Temperature and Relative Humidity Prediction in Swine Livestock Buildings

Molano-Jimenez, Andres; Orjuela-Cañón, Álvaro David; Wilmer, Acosta-Burbano

doi:10.1109/la-cci.2018.8625245

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…When compared with our study, both the accuracy and validation was limited in the previous studies. Likewise, [ 10 ] proposed an ANN-based MLP model to predict the temperature and relative humidity of a swine building. The study validates the model by MSE and MAE; the IRH’s RMSE was better than our proposed model (RMSE = 0.8310), whereas the IAT’s RMSE was inferior (RMSE = 0.8095).…”

Section: Discussionmentioning

confidence: 99%

“…Heretofore researchers developed several models as dynamic, steady-state models, heat balance equations, computational fluid dynamics to predict indoor air temperature (IAT), and indoor relative humidity (IRH). Most of the previous models were developed by using the theoretical relationship between heat and mass transfer functions, energy-oriented facets, and indoor fluid dynamics [ 3 , 8 , 9 , 10 ]. Such mechanisms require complex information such as airflow dynamics, animal information, and fan specifications to derive the equations.…”

Section: Introductionmentioning

confidence: 99%

“…Applying such technologies could analyze the large data sets more effectively with relative ease than physical or statistical models. Especially for determining linear and nonlinear variables that follow time-series such as indoor microclimate modelling field, the ML-based models have proven it outperformed the statistical models [ 10 ]. Several training algorithms are available for the ML framework, including linear regression (LR), decision trees regression (DTR), random forest regression (RFR), support vector regression (SVR), etc., have been developed to handle the regression and classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, ref. [ 10 ] utilized an ANN model to predict a swine building’s temperature and relative humidity, whereas the growth performance of swine was analyzed with decision trees and support vector machines by a previous study [ 12 ]. Likewise, ref.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Ref. [ 10 ] employed a multilayered perceptron with a backpropagation model to predict the IAT and IRH of a swine building, and evaluate the model without comparison with other models. In contrast, refs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Prediction of cooling effect of constant temperature community bin based on BP neural network

Zhu

et al. 2023

Int J Biometeorol

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Outdoor Relative Humidity Prediction via Machine Learning Techniques

Zarinkamar¹,

Mayorga

2021

J ENVIRON INFORM LET

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In an environmental control system, relative humidity (RH) is a very important factor because of its direct impact on humans or even animals and plants. However, there are few studies focused on prediction of humidity variables. The main objective of this paper is to show the capability of machine learning algorithms for RH prediction. In this study, a Long-Short Term Memory (LSTM) and four popular machine learning algorithms; namely, Multi-Layer Perceptron (MLP), Random Forest (RF), k-Nearest Neighbor (KNN) and Support Vector Machine for Regression (SVMR) are presented for a year period of time-series relative humidity to predict for a particular case in an Italian city. In order to have precise performance, data pre-processing is done before running the models. This thorough examination proves the positive effect of all Machine Learning-based algorithms in time-series relative humidity prediction based on predictive accuracy. Over the different metrics, LSTM indicates the best performance among all considered algorithms.

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Temperature and Relative Humidity Prediction in Swine Livestock Buildings

Cited by 5 publications

References 9 publications

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Prediction of cooling effect of constant temperature community bin based on BP neural network

Outdoor Relative Humidity Prediction via Machine Learning Techniques

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