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Development of an energy monitoring practice framework to profile energy use in residential buildings

Fitzgerald, Darryl Edward (2021) Development of an energy monitoring practice framework to profile energy use in residential buildings. Masters by Research thesis, Murdoch University.

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Energy monitoring is pivotal to undertaking energy consumption and efficiency studies in residential buildings. Residential building account for approximately 25% of final energy demand. However, many of the promised gains from energy monitoring have yet to be realised. Key to this issue is the lack of an energy monitoring practice framework that can provide accurate and repeatable long-term load profile data for use in energy management systems. The lack of such a framework was recently highlighted by ISO 50001:2018 through the inclusion of monitoring performance evaluation in the 2018 revision of the standard. In this research a practice framework was developed and validated based on an energy measurement, monitoring, and processing conceptual hierarchy. The energy practice framework emphasises the links between the measurement, monitoring and load profile data processing functions integral to an energy monitoring process. To validate the application of the practice framework a building electrical energy simulator and tester (BEEST) was designed and constructed so that load profile time-series data could be simultaneously collected, communicated, and stored by multiple commercial energy monitoring devices. The physically simulated load profile time-series data from multiple simulations and across multiple different energy monitoring devices was examined using extracted statistical, structural and frequency domain features to gauge load profile accuracy and repeatability. The load profiles extracted features were also tested for accuracy and repeatability through the application of cluster analysis. Research results showed variations in energy monitoring practice caused significant inaccuracy and low precision in monitored load profile features. In particular precision of extracted feature (e.g., frequency domain data) can vary more than ±100%. The research showed that mapping energy monitoring practice to a known framework provides a basis on which load profile data can be compared and profiled.

Item Type: Thesis (Masters by Research)
Murdoch Affiliation(s): Engineering and Energy
Supervisor(s): Urmee, Tania and Whale, Jonathan
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