Difference between revisions of "Journal:Simulation of greenhouse energy use: An application of energy informatics"

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==Abstract==
==Abstract==
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Prior research to reduce CAE electricity costs by making greenhouses smarter has produced a variety of "branded" solutions. Intelligrow<ref name="AaslyngIntelli03">{{cite journal |title=IntelliGrow: A greenhouse component-based climate control system |journal=Environmental Modelling & Software |author=Aaslyng, J.M.; Lund, J.B.; Ehler, N. et al. |volume=18 |issue=7 |page=657–66 |year=2003 |doi=10.1016/S1364-8152(03)00052-5}}</ref><ref name="MarkvartIntelli07">{{cite journal |title=IntelliGrow 2.0–A greenhouse component-based climate control system |journal=Acta Horticulturae |author=Markvart, J.; Kalita, S.; Nørregaard Jørgensen, B. et al. |volume=801 |page=507–14 |year=2007 |doi=10.17660/ActaHortic.2008.801.56}}</ref>, DynaLight<ref name="KjaerCost11">{{cite journal |title=Cost-efficient light control for production of two ''Campanula'' species |journal=Scientia Horticulturae |author=Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. |volume=129 |issue=4 |page=825-831 |year=2011 |doi=10.1016/j.scienta.2011.05.003}}</ref><ref name="KjaerTiming12">{{cite journal |title=Timing growth and development of ''Campanula'' by daily light integral and supplemental light level in a cost-efficient light control system |journal=Scientia Horticulturae |author=Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. |volume=143 |page=189–96 |year=2012 |doi=10.1016/j.scienta.2012.06.026}}</ref><ref name="ClausenIntegrating15">{{cite journal |title=Integrating Commercial Greenhouses in the Smart Grid with Demand Response based Control of Supplemental Lighting |journal=Proceedings of the 2015 International Conference on Industrial Technology and Management Science |author=Clausen, A.; Maersk-Moeller, H.M.; Sørensen, J.C. et al. |year=2015 |doi=10.2991/itms-15.2015.50}}</ref>, and DynaGrow<ref name="SørensenDyna16">{{cite journal |title=DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses |journal=Proceedings of the 8th International Joint Conference on Computational Intelligence |author=Sørensen, J.C.; Kjaer, K.H.; Ottosen, C.-O. et al. |pages=41–8 |year=2016 |doi=10.5220/0006047500410048}}</ref> 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 greenhouse’s 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<ref name="SørensenDyna16" />, the concern of this article.
Prior research to reduce CAE electricity costs by making greenhouses smarter has produced a variety of "branded" solutions. Intelligrow<ref name="AaslyngIntelli03">{{cite journal |title=IntelliGrow: A greenhouse component-based climate control system |journal=Environmental Modelling & Software |author=Aaslyng, J.M.; Lund, J.B.; Ehler, N. et al. |volume=18 |issue=7 |page=657–66 |year=2003 |doi=10.1016/S1364-8152(03)00052-5}}</ref><ref name="MarkvartIntelli07">{{cite journal |title=IntelliGrow 2.0–A greenhouse component-based climate control system |journal=Acta Horticulturae |author=Markvart, J.; Kalita, S.; Nørregaard Jørgensen, B. et al. |volume=801 |page=507–14 |year=2007 |doi=10.17660/ActaHortic.2008.801.56}}</ref>, DynaLight<ref name="KjaerCost11">{{cite journal |title=Cost-efficient light control for production of two ''Campanula'' species |journal=Scientia Horticulturae |author=Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. |volume=129 |issue=4 |page=825-831 |year=2011 |doi=10.1016/j.scienta.2011.05.003}}</ref><ref name="KjaerTiming12">{{cite journal |title=Timing growth and development of ''Campanula'' by daily light integral and supplemental light level in a cost-efficient light control system |journal=Scientia Horticulturae |author=Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. |volume=143 |page=189–96 |year=2012 |doi=10.1016/j.scienta.2012.06.026}}</ref><ref name="ClausenIntegrating15">{{cite journal |title=Integrating Commercial Greenhouses in the Smart Grid with Demand Response based Control of Supplemental Lighting |journal=Proceedings of the 2015 International Conference on Industrial Technology and Management Science |author=Clausen, A.; Maersk-Moeller, H.M.; Sørensen, J.C. et al. |year=2015 |doi=10.2991/itms-15.2015.50}}</ref>, and DynaGrow<ref name="SørensenDyna16">{{cite journal |title=DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses |journal=Proceedings of the 8th International Joint Conference on Computational Intelligence |author=Sørensen, J.C.; Kjaer, K.H.; Ottosen, C.-O. et al. |pages=41–8 |year=2016 |doi=10.5220/0006047500410048}}</ref> 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 greenhouse’s 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<ref name="SørensenDyna16" />, 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 economically 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 sugars{{Efn|Compounds of carbon, hydrogen, and oxygen. Sucrose is C<sub>12</sub>H<sub>22</sub>O<sub>11</sub>.}} 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 electron transport then results in the production of chemicals required for the synthesis of sugars. The electron transport rate (ETR) is a direct measure of the light reactions of photosynthesis 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 micromoles{{Efn|One mol &#61; 6.022 × 10<sup>23</sup> (Avogadro’s constant) photons or electrons.}} per square meter per second (μmol m<sup>-2</sup> s<sup>−1</sup> 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<sup>(−b.PPF)</sup>), where a and b vary by species. The fitted saturation curve for lettuce illustrates the nature of this relationship (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.


