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. 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 (R2) 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.