Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
SCOPES: Smart Cameras Object Position Estimation System
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
Energy efficient building environment control strategies using real-time occupancy measurements
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Evaluation of energy-efficiency in lighting systems using sensor networks
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
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
A limited-data model of building energy consumption
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Adaptation of a mixture of multivariate Bernoulli distributions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Non-Intrusive Occupancy Monitoring using Smart Meters
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Watts in the basket?: Energy Analysis of a Retail Chain
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Understanding building energy consumption has become important due to stricter energy regulations, increasing energy costs and also as buildings have long term impact on energy consumption. In order to recommend retrofits, it is important to have accurate estimates for building energy consumption which is affected significantly by occupancy patterns. This paper explores the development of static occupancy models using a model adaptation technique that is able to capture accurately features of occupancy distributions typically found using a large amount of training data (days, weeks, months). Using only one day of training data that can be easily recorded without any infrastructure but battery-operated sensors with on-board memory, we show that our adapted occupancy model can estimate energy savings of 10.9%; and the room temperatures for the adapted model schedules were 0.5°F and 1.4°F off from the target temperatures for summer and winter months, respectively. This performance was on par with models trained with four times as much data. Our proposed technique can be used by energy auditors to estimate energy savings for existing buildings and by building energy managers to optimize static schedules which assume maximum occupancy.