input is provided by
Conwx Intelligent Forecast Systems (Conwx, 2016).
The PAR Day Light Integral (ParDLI) input specifies
the average goal to be achieved over the five days
period and is provided by the grower. The total light-
plan PAR sum (lightPlanSum) variable is calculated
based on the Installed Lamp PAR (LampPAR) input
and the number of suggested light intervals in the
LightPlan output. The balance is then calculated
as the difference between the provided goal and the
total of ParHist, ParFuture and the lightPlanSum.
At the end of each day, the ParHist input will be
updated with data from the past days. Similarly, the
ParFuture input will be updated with data from next
day. That is, the balance is calculated based on a five
days sliding window.
SCHEAPLIGHT(option)
1 cost =
n
i=1
LightPlan.Switch
i
× ElForecasts.Price
i
×
(TotalLoad × LpTimeslot
i
)
2 return cost
The Prefer Cheap Light objective
(SCHEAPLIGHT) is specified as a cost func-
tion that minimizes the price of the Light-plan
(LightPlan) based on El. Spot and Prognosis Prices
(ElCompPrices). The electricity spot market price
forecast is provided by Nord Pool and the longer
three day prognosis is provided by Energi Danmark
(Nord Pool, 2016; Energi Danmark, 2016). The
Total Lamp Load (TotalLoad) is calculated as the
Installed Lamp Effect (InstLampEffect) multiplied
by the Greenhouse Size (GreenhouseSize). The
index i is the time-slot index of the LightPlan. For
each light time-slot i, the sub-cost is calculated as
the Total Lamp Load (TotalLoad) multiplied by the
light-plan time-slot interval T
i
and the electricity price
ElForecasts.Price
i
. The total cost of the Light-plan
(LightPlan) is then the sum of all the sub-costs for
each of the light intervals (LightPlan.Switch
i
). The
LightPlan.Switch
i
is zero for light switched off and
one for when the supplemental light is lit.
5 IMPLEMENTATION
The CONTROLEUM-GA function shows the pseudo-
code for the genetic algorithm implementation used
by the core of DynaGrow. Note that the line numbers
break and continue in places where sub-functions are
called from CONTROLEUM-GA ().
DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses
43
CONTROLEUM-GA(time,oldPop)
1 if oldPop.isNotEmpty
2 for each oldSolution oldPop
3 ADD-NONDOMSOLUTION(
COPY(time, solution))
4 for i = 0 to POPSIZE
5 ADD-NONDOMSOLUTION(D-INIT(time))
17 while isNotTerminated
18 for i = 0 to i POPSIZE
19 if RANDOM-DOUBLE() < MUTATIONRATE
20 child = S-MUTATE(RANDOM(pop))
27 else
28 child = S-CROSSOVER(RANDOM(pop),
RANDOM(pop))
34 ADD-NONDOMSOLUTION(child)
The CONTROLEUM-GA function has two argu-
ments: 1) a time-stamp time for when the algorithm
is executed, and 2) the population oldPop from previ-
ous executions. The time-stamp time is used for dy-
namic optimization problems that use the start time of
the optimization. The algorithm is separated into the
following phases: Initialization, Ranking, Mutation,
Crossover and Termination.
Initialization: A population consist of a number of
non-dominated Pareto optimal solutions. Each so-
lution is represented by a data-structure that has a
collection of objective results solution.objectives and
decision variables solution.variables. A solution can
have multiple different types of domain specific vari-
ables; e.g., temperature, CO
2
and light-plan. In this
work we focus on the light-plan variable. Line 1
checks if the previous population oldPop is empty.
The population oldPop is empty the first time the al-
gorithm is executed. If the population oldPop exists
from previous executions, it is copied into the new
non-dominated population pop (Line 3). A domain
specific initialization operator D-INIT is implemented
for each type of decision variable (Line 5). For exam-
ple, the supplemental light-plan is initialized by the
domain specific initialization operator that is an im-
plementation of the D-INIT function. For example the
different time resolutions of light-plans are encoded
in the light-plan variable.
CONTROLEUM-GA Line 17 test if the evolution
should terminate. Evolution is terminated after a
specified time limit, after a number of generations or
when the population is stable.
Ranking: The function ADD-
NONDOMSOLUTION sorts all solutions in the
population pop according to the Pareto domi-
nance relation (Line 8). That is, the objectives
are ranked given the proposed decision variables
solution.variables. The results of the evaluations are
assigned to the objective values solution.objectives
for each proposed solution. Only non-dominated
solutions are added to the population pop.
