This study delves into enhancing energy consumption forecasting in smart buildings by analyzing the implications of different contextual detection strategies, employing both K-Means and Fuzzy C-Means clustering techniques. Utilizing a comprehensive dataset from various sensors, the research segments power usage data to uncover distinct consumption patterns, extracted from user’s behavior. A suite of seven machine learning forecasting algorithms undergoes evaluation, guided by two distinct methodologies: crafting specialized models for individual clusters and embedding contextual data as features within an integrated model. To perform the selection of the most suitable algorithm for each approach and cluster, an automated machine learning process is utilized, streamlining the optimization and comparison process. Moreover, the study explores the influence of soft clustering in providing nuanced, probabilistic cluster memberships and their impact on forecasting accuracy. A baseline scenario, devoid of contextual considerations, serves as a benchmark for comparison, allowing for a clear evaluation of the benefits introduced by integrating context into predictive models. The research aims to identify the optimal contextual detection strategy, considering the unique advantages of soft and hard clustering, to significantly improve energy consumption forecasts.