[[File:Fig1 Watson EnergyInfo2018 1-1.png|500px]]
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{| border="0" cellpadding="5" cellspacing="0" width="500px"
|-
  | style="background-color:white; padding-left:10px; padding-right:10px;"| <blockquote>'''Figure 1.''' Electron transport rate (ETR) of lettuce as a function of the photosynthetic photon flux (PPF). Source: Unpublished University of Georgia research</blockquote>
|-
|}
|}
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<sup>−2</sup> day<sup>−1</sup> of electrons. On a typical day, this rate of electron transport requires approximately 18 mol of photons m<sup>−2</sup> day<sup>−1</sup>. This is close to the recommended daily light level for year-round production of high-quality lettuce of 17 mol m<sup>−2</sup> day<sup>−1</sup>.<ref name="BothHydro97">{{cite journal |title=Hydroponic lettuce production influenced by integrated supplemental light levels in a controlled environment agriculture facility: Experimental results |journal=Acta Horticulturae |author=Both, A.J.; Albright, L.D.; Langhans, R.W. et al. |volume=418 |page=45–52 |year=1997 |doi=10.17660/ActaHortic.1997.418.5}}</ref>
We can convert daily electron transport into a required level of photons per second, as follows:
:''D'' = ETR in moles m<sup>−2</sup> day<sup>−1</sup> to maximize growth
:Tetr = threshold ETR in μmoles m<sup>−2</sup> s<sup>−1</sup> to achieve ''D''
:Tppf = threshold PPF in μmoles m<sup>−2</sup> s<sup>−1</sup> to achieve ''D''
:''t'' = seconds of operation per day of the greenhouse
<math>{Tetr} = \frac{D \ast 10^{6}}{t}</math>
<math>{Tppf} = \frac{1}{b}\log\left( \frac{a}{a - Tetr} \right)</math>
===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 (400–700 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 an LED light, the relationship between energy consumption and photons generated is essentially linear<ref name="NEMAEnergy15">{{cite web |url=https://www.nema.org/Standards/SecureDocuments/NEMALSD%2073-2015%20WATERMARKED.pdf |format=PDF |title=Energy Savings with Fluorescent and LED Dimming |author=NEMA Ballast and Lighting Controls |publisher=National Electrical Manufacturers Association |date=14 July 2015 |accessdate=18 June 2018}}</ref>, and we so assume in this research.
Leveraging this kind of lighting, a local company, Phytosynthetix{{Efn|http://phytosynthetix.com/}}, collaborated with the University of Georgia’s 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 crop’s threshold needs.<ref name="vanIerselAnAdapt17" /> 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 crop’s physiological ability to use that light efficiently (see Fig. 1). This is an important innovation in greenhouse 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 informatics principles<ref name="WatsonInfo10" /> 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.
[[File:Fig2 Watson EnergyInfo2018 1-1.png|600px]]
{{clear}}
{|
| STYLE="vertical-align:top;"|
{| border="0" cellpadding="5" cellspacing="0" width="600px"
|-
  | style="background-color:white; padding-left:10px; padding-right:10px;"| <blockquote>'''Figure 2.''' Energy informatics framework</blockquote>
|-
|}
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==Simulations==


==Footnotes==
==Footnotes==
Line 66: Line 125:


==Notes==
==Notes==
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References in the original are listed alphabetically; they appear here in order of appearance, by design. The original article cited a Wikipedia page; replaced with URL and info for citation on that wiki page. To more easily differentiate footnotes from references, the original footnotes (which where numbered) were updated to use lowercase letters. Footnotes referencing web pages were turned into proper citations.
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References in the original are listed alphabetically; they appear here in order of appearance, by design. The original article cited a Wikipedia page; replaced with URL and info for citation on that wiki page. To more easily differentiate footnotes from references, the original footnotes (which where numbered) were updated to use lowercase letters. Most footnotes referencing web pages were turned into proper citations.


