Rationalising data collection for supporting decision making in building energy systems using Value of Information analysis

TL;DR

This paper applies Value of Information analysis to quantify the economic benefits of data collection strategies in building energy systems, aiding decision-making and optimizing data-related expenditures.

Contribution

It introduces the use of Bayesian Decision Analysis (VoI) to evaluate the economic value of data collection in building energy management, which was previously unquantified.

Findings

Smart meters are economically beneficial for maintenance scheduling.

Occupancy monitoring supports ventilation decisions effectively.

Ground thermal tests aid in system design decisions.

Abstract

The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economised and prioritised, avoiding wastage.