Daily Electricity Consumption Forecasting: A Comparative Study of Neural Network and Radial Basis Function Models

Electricity consumption forecasting stands as a critical research domain within electrical engineering, with myriad of traditional forecasting models and artificial intelligence techniques undergoing rigorous examination. This paper is devoted to a comparison of three Machine Learning approaches to surrogate modelling and forecasting of the daily electricity consumption in Tirana, Albania: A Radial Basis Function (RBF) approach, a feed forward Neural Network approach and a Recurrent Neural Network approach. Through meticulous experimentation across four distinct scenarios encompassing variations in training/testing splits, historical data utilization, and hyper parameter optimization, we thoroughly evaluate the performance of each model. Comparative analysis is conducted based on model fit, computational efficiency, and error measurement metrics. Our findings highlight the remarkable performance of the RBF ARX approach, underscoring its effectiveness in accurately forecasting electricity consumption.