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Revision as of 23:55, 10 December 2018

Full article title Simulation of greenhouse energy use: An application of energy informatics
Journal Energy Informatics
Author(s) Watson, Richard T.; Boudreau, Marie-Claude; van Iersel, Marc W.
Author affiliation(s) University of Georgia - Athens
Primary contact Email: rwatson at terry dot uga dot edu
Year published 2018
Volume and issue 1(1)
DOI 10.1007/s42162-018-0005-7
ISSN 2520-8942
Distribution license Creative Commons Attribution 4.0 International
Website https://energyinformatics.springeropen.com/articles/10.1007/s42162-018-0005-7
Download https://energyinformatics.springeropen.com/track/pdf/10.1007/s42162-018-0005-7 (PDF)
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Abstract

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 Southeast U.S. 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

Background

In January 2007, the first two authors of this article started a project to link information 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[1] 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.[2][3] 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.[4][5] Leveraging the energy informatics framework, we produced a case study on how one of the world’s major logistic companies was applying the principles to reduce energy consumption and advance sustainability.[5] 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 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 consumed 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 world’s 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%.[6] 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.[7] 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.[8] 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.[8]

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 dominate EMEA production; in the Americas, horticulture is mainly in North America; and China has been growing rapidly with more than one million greenhouses.[9]

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, as well as 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.[10] At this stage, the smart greenhouse market is about 3% of the world market.

The greenhouse industry’s 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.[11] Currently, many commercial greenhouses use high intensity discharge (HID) lights[a], 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 a crop’s 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 hours 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 produced a variety of "branded" solutions. Intelligrow[12][13], DynaLight[14][15][16], and DynaGrow[17] 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 greenhouse’s 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[17], 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 economically 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 sugars[b] 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 electron transport then results in the production of chemicals required for the synthesis of sugars. The electron transport rate (ETR) is a direct measure of the light reactions of photosynthesis 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 micromoles[c] per square meter per second (μmol m-2 s−1 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(−b.PPF)), where a and b vary by species. The fitted saturation curve for lettuce illustrates the nature of this relationship (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.


Fig1 Watson EnergyInfo2018 1-1.png

Figure 1. Electron transport rate (ETR) of lettuce as a function of the photosynthetic photon flux (PPF). Source: Unpublished University of Georgia research

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−2 day−1 of electrons. On a typical day, this rate of electron transport requires approximately 18 mol of photons m−2 day−1. This is close to the recommended daily light level for year-round production of high-quality lettuce of 17 mol m−2 day−1.[18]

We can convert daily electron transport into a required level of photons per second, as follows:

D = ETR in moles m−2 day−1 to maximize growth
Tetr = threshold ETR in μmoles m−2 s−1 to achieve D
Tppf = threshold PPF in μmoles m−2 s−1 to achieve D
t = seconds of operation per day of the greenhouse

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 (400–700 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 an LED light, the relationship between energy consumption and photons generated is essentially linear[19], and we so assume in this research.

Leveraging this kind of lighting, a local company, Phytosynthetix[d], collaborated with the University of Georgia’s 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 crop’s threshold needs.[11] 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 crop’s physiological ability to use that light efficiently (see Fig. 1). This is an important innovation in greenhouse 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 informatics principles[4] 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.


Fig2 Watson EnergyInfo2018 1-1.png

Figure 2. Energy informatics framework

Simulations

Footnotes

  1. ↑ Which can be further subdivided into two types: metal halide and high-pressure sodium.
  2. ↑ Compounds of carbon, hydrogen, and oxygen. Sucrose is C12H22O11.
  3. ↑ One mol = 6.022 × 1023 (Avogadro’s constant) photons or electrons.
  4. ↑ http://phytosynthetix.com/

