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
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https://doi.org/10.3384/ecp2016973Published in: Proceedings of the American Modelica Conference 2020, Boulder, Colorado, USA, March 23-25, 2020
Linköping Electronic Conference Proceedings 131:8, p. 73-80
Published: 2020-11-03
ISBN: 978-91-7929-900-2
ISSN: 1650-3686 (print), 1650-3740 (online)
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).
Occupant behavior modeling, occupant
presence prediction, lighting power prediction,
regression model, stochastic simulation
