Feature Selection to Predict LED Light Energy Consumption with Specific Light Recipes in Closed Plant Production Systems
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Greenhouse Technology and Climate Control · Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Dirección de Posgrados e Investigación, Universidad Politécnica de Aguascalientes, Calle Paseo San Gerardo #201, Fracc. San Gerardo, Aguascalientes 20342, Mexico
Laboratorio de Iluminación Artificial, Tecnológico Nacional de México/IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, Aguascalientes 20670, Mexico
Departamento de Ingenierías, Tecnológico Nacional de México/IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, Aguascalientes 20670, Mexico
Funding
Consejo Nacional de Ciencia y Tecnología (CONACYT) in Mexico · INFRA-2016-01 · CB-2016-01
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