A BAYESIAN NETWORK MODEL FOR PREDICTING THE COOLING LOAD OF EDUCATIONAL FACILITIES
In the U.S., educational facilities consume a large
amount of energy. Model predictive control schemes can
improve the energy efficiency of educational facilities.
Accurate and fast prediction of the cooling load is
essential to performances of model predictive control
schemes. Although many methods for the cooling load
prediction were proposed, they are not suitable for
educational facilities due to the lack of an efficient way
to reflect the impact of internal activities on the cooling
load. After analyzing the characteristics of cooling load
of educational facilities, we proposed to use the day type
instead of the day of the week as the input for the
prediction. Then we constructed a Bayesian Network
model based on that. To evaluate how the proposed
inputs enhance the cooling load prediction, we also
implemented the other Bayesian Network model with
inputs recommended by the literature. To assess
performances of those models, we performed a case
study in which on-site measured cooling load and
meteorological data was used for the training and testing.
The results show that the Bayesian Network models can
capture the trend of cooling load even with a limited size
of training data. Replacing the day of the week by the
day type can significantly improve the accuracy of
cooling load prediction for educational facilities.
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