References

  1. ↑ Holden, J.P. (2009). "Energy for Change: Introduction to the Special Issue on Energy & Climate". Innovations: Technology, Governance, Globalization 4 (4): 3–11. doi:10.1162/itgg.2009.4.4.3. 
  2. ↑ Gholami, R.; Watson, R.T.; Hasan, H. et al. (2016). "Information Systems Solutions for Environmental Sustainability: How Can We Do More?". Journal of the Association for Information Systems 17 (8): 2. https://aisel.aisnet.org/jais/vol17/iss8/2. 
  3. ↑ Malhotra, A.; Melville, N.P.; Watson, R.T. (2013). "Spurring impactful research on information systems for environmental sustainability". MIS Quarterly 37 (4): 1265-1273. doi:10.25300/MISQ/2013/37:4.3. 
  4. ↑ 4.0 4.1 Watson, R.T.; Boudreau, M.-C.; Chen, A.J. (2010). "Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community". MIS Quarterly 34 (1): 23–38. 
  5. ↑ 5.0 5.1 Watson, R.T.; Boudreau, M.-C. (2011). Energy Informatics. Green ePress. 
  6. ↑ Food and Agriculture Organization of the United Nations (12 October 2009). "Global agriculture towards 2050" (PDF). http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf. 
  7. ↑ Magnin, C. (August 2016). "How big data will revolutionize the global food chain". Digital McKinsey. McKinsey & Company. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/how-big-data-will-revolutionize-the-global-food-chain. Retrieved 18 June 2018. 
  8. ↑ 8.0 8.1 "CEA". CEA Capital Holdings LLC. 2014. http://www.ceacapitalholdings.com/education/sub-page/. Retrieved 18 June 2018. 
  9. ↑ Technavio (13 July 2017). "Global Greenhouse Horticulture Market 2017-2021: Key Geographies and Forecasts by Technavio". BusinessWire. https://www.businesswire.com/news/home/20170713006114/en/Global-Greenhouse-Horticulture-Market-2017-2021-Key-Geographies. 
  10. ↑ "Smart Greenhouse Market by Technology: Global Industry Perspective, Comprehensive Analysis and Forecast, 2016 – 2022". Zion Market Research. 18 January 2017. https://www.zionmarketresearch.com/report/smart-greenhouse-market. 
  11. ↑ 11.0 11.1 van Iersel, M.W.; Gianino, D. (2017). "An Adaptive Control Approach for Light-emitting Diode Lights Can Reduce the Energy Costs of Supplemental Lighting in Greenhouses". HortScience 52 (1): 72–77. doi:10.21273/HORTSCI11385-16. 
  12. ↑ Aaslyng, J.M.; Lund, J.B.; Ehler, N. et al. (2003). "IntelliGrow: A greenhouse component-based climate control system". Environmental Modelling & Software 18 (7): 657–66. doi:10.1016/S1364-8152(03)00052-5. 
  13. ↑ Markvart, J.; Kalita, S.; Nørregaard Jørgensen, B. et al. (2007). "IntelliGrow 2.0–A greenhouse component-based climate control system". Acta Horticulturae 801: 507–14. doi:10.17660/ActaHortic.2008.801.56. 
  14. ↑ Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. (2011). "Cost-efficient light control for production of two Campanula species". Scientia Horticulturae 129 (4): 825-831. doi:10.1016/j.scienta.2011.05.003. 
  15. ↑ Kjaer, K.H.; Ottosen, C.-O.; Jørgensen, B.N. (2012). "Timing growth and development of Campanula by daily light integral and supplemental light level in a cost-efficient light control system". Scientia Horticulturae 143: 189–96. doi:10.1016/j.scienta.2012.06.026. 
  16. ↑ Clausen, A.; Maersk-Moeller, H.M.; Sørensen, J.C. et al. (2015). "Integrating Commercial Greenhouses in the Smart Grid with Demand Response based Control of Supplemental Lighting". Proceedings of the 2015 International Conference on Industrial Technology and Management Science. doi:10.2991/itms-15.2015.50. 
  17. ↑ 17.0 17.1 Sørensen, J.C.; Kjaer, K.H.; Ottosen, C.-O. et al. (2016). "DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses". Proceedings of the 8th International Joint Conference on Computational Intelligence: 41–8. doi:10.5220/0006047500410048. 
  18. ↑ Both, A.J.; Albright, L.D.; Langhans, R.W. et al. (1997). "Hydroponic lettuce production influenced by integrated supplemental light levels in a controlled environment agriculture facility: Experimental results". Acta Horticulturae 418: 45–52. doi:10.17660/ActaHortic.1997.418.5. 
  19. ↑ NEMA Ballast and Lighting Controls (14 July 2015). "Energy Savings with Fluorescent and LED Dimming" (PDF). National Electrical Manufacturers Association. https://www.nema.org/Standards/SecureDocuments/NEMALSD%2073-2015%20WATERMARKED.pdf. Retrieved 18 June 2018. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References in the original are listed alphabetically; they appear here in order of appearance, by design. The original article cited a Wikipedia page; replaced with URL and info for citation on that wiki page. To more easily differentiate footnotes from references, the original footnotes (which where numbered) were updated to use lowercase letters. Most footnotes referencing web pages were turned into proper citations.

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