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Greenhouse agriculture is a highly efficient method of food production that can greatly benefit from supplemental electric lighting. The needed electricity associated with greenhouse lighting amounts to about 30% of its operating costs. As the light level of LED lighting can be easily controlled, it offers the potential to reduce energy costs by precisely matching the amount of supplemental light provided to current weather conditions and a crop’s light needs. Three simulations of LED lighting for growing lettuce in the south-east of the US using historical solar radiation data for the area were conducted. Lighting costs can be potentially reduced by approximately 60%. Simulation of greenhouse energy use: an application of energy informatics. Available from: [accessed Aug 11 2018].
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R E S E A R C H Open Access
Simulation of greenhouse energy use: an
application of energy informatics
Richard T. Watson
, Marie-Claude Boudreau
and Marc W. van Iersel
* Correspondence: rwatson@terry.
Department of Management
Information Systems, University of
Georgia, Athens, GA 30602-1575,
Full list of author information is
available at the end of the article
Greenhouse agriculture is a highly efficient method of food production that can greatly
benefit from supplemental electric lighting. The needed electricity associated with
greenhouse lighting amounts to about 30% of its operating costs. As the light level
of LED lighting can be easily controlled, it offers the potential to reduce energy costs
by precisely matching the amount of supplemental light provided to current weather
conditions and a crops light needs. Three simulations of LED lighting for growing
lettuce in the south-east of the US using historical solar radiation data for the area
were conducted. Lighting costs can be potentially reduced by approximately 60%.
Keywords: Controlled-environment agriculture, Horticulture, Supplemental lighting,
Simulation, Energy efficiency
In January 2007, the first two authors of this article started a project to link informa-
tion systems (IS) scholarship to ecological sustainability. They were concerned that IS
scholars were paying minimal attention to global warming and that they did not
consider it a problem worthy of their attention. While leading scientists (Holdren
2009) were calling for their peers to spend 10% of their time solving the existential
threat resulting from the burning of fossil fuels, IS scholars were generally ignoring the
issue. Consequently, this important problem has remained a marginal IS research topic
(Gholami et al. 2016;Malhotraetal.2013). Since 2010, we have advocated for academic
leaders (journal editors in particular) to embrace environmental sustainability as a core
principle in the research they publish (Watson et al. 2010); the emergence of this journal
constitutes a critical step in engaging more scholars in solving the problem of our time.
The project to engage IS scholars in sustainability scholarship resulted in a proposal to
create Energy Informatics as a new field of IS research (Watson et al. 2010) (Watson and
Boudreau 2011). Leveraging the Energy Informatics framework, we produced a case study
on how one of the worlds major logistic companies was applying the principles to reduce
energy consumption and advance sustainability (Watson and Boudreau 2011). Energy
efficiency, the invisible fuel, reduces the demand for fossil fuels and thus contributes to
carbon emission reduction.
The Energy Informatics framework identifies the central elements of an energy supply
and demand system, including the key stakeholders, the three major eco-goals, and the
major social forces influencing an energy production/consumption system. The framework
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directs attention to flow networks, sensor networks, and sensitized objects as these are the
source and destination of the necessary data inputs and outputs to advance energy efficiency
through the use of information systems. The framework arose from reviewing many energy
production/consumption systems, such as traffic congestion management and building
energy management.
The principal observation is that economies consist of flow networks (e.g., cars, water,
people, packages, containers) that consume energy. The management of the energy con-
sumed by such flows requires sensor networks to provide digital data streams reporting on
the current status of the flow network so that high quality decisions can be made about the
status of sensitized objects that can control the flow network. Actions might be as simple as
turning off a valve supplying hot air to a work space. They may also be remotely controlled,
such as in the management of hundreds of traffic lights to reduce city traffic congestion.
The original Energy Informatics framework has proved robust because it identifies
the main components of an energy flow network and the means of controlling them. It
highlights the central role of an information system, linking the interdependencies
between supply and demand and the major components (i.e., flow networks, sensor
networks, and sensitized objects).
The Energy Informatics framework has been applied to multiple domains, such as
road pricing, farming, logistics, bicycle sharing, and others. Recently, we have applied
this framework to another major existential problem: food security.
Food security
Food security is a critical problem that demands the attention of IS scholars, as well as
horticulturists, agricultural scientists, and others concerned with food production. Over
the next 30 years, the worlds population is predicted to grow by up to 34% and
urbanization will increase by around 20%. To feed this wealthier and larger population,
food production must increase by an estimated 70% (Food and Agriculture Organization
of the United Nations 2009). Achieving food security by minimizing variations in supply
and adjusting to the growth in food demand presents many challenges that will likely
require a major adjustment in current agricultural practices (Magnin 2016). Food security
could be enhanced by reducing personal meat consumption and shifting to a predominantly
vegetarian diet, but this would require significant behavioral changes to well-established
customs and practices. Our focus in this research is on modifying agricultural practices
rather than promoting behavioral change.
A solution
Controlled-environment agriculture (CEA), such as indoor farms and greenhouses, is a
key path to increasing food production. CEA can produce up to 20 times as much
high-end, pesticide-free produce as a similar-size plot of soil but requires electric lighting
to do so (Science Illustrated 2011). Such facilities will be necessary to meet the future
demand for quality fruits and vegetables, particularly in China with its rapidly growing
and large middle class (Science Illustrated 2011).
In 2016, the CEA market was dominated by Europe, Middle East, and Africa (EMEA),
following by the Americas, and Asia Pacific, with percentage shares and values of 62%,
23%, and 15% of a market valued at USD 20.25 billion. The Netherlands, Spain, and Italy
Watson et al. Energy Informatics (2018) 1:1 Page 2 of 14
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dominate EMEA production; in the Americas, horticulture is mainly in North America;
and China has been growing rapidly with more than one million greenhouses.
The smart greenhouse is emerging as a solution, offering various collections of integrated
technologies in a greenhouse, to improve the productivity of CEA. The smart greenhouse
vision is based on sensors, actuators, and monitoring and control system that can optimize
plant growth and quality and automate the growing process. This market was valued at
approximately USD 680.3 million in 2016. With a short-term compound annual growth rate
of around 14.12%, it is expected to reach approximately USD 1.3 billion by 2022.
At this
stage, the smart greenhouse market is about 3% of the world market.
