Abstract
The use of monitored data to improve the accuracy of building energy models
and operation of energy systems is ubiquitous, with topics such as building
monitoring and Digital Twinning attracting substantial research attention.
However, little attention has been paid to quantifying the value of the data
collected against its cost. This paper argues that without a principled method
for determining the value of data, its collection cannot be prioritised. It
demonstrates the use of Value of Information analysis (VoI), which is a
Bayesian Decision Analysis framework, to provide such a methodology for
quantifying the value of data collection in the context of building energy
modelling and analysis. Three energy decision-making examples are presented:
ventilation scheduling, heat pump maintenance scheduling, and ground source
heat pump design. These examples illustrate the use of VoI to support
decision-making on data collection.