Conference article

Data-driven Prediction of Occupant Presence and Lighting Power: A Case Study for Small Commercial Buildings

Jing Wang
Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, USA

Wangda Zuo
Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, USA

Sen Huang
Pacific Northwest National Laboratory, USA

Draguna Vrabie
Pacific Northwest National Laboratory, USA

Download articlehttps://doi.org/10.3384/ecp2016973

Published in: Proceedings of the American Modelica Conference 2020, Boulder, Colorado, USA, March 23-25, 2020

Linköping Electronic Conference Proceedings 169:8, s. 73-80

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Published: 2020-11-03

ISBN: 978-91-7929-900-2

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

Commonly used deterministic methods are unable to capture the randomness in occupant behavior and its impact on electric power consumption. In this paper, we propose a new data-driven model to capture occupant behavior in a stochastic manner. Unlike existing models and prediction tools, this new model does not require occupant presence data and can learn occupants’ arrival and departure time based on lighting power consumption data, which is more readily available than occupant presence data. We applied this occupant behavior model to lighting power consumption prediction and implemented the entire prediction process in Modelica. We then validated the Modelica model by comparing the predicted daily, weekly and monthly peak lighting power with measurements from two small commercial buildings. The results suggest that the prediction matches the measurement within acceptable deviations of 7%. The results also indicate that the proposed stochastic model performs better for long-term prediction of lighting power (monthly and weekly) than the short-term (daily).

Keywords

Occupant behavior modeling, occupant presence prediction, lighting power prediction, regression model, stochastic simulation

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