This article represents a further step in the continuously developing process of improving the prediction capabilities using databases. Its aim is to compare and evaluate the operation, performance and validity of knowledge extraction techniques related to prediction. The innovative part of this study concerns the selection, enrichment and processing of the database used. In particular, the database contains consumption data for an entire city over the course of a year. These data were then enriched with elements concerning the determination of the time and the environmental conditions, in order to take into consideration the correlation of the data with these parameters. Subsequently, after being converted into an editable format, they were processed using techniques such as normalization and factor analysis, which finally led to the prediction process. At this stage, different methods, such as decision trees, deep learning and generalized linear models, were applied and thoroughly analyzed, and both their operation and their effectiveness were compared and evaluated. The present effort, therefore, intends to provide a useful tool that will contribute to future efforts to improve predictions from existing data.