Assessment of a methodology to evaluate constructive systems for industrialization: the case of dwellings in Spain.
DOI:
https://doi.org/10.20868/ade.2024.5304Keywords:
Fault detection and diagnosis; Thermal facilities; Machine learning; SCADA; R software.Abstract
Building monitoring systems deliver large volumes of information and advanced data analysis tools are available. A fault detection and diagnosis (FDD) problem in building energy systems can also be regarded as a pure machine learning problem. The aim of this work is to promote FDD with machine learning applications in building environment. As a contribution, in this work raw time data series, obtained from a SCADA, are processed for further pattern construction of a building thermal facility. The thermal facility supplies the DHW, and heating demands of a residential building, consisting of 26 social dwelling units and located at Durango (northern Spain). Data recorded every 24 hours in cumulative values is included in the R software for computing statistical graphs. For DHW and heating consumption meter values, 229 valid data points are obtained, and the daily consumption ranges are between 1.94 - 5.90 m3 and 0 - 547.63 kWh respectively.
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