The greenhouse industrys current practices can require considerable energy to power
electric lighting to maintain plant growth on overcast days, so as to meet production
schedules. Electricity for lighting can make up to 30% of the costs for greenhouses (van
Iersel MW, Gianino D: An adaptive control approach for LED lights can reduce the en-
ergy costs of supplemental lighting in greenhouses, Submitted). Currently, many commer-
cial greenhouses use high intensity discharge (HID) lights,
which have high output, cover
wide areas, and emit high heat. These lights have timers or automated control systems
that use sensors to turn on all lights at full power when natural light levels drop below a
predetermined intensity, even when only a fraction of the light might be required to reach
acrops needs for growth. In the extreme, some growers might leave the lights on full
power for a substantial portion of the day (as most plants need between 12 to 18 h of light
per day, depending on the species). This approach to growing crops is inefficient, resulting
in energy waste, higher operational costs, and often unnecessary carbon emissions.
Prior research to reduce CAE electricity costs by making greenhouses smarter has pro-
duced a variety of brandedsolutions. Intelligrow (Aaslyng et al. 2003; Markvart et al. 2007),
DynaLight (Kjaer et al. 2011,2012)(Clausenetal.2015), and DynaGrow (Sørensen et al.
2016) are successive developments of software for optimizing greenhouse production and
minimizing energy costs. DynaGrow, the most recent and advanced of cumulative research
in Danish greenhouses, applies multi-criteria methods to control a greenhouses climate.
The solution has been physically implemented through software and associated sensors and
actuators within a greenhouse and produces savings of 64% with LED lighting (Sørensen
et al. 2016), the concern of this article.
The study reported in this article differs in several ways. First it is a simulation rather
than a physical implementation. A key purpose is to identify the savings generated by
adaptive LED lighting and what form of software as a service (SaaS) might be economic-
ally viable. Simulation enables inexpensive consideration of alternatives. Second, there is a
single objective of minimizing electricity cost subject to ensuring that a crop receives
sufficient light each day to meet growth needs for on time contract delivery. This research,
like the Danish stream, is based on current knowledge of plant physiology.
Plant physiology
Plants grow by converting photons (sunshine or supplemental lighting), water, and CO2 to
and oxygen. The environmental conditions and physiology of each plant determine
the rate of photosynthesis. For the purposes of this research, we can think of a plant using
photons to transport electrons. In the light reactions of photosynthesis, photons are
absorbed by photosynthetic pigments, and the energy is used to transport electrons. This
Watson et al. Energy Informatics (2018) 1:1 Page 3 of 14
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electron transport then results in the production of chemicals required for the synthesis of
sugars. The electron transport rate(ETR)isadirectmeasureofthelightreactionsofphoto-
synthesis in response to photosynthetic photon flux (PPF) (Fig. 1). ETR is the driving force
for photosynthesis and ultimately crop growth. Both ETR and PPF are measured in micro-
per square meter per second (μmol m
of electrons and photons, respectively).
The efficiency of the conversion of the energy of photons into electron transport varies by
plant species, but in general we can represent this relationship as a saturation curve of the
form ETR = a.(1-e
), where a and b vary by species. The fitted saturation curve for
lettuce illustrates the natureofthisrelationship(Fig.1). As the saturation curve shows,
conversion of photons into electron transport is most efficient at low levels of PPF, which is
an important consideration when electric lighting is used. Essentially, low levels of lighting
for long periods are more energy efficient than high levels for short periods. Furthermore,
supplemental light provided when sunlight levels are low will be used more efficiently than
supplemental light provided when sunlight levels are high.
A plant needs to transport a threshold number of moles of electrons per day to optimize
its growth. This is particularly important for commercial crops, which are usually grown
under contract with a scheduled harvesting date and defined delivery volume. Based on
experiments at the University of Georgia, it has been determined that lettuce, for example,
needs to transport approximately 3 mol m
of electrons. On a typical day, this rate
of electron transport requires approximately 18 mol of photons m
. This is close
to the recommended daily light level for year-round production of high-quality lettuce of
17 mol m
(Both et al. 1994).
We can convert daily electron transport into a required level of photons per second,
as follows:
D = ETR in moles m
to maximize growth
Fig. 1 Electron transport rate (ETR) of lettuce as a function of the photosynthetic photon flux (PPF). Source:
Unpublished University of Georgia research
Watson et al. Energy Informatics (2018) 1:1 Page 4 of 14
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Tetr = threshold ETR in μmoles m
to achieve D
Tppf = threshold PPF in μmoles m
to achieve D
t = seconds of operation per day of the greenhouse
Tetr ¼D106
Tppf ¼1
blog a
The innovation
Supplementary lighting typically lacks intelligence and can be left on continually during
the hours of operation, if not for an entire day. This means that energy can be wasted
by providing more photons than a crop needs to optimize its daily growth.
A recent development in the context of commercial greenhouses is the replacement of
HID lights with LED technology. LED technology has many advantages, such as smaller
size (and thus easier to mount) than its HID predecessor and it is more energy efficient.
More importantly, LEDs can be designed to produce light in the part of the spectrum that
drives photosynthesis (400700 nm), without producing infra-red radiation (which is not
used for photosynthesis). LEDs are also fully dimmable, unlike HID lights, thus allowing
growers to precisely control how much supplemental light is provided. For a LED light,
the relationship between energy consumption and photons generated is essentially linear
(National Electrical Manufacturers Association 2015), and we so assume in this research.
Leveraging this kind of lighting, a local company, Phytosynthetix,
collaborated with the
University of Georgias Horticultural Physiology Laboratory to develop an innovation that
could be transformative to the industry: adaptive LED lighting, which uses a built-in light
intensity sensor to determine how much supplemental lighting to provide when natural
lighting falls below a crops threshold needs (van Iersel MW, Gianino D: An adaptive
control approach for LED lights can reduce the energy costs of supplemental lighting in
greenhouses, Submitted). Adaptive lighting provides just enough light to assure optimal
crop growth and reduces electricity use compared to conventional control algorithms.
The amount of supplemental light to provide can be based on the crops physiological
ability to use that light efficiently (see Fig. 1). This is an important innovation in green-
house production as none of the available similar solutions take into consideration both
natural light levels and crop-specific light use efficiency.