ADD-NONDOMSOLUTION(newSolutionA)
6 for each oldSolutionB pop
7 flag =
8 PARETO-COMPARE(newSolutionA, oldSolutionB)
9 if f lag
= =
ADOMINATESB
10 REMOVE(oldSolutionB,pop)
11 elseif f lag
= =
BDOMINATESA
12 return f alse
13 elseif DISTANCE(newSolutionA, oldSolutionB) < EPS
14 return f alse
15 ADD(newSolutionA, pop)
16 return true
The function PARETO-COMPARE compares
if a solution newSolutionA dominates a solution
oldSolutionB or visa verse. If solution newSolutionA
dominates solution oldSolutionB then solution
oldSolutionB is removed from the population pop
(Line 10). Contrary, if solution oldSolutionB domi-
nates solution newSolutionA then it is not added to the
population pop (Line 12). Two solutions are defined
as the same, if the Euclidean DISTANCE between
two solutions in the objective space, is less than the
level of significance defined by constant EPS. In case
newSolutionA is the same as oldSolutionB then it is
not added to the population pop (Line 14).
Mutation: For each generation, solutions are ran-
domly selected a number of times for mutation. The
number of mutations is determined by the constants
MUTATIONRATE and POPSIZE. For example, if
POPSIZE is 100 and MUTATIONRATE is 50 % then
50 randomly selected solutions are mutated. If a mu-
tation results in a non-dominated solution then it is
added to the population pop.
S-MUTATE(solution)
21 i = RANDOM-INT()
22 solution. variables[i] = D-MUTATE(solution.variables[i])
Mutation is applied at solution level and domain
variable level. At solution level a random decision
variable is selected for mutation in Line 21 using a
generic uniform mutation (UM) operator. Each de-
cision variable has its own domain specific mutation
operator D-MUTATE. The D-MUTATE operator is ap-
plied on the randomly selected variable in Line 22.
D-MUTATE-LIGHT shows the implementation of D-
MUTATE for a light plan variable. The selected light
plan variable is copied and a randomly selected index
in the plan is negated. That is, if the light state for the
selected index was ON, then after mutation it will be
set to OFF.
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
44
D-MUTATE-LIGHT(lightPlan)
23 l p = COPY(lightPlan, TIMEINTERVAL)
24 i = RANDOM-INT(l p. size)
25 l p[i] = ¬l p[i]
26 return l p
Implementations of D-MUTATION operators in-
corporate domain knowledge to ensure that the val-
ues of the decision variables are always viable. In
case of the light plan variable, the D-MUTATION-
LIGHT incorporate knowledge about time-resolution
and which index of the light plan that is viable for
change (Line 23 and 24). For example, if a light inter-
val only can change once for a given period of time, or
if a light state is always fixed, then it is implemented
in the implementation of the D-MUTATE function for
the given variable. Each domain mutation operator
defines a range for a specific type of decision variable
(temperature, energy, CO
2
, etc.). The intersection of
these ranges defines the viability space of the decision
variable.
Crossover: Solutions are randomly selected for
crossover for a number of iterations. The number
of crossover iterations is determined by the constant
POPSIZE. Crossover is applied at solution and deci-
sion variable level. The solution level crossover func-
tion S-CROSSOVER is called in CONTROLEUM-GA
Line 28.
S-CROSSOVER(solutionA,solutionB)
29 i = RANDOM-INT(solutionA. variables. size)
30 solutionA. variables[i] =
D-CROSSOVER(solutionA.variables[i],
solutionB. variables[i])
31 for j = i + 1 to j < solutionA.variables. size 1
32 solutionA. variables[ j] = solutionB.variables[ j]
33 return solutionA
Random variables from two solutions solutionA
and solutionB are selected for crossover at decision
variable level using a generic one-point crossover op-
erator (Line 29,30). At the other variables from the
selected index i + 1 till last index from solutionB is
copied to solutionA.
The domain specific D-CROSSOVER operator is
applied on the selected decision variables in Line 30.
The D-CROSSOVER function is the domain specific
crossover operator for two light plans lightPlanA
and lightPlanB. In the DynaGrow experiment, the
two light plans are crossed by a standard one-point
crossover operator but more complex outputs requires
more knowledge encoded into the output datastruc-
ture.
6 EXPERIMENT
In February 2015 a greenhouse experiment was exe-
cuted to evaluate the cost effectiveness and the quali-
ties of the DynaGrow software. The experiment was
executed as three treatments in three identical green-
house compartments. The first treatment (S SON-T)
was executed by a standard control system with SON-
T lamps and fixed day length of 18 hours. The sec-
ond treatment (DG SON-T) was equipped with SON-
T lamps but controlled by DynaGrow. The third treat-
ment (DG LED) was equipped with LED lamps and
was controlled by DynaGrow. For both the Dyna-
Grow compartments the control cycle was set to 5
minutes.
The PAR Day Light Integral provided by natural
light was on average approximately 12 mol m
2
d
1
and the DLI goal for all of the evaluated control strate-
gies was set to 8 mol m
2
d
1
.
