Abstract
<jats:p>In this study, we compare the effectiveness of fuzzy logic, neural networks, and decision trees foroptimizing energy consumption in smart grids. We evaluate these techniques based on their forecastingaccuracy, load balancing efficiency, computational time, and flexibility. Our results reveal that neuralnetworks exhibit the highest forecasting accuracy at 92.4% and superior load balancing efficiency of82.1%, though they require significant computational time (25 minutes). Fuzzy logic provides a balancedperformance with a forecasting accuracy of 85.2%, a load balancing efficiency of 78.5%, and a moderatecomputational time of 12 minutes. It also scores highly in flexibility, demonstrating strong adaptabilityto changing conditions. Decision trees, while the most computationally efficient with a processing timeof 8 minutes, show the lowest forecasting accuracy (80.1%) and load balancing efficiency (74.6%),indicating limitations in handling complex energy management tasks. This comparison highlights thestrengths and trade-offs of each technique, offering insights into their suitability for real-time energyoptimization in smart grids.</jats:p>