Enhancing adaptive lighting by applying Energy Informatics
The value of adaptive LED lighting can be increased by the application of Energy Inform-
atics principles (Watson et al. 2010) to manage the lighting system to minimize electricity
costs while meeting schedule constraints. The CEA version of the Energy Informatics
framework (Fig. 2) incorporates all elements of an energy supply and demand system for
greenhouses. We have taken the basic Energy Informatics framework, the yellow section
of Fig. 2, and added details of the digital data streams (energy prices and solar radiation
forecasts) and databases (plant details and production schedule) necessary to control the
sensitized object (LED lighting). These four additional components illustrate how the core
Energy Informatics information system can be extended for CEA.
Watson et al. Energy Informatics (2018) 1:1 Page 5 of 14
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To understand the advantage of adaptive LED lighting, while leveraging the Energy
Informatics framework adapted to the context of CEA, we ran three simulations with
increasing levels of sophistication. The first simulation leverages LED adaptive lighting.
The second incorporates a daily decision, where the adaptive lights are turned off for the
day when the expected daily solar radiation exceeds the ETR that optimizes growth. As to
the third simulation, it leverages within daydecision making, where the adaptive lights
are turned off when the target solar radiation for the day has been achieved. Note that
whereas Fig. 3suggests an energy price forecast, we assumed a fixed energy cost for the
time being. Moreover, rather than solar radiation forecasts, we used historical solar
radiation data for a specific location and period. Last, for sake of simplicity in this first set
of simulations, we did not consider the cost of production schedule delays.
The simulations were based on a 5-year period of growing lettuce in Athens, Georgia,
with the following parameters:
D = 3 mol m
t= 20 h day
a = 124.3 μmol m
b = .002737 m
Electricity cost = USD .12/kWh (the rate charged by the local utility)
LED light = 600 W
LED light range is 0200 μmoles m
Fig. 2 Energy Informatics framework
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We assumed 70% transmittance of the received solar radiation into the greenhouse.
We compared the results of all three simulations to a baseline scenario, which involved
non-adaptive LED lighting. This baseline scenario, along with the three simulations, are
further detailed next. The greenhouse lighting layout assumes the use of 1,200 600 W
LED lights per hectare, capable of providing a PPF of 200 μmoles m
with all
lights on at full power.
Baseline scenario: non-adaptive LED lighting
Based on a recent survey of growers,
we assume a typical greenhouse uses supplemental
lighting for an average of 3.25 h per day (9 h in winter, 2 h in fall and spring, and 0 in
summer). The annual cost for this level of LED lighting is .12*3.25*365.25*600/1000 =
USD 85.47 per light per year. A one hectare greenhouse needs about 1200 lights, so the
cost would be USD 102,562 per year.
Simulation#1: Adaptive lighting
This scenario assumes that the lights can be dimmed to any PPF between 0 and 200
μmoles m
, and the relationship between energy use and PPF is linear between 0
and 600 W. At its peak, adaptive lighting consumes the same energy as non-adaptive
lighting. We use these parameters for the simulation.
Using solar radiation data collected in 10-min intervals in Athens, Georgia in 2010
2014, we simulate the use of adaptive lighting by computing Tppf for lettuce and setting
the lights to maintain the mix of natural and supplemental lighting at this level. As the
threshold for Tppf in this case is 149 μmoles m
, the adaptive lights were set to
maintain this level. When there is sufficient natural light, the adaptive lights will consume
0 W and when there is complete darkness, they will consume 149/200*600 = 447 W.
Fig. 3 Average cost per light per day (USD) of adaptive lighting
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The cost of operating the adaptive lighting in 20102014 to grow lettuce for 20 h per
day is estimated to be USD 194.78 per light per year or for a one hectare greenhouse
USD 233,738 per year. As expected, the lights are costlier to operate in winter than in
summer (Fig. 3).
Simulation#2: daily decision making
We simulated a model that would inform the grower whether to turn the adaptive lights
off for the day when the forecast solar radiation for the day exceeds the total required to
achieve an ETR of D moles m
. Such an approach requires minimal investment
and under a Software as a Service (SaaS) business model, the grower could be sent a text
message on the recommended status of the adaptive lighting early each day.
The cost of operating the adaptive lighting under daily decision making in 20102014
to grow lettuce with 20 h of light per day is estimated to be USD 86.92 per light per year
or for a one hectare greenhouse USD 104,302 per year, slightly above the baseline
scenario. As to be expected, on many days there is no need to turn on the lights (Fig. 4).
Simulation#3: within day decision making
A major shortcoming of a daily decision making model is that the forecasted radiation for
a day could be just below the threshold, but the lights are turned on for the entire day.
Ideally, once the target for the day has been achieved, the lights should be turned off for
the remainder of the day. We simulated such a model (Fig. 5), which shows that on some
days the lights come on, but only for a short period with a corresponding lower cost.
The cost of operating the adaptive lighting under this approach in 20102014 to
grow lettuce for 20 h per day is estimated to be USD 32.28 per light per year or for a
one hectare greenhouse USD 38,732 per year, about one third of the baseline scenario.
Fig. 4 Average cost per light per day (USD) of adaptive lighting with daily decision making
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Under a Software as a Service (SaaS) business model, the within day approach
requires that the grower invests in a lighting control system that can be controlled
remotely. The controller would receive periodically a message to set the status (on or
off) of the lights in the greenhouse. Thus, some of the energy savings will be lost to the
operation and maintenance of a lighting control system, but we expect these to be
minor compared to the energy use of the lights themselves.
The simulations were written in R, and the code is in four modules (see Additional file 1).
The main module (simulation.R) loads modules to read the parameters for a simulation
(parameters.R) and prepares data for the simulation (prepare.R). Another module
(report.R) reports the results for each of the simulations discussed previously.
The simulations take advantage of Rs vector-oriented operations for operating on
data frames, and a typical run with 5 years of data takes a few seconds. In comparison,
an earlier loop-based version of the model took 10 or so minutes to run.
The simulations require two binary input files, coded in Rs feather format,
are described in Appendix.
Return on investment
The findings of the analysis show that only within day decision making will result in a
reduction in total lighting cost compared to current manual practices (Table 1). Partial
solutions, while they might deliver light precisely to ensure optimum plant growth, are
likely to be subject to grower resistance because there is no cost advantage. Growers
can save around 60% of the current cost of lighting by retrofitting their current LED
lighting systems. Note that in the within day decision simulation, the lights are on half
the time of the current manual or timer practice, but the electricity saving is higher
Fig. 5 Average cost per light per day (USD) of adaptive lighting under within-day decision making
Watson et al. Energy Informatics (2018) 1:1 Page 9 of 14
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because the adaptive lighting generally does not operate the lights at full power. The
energy savings are similar in magnitude to those reported for DynaGrow (Sørensen
et al. 2016), however the two sites are at different latitudes, with Denmark in the mid
50s° and Athens, Georgia close to 34°, thus resulting in different light environments.
