Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach

Abstract

The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control.