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A framework for a multi-source, data-driven building energy management toolkit
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DOI：10.1016/j.enbuild.2021.111255.png)
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Abstract
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Poor building energy performance often arises from suboptimal operations such as inappropriate control sequences or hard faults in heating, ventilation, and air-conditioning (HVAC) systems. Furthermore, such deficiencies are often left unaddressed due to a lack of accessible analytical tools that can derive insights which identify energy-saving measures using multiple data resources. This paper presents a novel multi source, data-driven building energy management toolkit as a synthesis of established inverse energy modelling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods in the literature. The toolkit contains seven functions that input HVAC controls, energy meter, Wi-Fi-based occupancy, and work order log data to detect hard and soft faults, improve sequences of operation, and monitor energy flows, occupancy patterns, and occupant satisfaction. The toolkit's unique multifaceted analytical approach was demonstrated on a case study building as a proof of concept. Five faults pertaining to the air handling units' mode of operation and heating coil valves were identified and the generated insights were used to pinpoint operational deficiencies stemming from inappropriate zone temperature overheating thresholds and perimeter heating devices. The toolkit, along with the data from the case study, is open-source and accessible through GitHub (Markus, 2021) to initiate and facilitate future development. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Building energy management
Multi-source data analytics
Change point models
Inverse energy modelling
Clustering
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