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DOI | 10.3390/agriculture14020316 |
Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach | |
Garcia, Carlos Alejandro Perez; Bovo, Marco; Torreggiani, Daniele; Tassinari, Patrizia; Benni, Stefano | |
发表日期 | 2024 |
EISSN | 2077-0472 |
起始页码 | 14 |
结束页码 | 2 |
卷号 | 14期号:2 |
英文摘要 | 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. |
英文关键词 | microclimate control; Prophet; heat stress; machine learning; livestock building |
语种 | 英语 |
WOS研究方向 | Agriculture |
WOS类目 | Agronomy |
WOS记录号 | WOS:001172134900001 |
来源期刊 | AGRICULTURE-BASEL
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/295689 |
作者单位 | University of Bologna |
推荐引用方式 GB/T 7714 | Garcia, Carlos Alejandro Perez,Bovo, Marco,Torreggiani, Daniele,et al. Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach[J],2024,14(2). |
APA | Garcia, Carlos Alejandro Perez,Bovo, Marco,Torreggiani, Daniele,Tassinari, Patrizia,&Benni, Stefano.(2024).Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach.AGRICULTURE-BASEL,14(2). |
MLA | Garcia, Carlos Alejandro Perez,et al."Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach".AGRICULTURE-BASEL 14.2(2024). |
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