A Bayesian Multiple Models Combination Method for Time Series Prediction
Journal of Intelligent and Robotic Systems
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Occupancy-driven energy management for smart building automation
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Building-level occupancy data to improve ARIMA-based electricity use forecasts
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Schedule-calibrated occupant behavior simulation
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Improving building energy efficiency with a network of sensing, learning and prediction agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Forecasting the occupancy of buildings can lead to significant improvement of smart heating and cooling systems. Using a sensor network of simple passive infrared motion sensors densely placed throughout a building, we perform data mining to forecast occupancy a short time (i.e., up to 60 minutes) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model then forecasts occupancy a various horizons into the future. We combine these forecasts using a modified Bayesian combined forecasting approach. The method is demonstrated on two large building occupancy datasets, and shows promising results for forecasting horizons of up to 60 minutes. Because the two datasets have such different occupancy profiles, we compare our algorithms on each dataset to evaluate the performance of the forecasting algorithm for the different conditions.