封闭式工厂生产系统中用特定光配方预测LED光能消耗的特征选择

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时间:2023-03-14

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Citation: Montes Rivera, M.;
Escalante-Garcia, N.; Dena-Aguilar,
J.A.; Olvera-Gonzalez, E.;
Vacas-Jacques, P. Feature Selection to
Predict LED Light Energy
Consumption with Specific Light
Recipes in Closed Plant Production
Systems. Appl. Sci. 2022, 12, 5901.
https://doi.org/10.3390/
app12125901
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara Gallardo
Saavedra
Received: 3 May 2022
Accepted: 26 May 2022
Published: 9 June 2022
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Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Feature Selection to Predict LED Light Energy Consumption
with Specific Light Recipes in Closed Plant Production Systems
Martín Montes Rivera
1,
* , Nivia Escalante-Garcia
2,
*, José Alonso Dena-Aguilar
3
, Ernesto Olvera-Gonzalez
2
and Paulino Vacas-Jacques
3
1
Dirección de Posgrados e Investigación, Universidad Politécnica de Aguascalientes,
Calle Paseo San Gerardo #201, Fracc. San Gerardo, Aguascalientes 20342, Mexico
2
Laboratorio de Iluminación Artificial, Tecnológico Nacional de México/IT de Pabellón de Arteaga,
Carretera a la Estación de Rincón Km. 1, Aguascalientes 20670, Mexico; jose.og@pabellon.tecnm.mx
3
Departamento de Ingenierías, Tecnológico Nacional de México/IT de Pabellón de Arteaga,
Carretera a la Estación de Rincón Km. 1, Aguascalientes 20670, Mexico;
jose.da@pabellon.tecnm.mx (J.A.D.-A.); paulino.vj@pabellon.tecnm.mx (P.V.-J.)
* Correspondence: martin.montes@upa.edu.mx (M.M.R.); nivia.eg@pabellon.tecnm.mx (N.E.-G.)
Abstract: The use of closed growth environments, such as greenhouses, plant factories, and vertical
farms, represents a sustainable alternative for fresh food production. Closed plant production systems
(CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an
essential role in CPPSs as it promotes growth by providing optimal conditions for plant development.
Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation
systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point
to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and
profitability in closed plant production systems. However, no tools have been applied in the literature
to contribute to energy savings in LED-type artificial radiation systems through the configuration
of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-
evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI)
methods integrated into the prediction crucial variables such as each input-variable light color or
specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency
(pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression
model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs.
This stage is critical because it identifies the most representative features for training the model, and
the other stages depend on it. These tools can enable further in-depth analysis of the energy savings
that can be obtained with light recipes and pulsed and continuous operation light modes in artificial
LED lighting systems.
Keywords: light wavelength; energy efficiency; features selection; machine learning
1. Introduction
1.1. LED Lights in Closed Plant Production Systems
Agriculture in 2050 will have to produce almost 50% more output to meet the de-
mand for food supplies, presenting it with a crucial challenge in meeting the increase in
demand [
1
]. Technological development and innovation can offer alternatives to ensure
food security sustainably. The use of closed growth environments, such as greenhouses,
plant factories, and vertical farms [26], represents a sustainable alternative for fresh food
production. In closed plant production systems (CPPSs), several variables can be controlled
and optimized, such as water, fertilizers, CO
2
injection, and temperature, as well as the
quantity and quality of light thus ensuring minimum greenhouse gas emissions [
3
]. CPPSs
allow growing of any plant variety, no matter the season of the year. Artificial lighting
plays an essential role in CPPSs, as it promotes growth by providing optimal conditions for
Appl. Sci. 2022, 12, 5901. https://doi.org/10.3390/app12125901 https://www.mdpi.com/journal/applsci
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