To compute the ROI, we conservatively estimate that the cost of equipping a green-
house with a single solar radiation sensor, control software, and connecting existing LED
lighting to the control software is USD 100 per light with a lifetime of 10 years, or USD
120,000 for a one-hectare greenhouse. The computed ROI is 68% for within day decision
making. If we triple the cost estimate to USD 300 per light, then the ROI is 14%. In effect,
anything below USD 300 per light for retrofitting a greenhouse is financially sound and
likely to provide additional benefits from exact contract fulfillment.
Although the prior simulations are based on a fixed energy cost of USD 0.12 per kWh,
we wrote the code so that it can also process a file of historical real-time electricity prices.
Such pricing is typically available only to large consumers (e.g., a factory or a university).
In this case, the within day simulation determines which time periods during the day are
the least expensive for operating the lights in order to meet the daily ETR goal.
Solar radiation and electricity prices are dependent on local conditions. In this situation,
based on 5 years of solar radiation data measured at the University of Georgia, the average
solar radiation is 5.22 kWh m
. The University is in the sunny south-east of the
US, and thus our results show the advantage of this additional solar energy. Also, the
region has some of the cheaper electricity in the US. The US average is USD .13 kWh,
with a range of USD .10 to .29 kWh. Georgia, at USD .125, is towards the lower end of
this range.
If the simulations had been based on solar radiation in the north-east of the
US, for example, savings would be different because of less solar radiation, more required
supplemental lighting, and higher electricity prices. In essence, our results should be inter-
preted as being appropriate for a relatively sunny region with relatively cheap electricity.
Our simulation framework can easily be used to simulate different locations.
The main purpose of these simulations is to show the potential of using solar radiation
and electricity cost forecasts to reduce the cost of operating a greenhouse. However,
rather than forecasts, we have simulated the perfect information situation where electri-
city prices and solar radiation can be precisely predicted. As most greenhouses are too
small to presently avail themselves of real-time pricing, treating the price of electricity as
fixed is appropriate. Thus, in moving beyond perfect information, the focus needs to be
on solar radiation forecasting.
It can be argued that simulations represent a simplified reality. However, such simplicity
means that they can be readily explained to a grower, which in turn is likely to increase
the likelihood of adoption. The simple decision rules are very effective in reducing cost,
Table 1 Simulation summary
Model Cost per LED light per year (USD) Average hours of LED light per day
Current manual practice 85.47 3.25
Adaptive 194.78 11.2
Adaptive with daily decision 86.92 5.09
Adaptive within day decision 32.28 1.69
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and additional complexity is unlikely to be required or produce significant additional
savings. Furthermore, the second and third simulations can be easily implemented from a
central source using a SaaS model, which lowers the cost for adoption and maintenance.
Future research
As mentioned earlier, by using historical data, we assume perfect information. Thus, the
next step is to simulate the effects of solar radiation forecasts with varying degrees of
accuracy to determine the sensitivity of the findings to forecast precision. We speculate
that sensitivity will be low for two reasons: (1) the goal is to determine whether or not to
turn on adaptive lighting for a set period, such as 10 min; and (2) a multi-period forecast
for the remainder of day can be continually adjusted throughout the day.
The essential problem, whether to turn the lights on or off for a set period would
seem to be a simpler problem than trying to forecast the level of radiation for the same
period. Intelligent adaptive LED lighting provides the precision to ensure plants receive
the necessary photons to meet growth goals, provided the LED light is on. We plan to
build two forecasting models, one for the binary decision of LED light on or off and
the second to forecast radiation levels. Based on prior research, it would appear that a
neural network (Wollsen and Jørgensen 2015) with allowance for autocorrelation is
appropriate for radiation level forecasting, whereas logistic regression might be suitable
for the binary decision.
The goal is to deliver sufficient photons each day for a plant to reach its daily growth
needs. Throughout a day, the forecast system must continually adapt to meet this target.
As the forecast period becomes shorter, then adaption can be faster. Thus, one of the
issues is to simulate the precision of the forecasting system to the length of the decision
period (e.g., 5 versus 20 min). As we expect the computing cost of each forecast to be
quite small and the communication cost of the on/off decision to each light to also be
small, the selected time period might make little financial difference for an individual
grower given the gains achieved by efficient management of LED usage. Nevertheless,
these assumptions need to be validated.
Since natural light conditions vary greatly among locations, it will also be important
to run simulations for a range of regions, especially those where greenhouses most
commonly use supplemental light, like Canada, the Northern US, and Northwestern
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. 2011,2012;
Mærsk-Møller and Jørgensen 2011; Markvart et al. 2007; Rytter et al. 2012;Sørensenetal.
2011;Sørensenetal.2016), and apply photosynthesis theories that take account of light
wavelength and the capability of controlling the wavelength of LED lighting.
There is much to learn about LED lighting because it is a relatively recent introduction
to the greenhouse environment. It enables control over spectral power distribution,
optical intensity distribution, form factor, and active color tuning. It can be used, as in this
study, to tailor light to a specific crop to improve productivity by controlling precisely the
photons delivered to ensure threshold needs for growth are met. Other aspects of plant
growth such as height, bushiness, and color or nutritional content are potential areas for
research (Pattison 2017). This research simulates LED usage approaches to minimize
electricity cost by applying prior research on threshold curves. It assumes that if a plant
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receives sufficient photons, or the right amount of light, to meet daily growth needs, the
quality of the crop will be of commercial standard. Assessment of this assumption is a
topic for future research, and the power of simulation is to help identify opportunities for
fruitful research.
This initial study of the application of Energy Informatics to CEA illustrates the poten-
tial to considerably reduce the energy required to operate a greenhouse. Our plans are
to extend the research to cover other commercial crops (e.g., red leaf lettuce, spinach,
basil, tomatoes, and cucumbers) as well as ornamental species, including high-light
crops (roses, petunias, and marigolds) and low light crops (impatiens, begonia, and
hellebore). Moreover, we plan to consider four diverse regions of the continental US,
because solar radiation varies greatly across this region (i.e., from an average of 3.0 to
over 6.5 kWh m
This research shows the value of the Energy Informatics framework for addressing
practical problems. It helps scientists communicate with growers by focusing on the three
elements that are necessary for an information system to manage an energy flow. In the
case of CEA, engaging growers will be critical to adoption of information systems
augmented adaptive lighting. It also demonstrates the potential for using simulation to
explore the effects of energy management options.