The cost of the energy, used for supplemental
light, was calculated based on when the light was lit
and the price of electricity for that specific time. The
electricity prices was for the west part of the Danish
electricity grid (DK1). Each compartment is 76 m
2
and in the SON-T compartments there is 16 lamps in-
stalled. A SON-T lamp has an effect of 600 Watt.
That is, the installed effect for the SON-T compart-
ments is (16 × 600 W )/76 m
2
= 126 W /m
2
. In com-
parison, a LED lamp has an effect of 190 W and the
LED compartment has 38 LED lamps installed. The
total installed effect for the LED compartment is then
(38 × 190 W )/76 m
2
= 95 W /m
2
.
The granularity of the SON-T light-plans was
set to one hour due the physical properties of the
lamps. A SON-T lamp can not tolerate to be switched
ON/OFF to often due to heating. The granularity of
the LED light-plans was set to 15 minutes as LED
lamps tolerate to be lit in short intervals.
Roses, Kalanchoe and Chili batches were grown
in the three treatments over the season, to evaluate the
effect of the different light-plans. The cultivars were
harvested a maturation, meaning that plants from the
different treatments were sometimes harvested at dif-
ferent times.
7 RESULTS
Figure 2 illustrates the light-plans, effectuated for
each of the compartments, together with the electric-
ity prices provided from the Nord Pool power spot
market (Nord Pool, 2016). The light-plan for the S
SON-T compartment has fixed rates as expected from
the standard fixed rate light control strategy.
DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses
45
El [EUR/MWh]
Plan [ON/OFF]
Feb 05 Feb 12 Feb 19 Feb 26
0
20
40
60
80
(a) Standard SON-T
El [EUR/MWh]
Plan [ON/OFF]
Feb 05 Feb 12 Feb 19 Feb 26
0
20
40
60
80
(b) DynaGrow SON-T
El [EUR/MWh]
Plan [ON/OFF]
Feb 05 Feb 12 Feb 19 Feb 26
0
20
40
60
80
(c) DynaGrow LED
Figure 2: Comparison of light-plans optimized by different
strategies in February 2015.
Figure 2a shows that the fixed rate light-plan
clearly requires supplemental light at hours where the
electricity price is high.
In contrast, the DG SON-T light-plan is quite dif-
ferent to the S SON-T light plan. Figure 2b illustrates
the DG SON-T light-plan for the same experiment
period. The DG SON-T light-plan has the same
one hour granularity as the S SON-T light-plan but
expensive supplemental light hours are avoided. That
is, the CONTROLEUM-GA has clearly optimized the
SCHEAPLIGHT objective in order to generate
the DG SON-T light-plan.
The DG LED light-plan is similar to the DG SON-
T light-plan as both light-plans are optimized by same
MOEA with same objectives, see Figure 2c. The dif-
ferences between the DG SON-T and DG LED light-
plans are the granularity of the generated light-plans
and the energy consumed by the different type of
lamps. The finer granularity (15 minutes) of the DG
LED light-plans can be observed February 20-23 in
Figure 2c.
Table 1: Energy results of the experiment February 2015.
Control Energy
[KWh]
Energy
[%]
Cost
[e ]
Cost
[%]
S SON-T 4139.2 100 127.0 100
DG SON-T 2482.6 40 62.4 51
DG LED 1828.5 56 45.3 64
Table 1 provides a summary of the experimental
energy results for February 2015. The total energy
consumed by the S SON-T treatment (control) was
4139.2 KW h and was set as the reference. The DG
SON-T the DG LED treatments consumed 40 % and
56 % less energy compared to the reference, respec-
tively. Furthermore, the total cost of S SON-T light-
plan (reference) was 127 e. The DG SON-T and LED
treatments was 51 % and 64 % cheaper than the ref-
erence, respectively.
Table 2: Light hours and DLIs of the experiment February
2015.
Control Average
DLI
[mol
m
2
d
1
]
DLI
[%]
Suppl.
Light
hours
[h]
Suppl.
Light
[%]
S SON-T 8.5 100 432.3 100
DG SON-T 6.5 19 259.3 40
DG LED 6.5 19 253.3 41
Table 2 shows the experimental DLI results for the
three compartments in February 2015. For the refer-
ence S SON-T treatment, supplemental light was lit in
432.3 hours. In comparison, supplemental light was
lit for 259.3 and 253.3 for the DG SON-T and DG
LED treatments, respectively. That is, the DG SON-
T and DG LED treatments had respectively 40 % and
41 % less supplemental light hours than the reference.
The DLI goal for each treatment was 8 mol m
2
d
1
.
Additionally, the average DLI for the S SON-T treat-
ment was higher than the DG SON-T and DG LED
treatments. The two DynaGrow treatments obtained
the same average DLI of 6.5 mol m
2
d
1
which is
19 % lower than the reference and 1.5 mol m
2
d
1
lower than the DLI goal.
