ARIMA modeling of animal zone temperature in weaned iglet buildings: Design of the model
Predictive models provide an efficient tool for improving environmental control in livestock buildings. In this article, a robust and accurate ARIMA model for forecasting temperature inside a building for weaned piglets in the range 6 to 20 kg live weight was built. The candidate models presented in...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | Universidad de Santiago de Compostela (USC) |
| Repositório: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglês |
| OAI Identifier: | oai:minerva.usc.gal:10347/45636 |
| Acesso em linha: | https://hdl.handle.net/10347/45636 |
| Access Level: | Acceso aberto |
| Palavra-chave: | ARIMA Forecast Model Piglet Temperature 310408 Porcinos 310404 Cuidado y Explotación 310403 Cría |
| Resumo: | Predictive models provide an efficient tool for improving environmental control in livestock buildings. In this article, a robust and accurate ARIMA model for forecasting temperature inside a building for weaned piglets in the range 6 to 20 kg live weight was built. The candidate models presented in this article predict 10 min values during a complete production cycle, which makes them suitable as predictive models for improving control strategies. The accuracy of the base model, which used outdoor temperature as a predictor variable, can be improved by appropriately replacing the outliers in the time series. Because accuracy increases with the increase in the number of predictor variables, the model that used four variables (temperature at the air outlet, area of the air outlet through the fan, volume of air extracted, and animal live weight) provided the best results, with a maximum absolute error of 0.840°C, a root mean square error of 0.204°C, and random residuals according to the Ljung-Box statistic. This model used only the values of the last 20 min for the forecast, which suggests low thermal inertia in the animal zone. In addition, the model includes predictor variables that are representative of outdoor conditions, operation of the systems, and animal health status |
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