Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System
Artificial lighting is a key factor in Closed Production Plant Systems (CPPS). A significant light-emitting diode (LED) technology attribute is the emission of different wavelengths, called light recipes. Light recipes are typically configured in continuous mode, but can also be configured in pulsed...
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| Format: | Article |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/6/2735 |
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| author | Ernesto Olvera-Gonzalez Martín Montes Rivera Nivia Escalante-Garcia Eduardo Flores-Gallegos |
| author_facet | Ernesto Olvera-Gonzalez Martín Montes Rivera Nivia Escalante-Garcia Eduardo Flores-Gallegos |
| author_sort | Ernesto Olvera-Gonzalez |
| collection | DOAJ |
| description | Artificial lighting is a key factor in Closed Production Plant Systems (CPPS). A significant light-emitting diode (LED) technology attribute is the emission of different wavelengths, called light recipes. Light recipes are typically configured in continuous mode, but can also be configured in pulsed mode to save energy. We propose two nonlinear models, i.e., genetic programing (GP) and feedforward artificial neural networks (FNNs) to predict energy consumption in CPPS. The generated models use the following input variables: intensity, red light component, blue light component, green light component, and white light component; and the following operation modes: continuous and pulsed light including pulsed frequency, and duty cycle as well energy consumption as output. A Spearman’s correlation was applied to generate a model with only representative inputs. Two datasets were applied. The first (Test 1), with 5700 samples with similar input ranges, was used to train and evaluate, while the second (Test 2), included 160 total datapoints in different input ranges. The metrics that allowed a quantitative evaluation of the model’s performance were MAPE, MSE, MAE, and SEE. Our implemented models achieved an accuracy of 96.1% for the GP model and 98.99% for the FNNs model. The models used in this proposal can be applied or programmed as part of the monitoring system for CPPS which prioritize energy efficiency. The nonlinear models provide a further analysis for energy savings due to the light recipe and operation light mode, i.e., pulsed and continuous on artificial LED lighting systems. |
| first_indexed | 2024-03-10T13:06:40Z |
| format | Article |
| id | doaj.art-4f640308732448b0b2d40c42d9f8d6f8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:06:40Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4f640308732448b0b2d40c42d9f8d6f82023-11-21T11:03:50ZengMDPI AGApplied Sciences2076-34172021-03-01116273510.3390/app11062735Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production SystemErnesto Olvera-Gonzalez0Martín Montes Rivera1Nivia Escalante-Garcia2Eduardo Flores-Gallegos3Laboratorio de Iluminación Artificial, Tecnológico Nacional de México/IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, 20670 Aguascalientes, MexicoResearch and Postgraduate Studies, Universidad Politécnica de Aguascalientes, Calle Paseo San Gerardo #201, Fracc. San Gerardo, 20342 Aguascalientes, MexicoLaboratorio de Iluminación Artificial, Tecnológico Nacional de México/IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, 20670 Aguascalientes, MexicoLaboratorio de Iluminación Artificial, Tecnológico Nacional de México/IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, 20670 Aguascalientes, MexicoArtificial lighting is a key factor in Closed Production Plant Systems (CPPS). A significant light-emitting diode (LED) technology attribute is the emission of different wavelengths, called light recipes. Light recipes are typically configured in continuous mode, but can also be configured in pulsed mode to save energy. We propose two nonlinear models, i.e., genetic programing (GP) and feedforward artificial neural networks (FNNs) to predict energy consumption in CPPS. The generated models use the following input variables: intensity, red light component, blue light component, green light component, and white light component; and the following operation modes: continuous and pulsed light including pulsed frequency, and duty cycle as well energy consumption as output. A Spearman’s correlation was applied to generate a model with only representative inputs. Two datasets were applied. The first (Test 1), with 5700 samples with similar input ranges, was used to train and evaluate, while the second (Test 2), included 160 total datapoints in different input ranges. The metrics that allowed a quantitative evaluation of the model’s performance were MAPE, MSE, MAE, and SEE. Our implemented models achieved an accuracy of 96.1% for the GP model and 98.99% for the FNNs model. The models used in this proposal can be applied or programmed as part of the monitoring system for CPPS which prioritize energy efficiency. The nonlinear models provide a further analysis for energy savings due to the light recipe and operation light mode, i.e., pulsed and continuous on artificial LED lighting systems.https://www.mdpi.com/2076-3417/11/6/2735artificial intelligentLED lightenergy consumptionartificial neural networkgenetic programmingclosed plant production systems |
| spellingShingle | Ernesto Olvera-Gonzalez Martín Montes Rivera Nivia Escalante-Garcia Eduardo Flores-Gallegos Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System Applied Sciences artificial intelligent LED light energy consumption artificial neural network genetic programming closed plant production systems |
| title | Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System |
| title_full | Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System |
| title_fullStr | Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System |
| title_full_unstemmed | Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System |
| title_short | Modeling Energy LED Light Consumption Based on an Artificial Intelligent Method Applied to Closed Plant Production System |
| title_sort | modeling energy led light consumption based on an artificial intelligent method applied to closed plant production system |
| topic | artificial intelligent LED light energy consumption artificial neural network genetic programming closed plant production systems |
| url | https://www.mdpi.com/2076-3417/11/6/2735 |
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