Nowadays, efficient management of energy consumption is crucial for the sustainability of our cities, and overall of our planet. Approaches investigated so far, mostly adopt complex approaches, often based on deep learning, which have an important footprint. This study focuses on the importance of using simpler methods to predict energy consumption in smart buildings, emphasizing a methodological approach that prioritizes simplicity, transparency, and computational efficiency, especially when data is scarce. It emphasizes that even the prediction of energy consumption at the scale of a building, which is sometimes ignored due to computational complexity, is feasible and can make a big difference. By using simple analytical models combined with outlier detection, this research contributes to the field by showing how we can still gain valuable insights with limited data. Therefore, this study provides a practical and scalable way to improve energy efficiency and sustainability in buildings, which has a significant contribution to energy management practices.