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
46
DLI [mol m
-2
d
-1
]
Feb 05 Feb 12 Feb 19 Feb 26
0
5
10
15
(a) S SON-T
DLI [mol m
-2
d
-1
]
Feb 05 Feb 12 Feb 19 Feb 26
0
5
10
15
(b) DG SON-T and “DG LED”
Figure 3: PAR light sum comparison from tree experimental
compartments optimized by different strategies in February
2015.
Figure 3 shows the accumulated DLIs for each of
the treatments. The DG SON-T and DG LED DLIs
are similar but the S SON-T DLIs are in average
higher which corresponds to the results in Table 2.
The properties of the cultivars was reported for
Roses, Kalanchoe and Chili by measuring the rela-
tive growth rate (RGR), relative dry weight (RDW)
and the number of flowers. In general the results
are reflecting that plant growth was related to the cli-
mate conditions of the treatments with species and
genotype-specific differences. All the plants grew
well in the three climates and reached maturation
within acceptable time. Figure 4 is pictures of Kalan-
choe (Simone) grown under the S SON-T (left), DG
SON-T (Middle) and DG LED (Right) treatments.
Further details about the results of growing the differ-
ent cultivars in irregular light conditions can be found
in work by Ottosen et al (Kjaer et al., 2012; Kjaer and
Ottosen, 2011; Kjaer et al., 2011). Last, is was con-
cluded that all the grown cultivars was in a sales-ready
quality.
Figure 4: Kalanchoe (Simone) as a result of the S SON-
T (left), DG SON-T (Middle) and DG LED (Right) treat-
ments.
8 DISCUSSION
The DG SON-T and LED treatments was 51 % and
64 % cheaper than the reference, respectively (Ta-
ble 1). In euro, that is a saving of 81.7 e per month
in the best case for a compartment of 76 m
2
. In Den-
mark, some industrial growers have greenhouse facil-
ities that is more than 65000 m
2
and the documented
energy saving can have a huge potential economical
impact. It’s important to emphasise that the price
calculation does not include rates for Public Service
Obligations (PSO), but is based purely on the spot
market prices and the theoretical installed effect in
each compartment.
Figure 5: Optimistic outdoor light forecast (bell curve) and
the actual outdoor light data for February 11-18.
The lower DG SON-T and DG LED DLIs was
not expected as similar effect (light intensity) should
have been installed in the S SON-T treatment. The
equal DG SON-T and DG LED DLIs indicate that
the optimization of PAR Light Sum Balance objec-
tive has been achieved but with a too low goal. A
reason for the lower DLI can be explained by the nat-
ural light forecast provided by the external service.
The cost calculation in the SPARBAL objective de-
DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses
47
pends on a precise outdoor light forecast, see SPAR-
BAL Section 3.
Figure 5 illustrates the time-series of the outdoor
light forecast and the actual outdoor light. The time-
series reveal that the outdoor light forecast has a ten-
dency to be too optimistic. An optimistic light fore-
cast will influence the sliding window balance in the
SPARBAL objective as the ParFuture will promise
more light than is the actual light. If the ParFuture
is optimistic over several days, like illustrated in the
time-series in Figure 5 (February 11-18), then the av-
erage of the ParDLI will never be achieved within the
sliding window. That is, the result will be a lower
average DLI as indicated by Table 2.
9 CONCLUSION
The increasing energy prices is a challenge for grow-
ers and need to be addressed by utilizing supplemen-
tal light when electricity prices are low and without
compromising the growth and quality of the crop. Op-
timization of such multiple conflicting objectives re-
quires advanced strategies that are currently not sup-
ported in existing greenhouse climate control sys-
tems.
The result of the winter experiment 2015 demon-
strates that DynaGrow utilizes supplemental light
at low electricity prices without compromising the
growth and quality of the crop compared to standard
fixed rate supplemental light control. It was possible
to produce a number of different cultivars where the
supplemental light (SON-T or LED), the temperature
and CO
2
was controlled by the DynaGrow software.
The energy savings are achieved in relation to a con-
trol treatment with a fixed day length, but only if the
DLI is comparable between the treatments.
In Denmark, DynaGrow will have a high impact
on cost in the beginning and end of the growing sea-
son, when there is a huge potential for optimizing the
supplemental light.
There is an unexplored potential to optimize the
utilization of supplemental light, temperature, CO
2
,
humidity and other climate variables simultaneously
by formulating multiple advanced control objectives
based on models already available from the extensive
horticultural literature.
The results clearly demonstrate, that DynaGrow
supports a dynamic climate control strategy by op-
timizing multiple control objectives that results in a
cost-effective control of the greenhouse climate.
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