Machine learning algorithms to assess the thermal behavior of a Moroccan agriculture greenhouse
The objective of this paper is to assess the potential of machine learning algorithms in predicting the indoor air temperature in a greenhouse using the outdoor data. A dataset gathering the main weather data and the indoor air temperature of a greenhouse located in Agadir, Morocco was used for this...
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Elsevier
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/15c44c42de524bafbca4b66a66fd668e |
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| Summary: | The objective of this paper is to assess the potential of machine learning algorithms in predicting the indoor air temperature in a greenhouse using the outdoor data. A dataset gathering the main weather data and the indoor air temperature of a greenhouse located in Agadir, Morocco was used for this purpose. Machine learning models including support vector machine based-regression, ensemble trees and Gaussian process regression are compared against multiple linear regression models. This comparison was carried out on the basis of a 5-fold cross validation framework and across unseen data. The results show that all predictive models are capable of describing the indoor air temperature of the greenhouse and perform well (R2 > 0.9), with only 10% fraction of the dataset as training data. The Gaussian process regression outperforms all models, with R2 = 0.94 in the 5-fold cross validation test. However, the computational time related to the training of Gaussian process regression model is slightly higher than other machine learning models. |
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