Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach
This research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximat...
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/3/633 |
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| author | Carlos Alejandro Perez Garcia Patrizia Tassinari Daniele Torreggiani Marco Bovo |
| author_facet | Carlos Alejandro Perez Garcia Patrizia Tassinari Daniele Torreggiani Marco Bovo |
| author_sort | Carlos Alejandro Perez Garcia |
| collection | DOAJ |
| description | This research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximately two years of historical energy consumption data, collected through a smart monitoring system deployed on the farm, were utilized as the primary input for the NeuralProphet model to predict long-term trends and seasonal variations. The computational results demonstrated satisfactory performance, achieving a coefficient of determination (R<sup>2</sup>) of 0.85 and a mean absolute error (MAE) of 27.47 kWh. The model effectively captured general trends and seasonal patterns, providing valuable insights into energy usage under existing operational conditions. However, short-term fluctuations were less accurately predicted due to the exclusion of exogenous climatic variables, such as temperature and humidity. The proposed model demonstrated superiority over traditional approaches in its capacity to forecast long-term energy demand, providing critical support for energy management and strategic decision-making in dairy farm operations. |
| format | Article |
| id | doaj-art-67368b335e1a4f7095cb202a7ebc1aec |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-67368b335e1a4f7095cb202a7ebc1aec2025-08-20T02:48:02ZengMDPI AGEnergies1996-10732025-01-0118363310.3390/en18030633Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning ApproachCarlos Alejandro Perez Garcia0Patrizia Tassinari1Daniele Torreggiani2Marco Bovo3Department of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 48, 40127 Bologna, ItalyDepartment of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 48, 40127 Bologna, ItalyDepartment of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 48, 40127 Bologna, ItalyDepartment of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 48, 40127 Bologna, ItalyThis research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximately two years of historical energy consumption data, collected through a smart monitoring system deployed on the farm, were utilized as the primary input for the NeuralProphet model to predict long-term trends and seasonal variations. The computational results demonstrated satisfactory performance, achieving a coefficient of determination (R<sup>2</sup>) of 0.85 and a mean absolute error (MAE) of 27.47 kWh. The model effectively captured general trends and seasonal patterns, providing valuable insights into energy usage under existing operational conditions. However, short-term fluctuations were less accurately predicted due to the exclusion of exogenous climatic variables, such as temperature and humidity. The proposed model demonstrated superiority over traditional approaches in its capacity to forecast long-term energy demand, providing critical support for energy management and strategic decision-making in dairy farm operations.https://www.mdpi.com/1996-1073/18/3/633machine learningenergy efficiencyNeuralProphetenergy load forecastcattle |
| spellingShingle | Carlos Alejandro Perez Garcia Patrizia Tassinari Daniele Torreggiani Marco Bovo Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach Energies machine learning energy efficiency NeuralProphet energy load forecast cattle |
| title | Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach |
| title_full | Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach |
| title_fullStr | Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach |
| title_full_unstemmed | Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach |
| title_short | Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach |
| title_sort | predictive modeling of energy consumption for cooling ventilation in livestock buildings a machine learning approach |
| topic | machine learning energy efficiency NeuralProphet energy load forecast cattle |
| url | https://www.mdpi.com/1996-1073/18/3/633 |
| work_keys_str_mv | AT carlosalejandroperezgarcia predictivemodelingofenergyconsumptionforcoolingventilationinlivestockbuildingsamachinelearningapproach AT patriziatassinari predictivemodelingofenergyconsumptionforcoolingventilationinlivestockbuildingsamachinelearningapproach AT danieletorreggiani predictivemodelingofenergyconsumptionforcoolingventilationinlivestockbuildingsamachinelearningapproach AT marcobovo predictivemodelingofenergyconsumptionforcoolingventilationinlivestockbuildingsamachinelearningapproach |