Predictive Modeling of Refrigeration Load Variations in Wind-Powered Systems Using Supervised Machine Learning Techniques

Abstract

Refrigeration systems powered by variable renewable energy sources face significant operational challenges due to load fluctuations, energy storage limitations, and grid dependency issues. Wind-powered refrigeration systems, in particular, require sophisticated predictive models to anticipate load variations and optimize performance under stochastic energy input conditions. This paper presents a novel framework for predictive modeling of refrigeration load variations in wind-powered systems using advanced supervised machine learning techniques. The framework incorporates multivariate time series analysis with recurrent neural networks and ensemble methods to forecast refrigeration loads across multiple time horizons. Our approach integrates meteorological data, system operational parameters, and thermodynamic variables to create a comprehensive model with uncertainty quantification. Experimental validation conducted over a 12-month period demonstrates the model's efficacy in predicting load variations with 92.7\% accuracy for short-term forecasts (1-4 hours) and 86.3\% accuracy for medium-term forecasts (24-48 hours). The proposed model significantly outperforms traditional statistical methods, reducing mean absolute percentage error by 34.2\% and improving computational efficiency by 27.9\%. This predictive framework enables proactive control strategies, enhances energy utilization efficiency, and reduces dependency on backup systems, representing a substantial advancement in the optimization of renewable energy-powered refrigeration technologies.