Critique of Occupancy Schedules Learning Process Through a Data Mining Framework

AbstractIn this paper I will be reviewing and evaluating the work of Simona D'Oca and Tianzhen Hong of the Lawrence Berkeley National Laboratory, entitled _Occupancy Schedules Learning Process Through a Data Mining Framework_, published in the Journal of Energy and Buildings in February 2015. After a brief summary of the paper and its findings, I present the author's background and related previous work - to find that their results are hard to interpret quantitatively, but potentially can be useful for qualitative workspace improvement. Subsequenlty I conduct a brief overview of related work and possible improvements, followed by my personal reflections and suggested future work pathways. I finish with a brief conclusion.

Keywords: building occupancy, decision tree model, building energy consumption

Summary

n this paper I will be reviewing and evaluating the work of Simona D'Oca and Tianzhen Hong of the Lawrence Berkeley National Laboratory, entitled Occupancy Schedules Learning Process Through a Data Mining Framework (OSL), published in the Journal of Energy and Buildings in February 2015 (D’Oca 2015).

OSL is a simple, real-world application of the knowledge discovery and data mining process on building occupancy schedules. This is important from the energetic scheduling and subsequent consumption perspective. Buildings account for one quarter of the global energy consumption (World Energy Outlook...) so they represent a key component of the future smart grid with dynamic supply and demand patterns. Theere is also a an increasing number of buildings fitted with building management systems (BMS) (Open data communicati...), some already connected to the so-called "Internet of Things", but the benefits of these data streams are yet to be harvested. Spaces in buildings can be characterized from their energetic consumption perspective (heat, light, electricity) based on their occupants' presence profiles. OSL presents a simple methodology of determining these profiles using a decision tree model. A set of 16 offices with existing 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Fin