  Energy conservation in buildings is a paramount concern to combat greenhouse
gas emissions and combat climate change. The efficient management of room
occupancy, involving actions like lighting control and climate adjustment, is a
pivotal strategy to curtail energy consumption. In contexts where surveillance
technology isn't viable, non-intrusive sensors are employed to estimate room
occupancy. In this study, we present a predictive framework for room occupancy
that leverages a diverse set of machine learning models, with Random Forest
consistently achieving the highest predictive accuracy. Notably, this dataset
encompasses both temporal and spatial dimensions, revealing a wealth of
information. Intriguingly, our framework demonstrates robust performance even
in the absence of explicit temporal modeling. These findings underscore the
remarkable predictive power of traditional machine learning models. The success
can be attributed to the presence of feature redundancy, the simplicity of
linear spatial and temporal patterns, and the advantages of high-frequency data
sampling. While these results are compelling, it's essential to remain open to
the possibility that explicitly modeling the temporal dimension could unlock
deeper insights or further enhance predictive capabilities in specific
scenarios. In summary, our research not only validates the effectiveness of our
prediction framework for continuous and classification tasks but also
underscores the potential for improvements through the inclusion of temporal
aspects. The study highlights the promise of machine learning in shaping
energy-efficient practices and room occupancy management.
