PROMT: predicting occupancy presence in multiple resolution with time-shift agnostic classification

Improving methods for predicting occupant presence in commercial buildings is crucial for optimizing energy consumption. Also it is crucial for providing amiable indoor environmental conditions. To enable these improvements, we require a more accurate and flexible framework for predicting occupancy. The promt framework proposed in this paper is an accurate and flexible framework for predicting occupancy presence in multiple resolution with time-shift agnostic classification. promt assumes that no single prediction algorithm, model, or static model parameter can guarantee high fidelity occupancy prediction for varying occupancy requirements and for every kind of rooms. Given this assumption, the promt framework facilitates the deployment of several prediction algorithms and it performs an hyper-parameter optimization procedure on all deployed algorithms to obtain the optimal model for obtaining occupancy prediction in covered room. promt was benchmarked with datasets from two building cases by comparing the F-score of the prediction results obtained from all deployed algorithms. The results document that promt outperforms the performance of any single prediction algorithm by a maximum difference in F-score of 2.3% and a minimum difference in F-score of 0.58%. As a case study we demonstrate the use of promt for scheduling demand response events in a commercial building.