Enhancing Electricity Consumption Forecasting with Artificial Intelligence on Small Datasets: A Comparative Study

Accurate electricity consumption forecasting is essential for efficient resource allocation and grid management, particularly with limited data. The present paper investigates how artificial intelligence techniques can enhance forecasting in small datasets and addresses data quality concerns in the educational sector. It reviews challenges in traditional forecasting, explores artificial intelligence methods such as support vector machines, autoregressive integrated moving average, and long short-term memory networks, and evaluates their performance using real-world data from four educational buildings belonging to the Technical University of Cluj-Napoca. By combining theoretical insights with empirical results, the present study advances artificial intelligence-driven forecasting for electricity consumption in small datasets, offering insights for future research and industry applications in energy management and policy formulation.