The Lawrence Livermore National Laboratory publishes annually an infographic on
energy use in the U.S. The 2016 version estimates that about two thirds of the energy
generated is wasted.
The application of Energy Informatics thinking to CEA illustrates
that in some areas of the economy, significant energy efficiency gains can be achieved
with simple solutions. We expect that CEA is not an exception and the launching of
Energy Informatics can potentially alert more scholars and practitioners to the synergis-
tic gains of integrating engineering advances, such as adaptive lighting, and information
systems to achieve higher levels of energy efficiency. Creating a sustainable society is
dependent on engineering and IS scholars combining minds and knowledge.
Which can be further subdivided into two types, metal halide and high-pressure
Compounds of carbon, hydrogen, and oxygen. Sucrose is C
1 mol = 6.022 × 10
(Avogadros constant) photons or electrons.
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Data availability
A binary file containing a timestamp and electricity price in cents per kWh. Measure-
ments are hourly.
A binary file containing a timestamp and incident solar radiation in Watts m
Measurements are for every 10 min, and derived from a file of measurements made
every 2 min.
These files must be stored in an R project working directory along with the R modules.
The location of this working directory must be specified in the second line of parameter.R
Additional file
Additional file 1: R code. (TXT 6 kb)
Only the authors listed on the manuscript contributed towards the article.
This research was supported by a University of Georgia Presidential seed grant.
Drs. M-CB and RTW were responsible for the Energy Informatics simulation. Dr. MWvI provided the data on the
saturation model for lettuce and details of plant physiology relevant for the simulation. All authors read and
approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author details
Department of Management Information Systems, University of Georgia, Athens, GA 30602-1575, USA.
of Horticulture, University of Georgia, Athens, GA 30602-7273, USA.
Received: 31 January 2018 Accepted: 11 June 2018
Aaslyng JM, Lund JB, Ehler N, Rosenqvist E (2003) IntelliGrow: a greenhouse component-based climate control system.
Environ Model Softw 18(7):657666
Both AJ, Albright LD, Langhans RW, Reiser RA, Vinzant BG (1994) Hydroponic lettuce production influenced by
integrated supplemental light levels in a controlled environment agriculture facility: experimental results. Paper
presented at the III International Symposium on Artificial Lighting in Horticulture. p. 418
Clausen A, Maersk-Moeller HM, Soerensen JC, Joergensen BN, Kjaer KH, Ottosen CO (2015) Integrating commercial
greenhouses in the smart grid with demand response based control of supplemental lighting. Paper presented at
the International Conference Industrial Technology Management Science (ITMS 2015)
Food and Agriculture Organization of the United Nations (2009) Global Agriculture Towards 2050. Retrieved 19 Aug
2016, from Food and Agriculture Organization of the United Nations:
docs/Issues_papers/HLEF2050_Global_Agriculture.pdf. Accessed 18 June 2018
Gholami R, Watson RT, Molla A, Hasan H, Bjørn-Andersen N (2016) Information systems solutions for environmental
sustainability: how can we do more? J Assoc Inf Syst 17(8):521
Holdren JP (2009) Energy for change: introduction to the special issue on Energy & Climate. Innov Technol
Gov Glob 4(4):312
Kjaer KH, Ottosen CO, Jørgensen BN (2011) Cost-efficient light control for production of two campanula species. Sci
Hortic 129(4):825831
Watson et al. Energy Informatics (2018) 1:1 Page 13 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Kjaer KH, Ottosen CO, Jørgensen BN (2012) Timing growth and development of Campanula by daily light integral and
supplemental light level in a cost-efficient light control system. Sci Hortic 143:189196
Mærsk-Møller HM, Jørgensen BN (2011) A software product line for energy-efficient control of supplementary lighting
in greenhouses. Paper presented at the The International Conference on Green Computing
Magnin C (2016) How Big Data Will Revolutionize the Global Food Chain. Retrieved 19 Aug 2016, from McKinsey &
revolutionize-the-global-food-chain?cid=digistrat-eml-alt-mip-mck-oth-1608. Accessed 18 June 2018
Malhotra A, Melville NP, Watson RT (2013) Spurring impactful research on information systems for environmental
sustainability. MIS Q 37(4):12651274
Markvart J, Kalita S, Nørregaard Jørgensen B, Mazanti Aaslyng J, Ottosen CO (2007) IntelliGrow 2.0A greenhouse
component-based climate control system. Paper presented at the International Symposium on High Technology
for Greenhouse System Management: Greensys 2007 801
National Electrical Manufacturers Association (2015) Energy Savings with Fluorescent and LED Dimming.). LSD 732015. Accessed 18
June 2018
Pattison (2017) Foreword. In: Gupta SD (ed) Light emitting diodes for agriculture: smart lighting. Springer, Singapore
Rytter M, Sørensen JC, Jørgensen BN, Körner O (2012) Advanced model-based greenhouse climate control using multi-
objective optimization. Paper presented at the IV International Symposium on Models for Plant Growth,
Environmental Control and Farm Management in Protected Cultivation- 957
Science Illustrated (2011) CEA. Science Illustrated, Feb.
Accessed 18 June 2018
Sørensen JC, Jørgensen BN, Klein M, Demazeau Y (2011) An agent-based extensible climate control system for
sustainable greenhouse production. Paper presented at the International Conference on Principles and
Practice of Multi-Agent Systems
Sørensen JC, Kjaer KC, Ottosen CO, Jørgensen BN (2016) DynaGrow-Multi-Objective Optimization for Energy Cost-
efficient Control of Supplemental Light in Greenhouses. Paper presented at the IJCCI (ECTA)
Watson RT, Boudreau MC (2011) Energy informatics. Green ePress, Athens
Watson RT, Boudreau MC, Chen AJ (2010) Information systems and environmentally sustainable development: energy
informatics and new directions for the IS community. MIS Q 34(1):2338
Wollsen MG, Jørgensen BN (2015) Improved local weather forecasts using artificial neural networks. Paper presented at
the Distributed Computing and Artificial Intelligence, 12th International Conference
Watson et al. Energy Informatics (2018) 1:1 Page 14 of 14
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... Given these projections [1], there is a demand for fast and high-yield agriculture while occupying a relatively small area. This can be achieved by the use of greenhouses, in which environmental and climatic conditions can be adjusted according to the plants to be grown while the structure can provide protection from external factors. ...
