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

Max Langtry, Chaoqun Zhuang, Rebecca Ward, Nikolas Makasis, Monika J. Kreitmair, Zack Xuereb Conti, Domenic Di Francesco, Ruchi Choudhary

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.

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

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.
Paper Structure (15 sections, 24 equations, 10 figures, 3 tables)

This paper contains 15 sections, 24 equations, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Assessment of data collection benefit for building energy systems
  4. Value of information analysis
  5. Research objectives & novel contributions
  6. Methodology
  7. Bayesian Decision Analysis
  8. Value of Information
  9. Influence diagrams
  10. Analysis extensions
  11. Example decision problems
  12. Condition Measurement for Optimising Maintenance Scheduling for Air-Source Heat Pumps
  13. Building Occupancy Measurement for Real-Time Ventilation Scheduling to Improve Indoor Air Quality
  14. Ground Conductivity Measurement for Optimising Borehole Design in Residential Ground-Source Heat Pump Heat Supply Systems
  15. Conclusions

Figures (10)

  • Figure 1: Decision tree representation of Pre-Posterior Decision Problem
  • Figure 2: Influence diagram representation of ASHP maintenance scheduling decision problem
  • Figure 3: Distribution of utilities achieved by optimal prior action, ${N_m}^*=2$. Dashed line indicates mean of distribution
  • Figure 4: Distribution of true optimal maintenance frequency, i.e. best action once uncertainty has been revealed
  • Figure 5: Influence diagram representation of building ventilation scheduling decision problem
  • ...and 5 more figures