Abstract
The available energy monitoring information in the dairy industry reveals significant gaps regarding the impact of operational factors on performance indicators and the generation of typical days from load profiles. This paper presents a method for predicting performance indicators and load profiles in the dairy industry based on multiple regression and clustering. The method is applied to a cheese dairy plant located in Tuscany, Italy, providing actionable insights for energy efficiency and renewable integration. With regard to performance indicators, predictions using multiple regression achieved accuracies within 8 % for electricity consumption and within 20 % for steam generation, mainly due to limited data availability. The combination of k-means clustering with multiple regression yielded an overall accuracy within approximately 10 % for electricity load profiles, enabling the labelling of clusters based on production and meteorological variables. The analysis of improved production planning and the compatibility of energy demand with solar resources showed potential reductions in the electrical performance indicator by 27 % and self-consumption rates between 14 % and 42 %, respectively. Validation with data from other dairy contexts confirms the accuracy of the method and the considerable potential for improvement, suggesting further implementation towards effective energy management in the dairy industry.
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