... To help decide on the parameters that need to be taken into account so that crops get comfortable levels of temperature inside the greenhouse, energy simulation software is often used by designers. This type of software is usually capable of doing an entire year's analysis of the indoor environment and energy use, based on the geometry, weather data, materials, loads schedules and so on [1]. Thus, it is possible to reduce energy and material costs while validating the internal environment with accuracy across the four seasons for a certain crop. ...
... Having the heating and cooling rates, the total annual energy required (EHtg,Clg) is obtained by the sum of every heating/cooling rate required (PHtg,Clg) for every timestep of the yearly simulation. This relation is expressed in Equation (1). ...
Full-text available
This paper investigates the design and implementation of a small greenhouse, based on an estimation of the required annual electrical loads, using robust energy modelling free software, namely OpenStudio. The greenhouse optimum materials, shape and orientation were estimated from this software, using weather file data and established environmental set points. Real-world electrical load estimations for the temperature, irrigation and lighting subsystems were consequently made, resulting in a good estimation of the required solar panel and battery combination. Sensors and actuators to physically establish the environmental set points were described, controlling with a microcontroller, while minimizing power losses. To maximize power throughput to the battery, a maximum power point tracking algorithm was described and modelled in Simulink, specifically for this system, using the microcontroller to implement a Perturb and Observe algorithm.
... Simulations of the light requirements for crop production and the cost of providing that light suggest that few crops have economic feasibility when electric lighting is the only light source [1]. The cost of supplemental or sole-source lighting in CEA can account for 10 to 50% of total operating costs [2][3][4] and, thus, has a great impact on profitability. To overcome the high costs associated with lighting and to maximize profits, researchers have studied different lighting strategies and developed improved cultivars for CEA [5,6]. ...
... The light responses curves of Φ PSII showed decreasing Φ PSII with increasing PPFD (Figure S6a,c) and were fitted using Equation (4). The Φ PSII light response curves of all cultivars resulted in an R 2 > 0.4 and p < 0.001. ...
Full-text available
Fast growth and rapid turnover is an important crop trait in controlled environment agriculture (CEA) due to its high cost. An ideal screening approach for fast-growing cultivars should detect desirable phenotypes non-invasively at an early growth stage, based on morphological and/or physiological traits. Hence, we established a rapid screening protocol based on a simple chlorophyll fluorescence imaging (CFI) technique to quantify the projected canopy size (PCS) of plants, combined with electron transport rate (ETR) measurements using a chlorophyll fluorometer. Eleven lettuce cultivars (Lactuca sativa), selected based on morphological differences, were grown in a greenhouse and imaged twice a week. Shoot dry weight (DW) of green cultivars at harvest 51 days after germination (DAG) was correlated with PCS at 13 DAG (R 2 = 0.74), when the first true leaves had just appeared and the PCS was <8.5 cm 2. However, early PCS of high anthocyanin (red) cultivars was not predictive of DW. Because light absorption by anthocyanins reduces the amount of photons available for photosynthesis, anthocyanins lower light use efficiency (LUE; DW/total incident light on canopy over the cropping cycle) and reduce growth. Additionally, the total incident light on the canopy throughout the cropping cycle explained 90% and 55% of variability in DW within green and red cultivars, respectively. Estimated leaf level ETR at a photosynthetic photon flux density (PPFD) of 200 or 1000 µmol m −2 s −1 were not correlated with DW in either green or red cultivars. In conclusion, early PCS quantification is a useful tool for the selection of fast-growing green lettuce phenotypes. However, this approach may not work in cultivars with high anthocyanin content because anthocyanins direct excitation energy away from photosynthesis and growth, weakening the correlation between incident light and growth.
... First, information systems can be used to automate several decisions to favour eco-efficient outcomes. For example, autonomous cyber-physical systems controlling greenhouse lighting can reduce energy costs for greenhouse agriculture by about 30 percent (Watson et al., 2018). ...
We conducted a field experiment to compare the effects of information system (IS) supporting reflective disclosure and information democratization on the sustainability footprint of a routine organizational work practice, paper printing. We find that both sensemaking processes lead to employees reducing their printing; however, the processes are interchangeable and do not mutually reinforce each other's effects. This finding contrasts a latent assumption of prior research about a co-dependence between reflective disclosure and information democratization, and suggests organizations have a choice in how they can design and use IS to help employees make sense of different possibilities for more eco-efficient work.
... Greenhouse crop production is one of the world's most intensive agricultural systems owing to high yield and energy consumption per unit area (Watson et al., 2018). Technical advances, overuse of chemical fertilizers and pesticides, and climatic factors such as sunlight, temperature, and air composition have all aided intensive greenhouse agricultural growth. ...
Full-text available
The application of the emergy approach to analyzing the sustainability of greenhouse systems has resulted in the deployment of adequate management in order to boost the production sustainability of these systems. The goal of this study was to apply the emergy approach to assess the sustainability of greenhouse tomato production systems. The data for this study was collected from greenhouse owners through face-to-face referrals and the completion of a questionnaire. Sixty three tomato greenhouses were chosen for this purpose in Mirjaveh district, Zahedan, Iran. In tomato greenhouse systems, the average total emergy supporting output was 1.06E+16 sej/1000 m² of greenhouse area. The unit emergy value of economic product (unit emergy value) was calculated to be 9.94E+03 sej/J, indicating that greenhouse systems outperform open field systems of various crops by at least 100 times. The significant proportion of purchased non-renewable resources affected the tomato production system's sustainability in this study. As a result, using productive renewable local environmental inputs, more efficient labor, and technologies to reduce greenhouse building inputs and acquire non-renewable inputs will make the greenhouse tomato production system more sustainable.
... In recent years, research focused primarily on enhancing energy efficiency in energy systems to cut energy consumption. Examples are manifold [17] and range from anomaly detection in energy consumption [18] over research on forecasting energy consumption [19,20] to the optimization of greenhouse energy use [21]. However, with the rapid increase in volatile renewable energies, balancing supply and demand becomes increasingly important. ...
Full-text available
The constant increase of intermittent renewable energies in the electricity grid complicates balancing supply and demand. Thus, research focuses on solutions in demand-side management using energy flexibility to resolve this problem. However, the interface between demand-side management and human behavior is often insufficiently addressed, although further potential could be leveraged here. This paper elaborates on the effect of light color on humans’ temperature and comfort perception in connection to energy flexibility. Researchers have found that people perceive blue light as colder and red light as warmer. To this end, we evaluate the effect of light color in a case study for a German industrial facility assuming sector-coupled electric heating. We simulate the entire heating period from October to April in an hourly granularity, using the well-established real options analysis and binomial trees as a decision support system to heuristically minimize energy expenditures by utilizing deferral options when energy prices are high. Our results show a 12.5% reduction in heating costs for sector-coupled electric heating, which extrapolated leads to CO2-eq emission savings of over 34,000 tons per year for the entire German industry, thereby supporting the energy transition.
... At the same time, a large amount of research is focused on technologies that can reduce the consumption of various types of energy [7,8]. This also applies to climatic problems associated with an increase in greenhouse gas emissions [9,10,11], and, as a consequence, the need to create branched sensor networks [12,13] to monitor the state of air pollution [14,15,16], water or soil. Also, deepening the study of energy informatics contributes to increasing the integration of energy systems and information and communication technologies [17,18,19,20]. ...
Full-text available
The system of statements for defining the problems of using the resources of information and measurement technologies (IMT) for solving the problems of energy informatics in a broad and narrow sense is given. Potential possibilities of using IMT resources are considered, which include: methods of mathematical, computer and physical modeling; methods of carrying out a full-scale experiment with using a priori and a posteriori data; models of research objects (ROs) under various modes of their functioning based on systems of multidimensional deterministic and random functions of time and space; a combination of physical and probabilistic measures to assess the quality of data during receiving, transmitting and processing information; options for the formation of primary information based on interaction of the RO with IMT sensor means; IMT methods, hardware and software, based on the use of modern achievements of science, technology and production. Evaluation of the quality of IMT research results is carried out on the basis of the concept of measurement results uncertainty.
... PV-generated solar power has seen tremendous growth over the last decade, with a total of 402.5 GW installed globally at the end of 2017-a 33% increase since 2016 alone (PVPS 2018), and minimizing integration-related costs has become ever more important. Besides PV applications, an increasing number of artificial lighting control systems, particularly in the context of controlled environment agriculture, also seek to accurately predict incident solar radiation in order to optimize electricity consumption and minimize costs (Albright et al. 2000;Watson et al. 2018). Several control systems in intelligent buildings also involve forecasting temperature and solar irradiance (Argiriou et al. 2004). ...
Conference Paper
Full-text available
The importance of incorporating underlying theory and domain knowledge while building artificial intelligence-based predictive models is examined. Using the context of predicting intraday solar radiation, we show that a theoretically grounded predictive model yields better performance and offers more interpretability and generalizability than a model that relies solely on other variables. Inclusion of theoretically-guided variables in data-driven predictive models is proposed as a means to mitigate overfitting and reduce potential bias.
Optimal allocation of the food, energy and water (FEW) resources is of emergent concern owing to depleting supply of the natural resources. Increasing demand for the FEW resources is attributable to growing population, migration, economic development, technological advancements and climate change. The FEW nexus (FEW-N) is an intricate system that requires robust quantitative decision-making tools to investigate the links between the various system components and sustainability. This study proposes a meta-model-based FEW-N system for addressing the issue of natural resource allocation for food and energy security. It incorporated an integrated model consisting of the Techno-Economic and Input/Output models in an Optimisation framework with maximum economic benefit as its objective function. The COIN-OR Branch and Cut (CBC) and CPLEX solvers in the Advanced Interactive Multidimensional Modelling System (AIMMs) were used to formulate and solve the optimisation problems. To validate the developed framework, the scenario analysis was performed on three cases in South Africa. First, it was found that using FEW resources for food production in dryland open fields, undercover greenhouses, and irrigated open fields was more profitable than for production of electrical energy from bioenergy, solar/wind-based hybrid renewable energy, and hydropower production systems. Second, the revenue of the sub-sector determined the percentage use of the FEW resources and the percentage contribution of technology options to food and energy security. Third, open fields, greenhouses, and irrigated open fields contributed significantly to food security. The holistic framework developed provided enhanced understanding of the FEW-N system. Resource security has significantly improved due to the ability of various technologies in each subsector to meet the food and energy demands of the specific population. Besides providing scientific support for national decisions regarding food, energy, and water policy, the proposed framework will also contribute to sustainable development in the states.
Full-text available
Smart greenhouse farming has emerged as one of the solutions to global food security, where farming productivity can be managed and improved in an automated manner. While it is known that plant development is highly dependent on the quantity and quality of light exposure, the specific impact of the different light properties is yet to be fully understood. In this study, using the model plant Arabidopsis, we systematically investigate how six different light properties (i.e., photoperiod, light offset, intensity, phase of dawn, duration of twilight and period) would affect plant development i.e., flowering time and hypocotyl (seedling stem) elongation using an established mathematical model of the plant circadian system relating light input to flowering time and hypocotyl elongation outputs for smart greenhouse application. We vary each of the light properties individually and then collectively to understand their effect on plant development. Our analyses show in comparison to the nominal value, the photoperiod of 18 hours, period of 24 hours, no light offset, phase of dawn of 0 hour, duration of twilight of 0.05 hour and a reduced light intensity of 1% are able to improve by at least 30% in days to flower (from 32.52 days to 20.61 days) and hypocotyl length (from 1.90 mm to 1.19mm) with the added benefit of reducing energy consumption by at least 15% (from 4.27 MWh/year to 3.62 MWh/year). These findings could provide beneficial solutions to the smart greenhouse farming industries in terms of achieving enhanced productivity while consuming less energy.
Full-text available
We contend that too few information systems (IS) academics engage in impactful research that offers solutions to global warming despite the fact that climate change is one of the most critical challenges facing this generation. Climate change is a major threat to global sustainability in the 21st century. Unfortunately, from submissions of our call for papers presenting IS solutions for environmental sustainability, we found only one paper worthy of publication. Given that IS have been the major force for productivity increases in the last half-century, we suggest that IS scholars should immerse themselves in creating solutions for environmental problems. Moreover, information is a perquisite for assessing the state of the environment and making appropriate decisions to ameliorate identified problems. Indeed, the IS scholarly community needs to help create a sustainable society. While there is an emerging body of IS scholarship under the banner of green IS, we strongly believe that we need to step up these efforts. Our experience indicates that the emergence of green IS as an academic discipline is still by far too slow relative to the needs of society. Too few people are working on green IS given its importance, and fewer still are publishing papers about IS solutions that could contribute to dealing with climate change. In this editorial, we speculate on some reasons for why and explore how the IS discipline can grasp the opportunity to contribute to one of the most important societal challenges of our time. We identify the major barriers that we assert curtail the involvement of IS scholars in green IS research; namely, incentives misalignment, the low status of practice science, data analysis poverty, identification of research scope, and research methods. We discuss each barrier and propose solutions for them.
Full-text available
Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show that the network outperforms the commercial forecast for lower step aheads (< 5). For larger step aheads the network’s performance is in the range of the commercial forecast. However, the neural network approach is fast, fairly precise and allows for further expansion with higher resolution.
Full-text available
The shift from manual to wireless metering is changing the information landscape of electricity billing from data poverty to data richness. Simultaneously, but at a slower pace, the Internet of things is emerging. Consequently, it will be feasible in the future to have real-time energy use for many objects. Thus, their work is a critical first step in learning how we can use wireless metering data streams of object energy usage to change consumption by lowering demand, by shifting demand to times when greener energy is more abundant, or alerting consumers to object use exceptions. Environmental sustainability is a real, colossal, and present problem that must be solved with some haste, in case there is a tipping point beyond which global warming, rising oceans, and ocean acidification cannot be reversed for hundreds of thousands of years, if ever.
Conference Paper
Full-text available
Since 1996 a dynamic model based climate control concept (IntelliGrow) has been developed in Denmark. The aim of the system is to adjust the greenhouse climate dynamic, so that the natural resources are used as optimal as possible. The concept has been proved to work in both growth chamber and greenhouse experiments, with many different species of pot plants, resulting in energy savings up to 40%, depending on the outside climate. Based on the former work a new system (IntelliGrow 2.0) is being developed which offers an improved user interface and an extensible component model. The goal is to test the system in full scale in five Danish commercial nurseries. The four steps to reach the goal are: 1) development of a demonstrator giving the grower advice on optimal climate control based on the IntelliGrow concept 2) testing the demonstrator at research facilities followed by tests at growers 3) development of an active climate control system that will take full control of the greenhouse climate based on the overall goals set by the grower 4) tests of the active climate control system at research facilities and at the growers. It will be possible to adjust the control by adding new components. A special emphasis will be on components that utilize local weather predictions for energy saving purposes and timing of production as well as components with photosynthesis based strategies for use of artificial light. We expect that the extension of intelligent climate control will result in better production management and resource utilization. A fast flow of knowledge from research to practice in the future will be established. Design of the concept and the first results are presented in this paper.
Conference Paper
Full-text available
In 2009, the industrial-size greenhouses in Denmark consumed 0.8 percent of the national electricity consumption. The increasing energy costs for heating and supplementary lighting have bankrupt many growers in 2010 causing an urgent need for remaining growers to reduce the consumption while preserving production quality. This paper presents a novel approach addressing this issue. We use weather forecasts and electricity prices to compute cost-and energy-efficient supplementary light plans that achieve the required plant growth defined by the grower. Experiments performed during the winter of 2009 – 2010 showed 25 percent savings with no negative effect on plant quality. To accelerate the impact of our approach, we chose to use Software Product Line Engineering, as it enables a greater variety of related software tools to be created faster. We have created a web-based analysis tool, DynaLight Web, for computing potential savings of our approach, and a desktop version, DynaLight Desktop, that computes an optimal supplementary light plan and controls the supplementary lighting accordantly. DynaLight Desktop is currently being field-tested at five industrial-size greenhouses. The development of these two tools is described together with the lessons learned from using Software Product Line Engineering in the domain of greenhouse software development.
Full-text available
Modern greenhouse climate control requires use of advanced climate-control models; however, adoption of advanced climate-control models in today's industrial greenhouse production is hindered by the shortcoming of existing climate-control systems to support non-invasive composition of independently-developed climate-control models. Despite the fact that adoption of advanced climate-control models allows growers to optimize their production through improved energy efficiency, improved plant quality and yield as well as reduced risks for various climate-related diseases, commercial vendors of industrial greenhouse-climate-control systems have not taken action to provide the necessary support for independent extensibility in their systems so far. Present climate-control systems require the control logic of independently-developed climate-control models to be merged into a single monolithic climate-control model. Hence, addition of new climate-control models requires modification and validation of this monolithic model. In this paper, we present a new approach to extensible greenhouse climate control that allows new climate-control models to be added dynamically to the climate-control system independently of each other. There is no need for merging models into a single monolithic model, as the approach allows independently-developed models to coexist alongside each other. The novelty of the approach is the use of a genetic algorithm to compute a balanced greenhouse climate that satisfies the multi-objective- optimization problem defined by the independently-added climate-control models. Feasibility of the approach is demonstrated through simulation of a number of selected production scenarios using a generic greenhouse simulator. The results of the simulations clearly show that the approach finds a balanced greenhouse climate that is satisfactory to the requirements of the independent climate-control models.
My thoughts on the prospects for LEDs in Agriculture
Two campanula species Campanula portenschlagiana (‘Blue Get Mee’) and Campanula cochlearifolia (‘Blue Wonder’) were grown in a cost-efficient light control system and the effect of supplemental light level and daily light integral (DLI) on growth and development was quantified. The alternative light control system (DynaLight desktop) automatically defines the most cost-efficient use of supplemental light based on predefined setpoints for daily photosynthesis integral (DPI), forecasted solar irradiance and the market price on electricity. It saves energy in high-cost periods of electricity, but creates irregular light periods which may disturb circadian rhythms and thereby affect plant growth and flower development. Plants were grown in four treatments controlled by DynaLight desktop with two setpoints for DPI (300 and 600 mmol m−2 leaf day−1) and two levels of supplemental lighting (48 and 84 μmol m−2 s−1). We found that differences in supplemental light levels, daily light hours or DLI had no effect on leaf area expansion and stem elongation, but there was a linear relation between dry matter accumulation and cumulative light integral (CLI) in both species, and a linear relation between the number of flowers and buds and CLI in ‘Blue Get Mee’. The results demonstrate that DLI was the main limiting factor for prediction of growth and development when two campanula species were grown in a cost-efficient light control system where the number of daily light hours was often below the critical day length of 14 h. However the light hours was always distributed in two or more light periods during the day, which thereby prevented dark periods longer than the critical night length of 9 h for campanulas.