Smart Building Data Collection and Ventilation System Energy Prediction
Master thesis
Permanent lenke
https://hdl.handle.net/11250/3021857Utgivelsesdato
2022Metadata
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- Studentoppgaver (TN-IDE) [823]
Sammendrag
Data has the potential to transform our environments for the better if utilized to its fullpotential. A highly interesting use case of data is in relation to Smart Buildings, whereIoT technology presents new possibilities. With appropriate collection and structuringof the available data, many new opportunities present themselves.
In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. Toillustrate the potential in the data, one specific problem is researched, namely that ofindoor climate optimization and its effects on energy usage. The problem descriptionand the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model
predictive control and various machine learning methods to predict energy usage.For a one day simulation, the proposed optimization strategy yields a 174.86% increasein energy usage. The conducted work indicates that the proposed model identificationtechnique is unsuitable for the underlying data utilized in this work. The proposedmodel predictive control strategy and machine learning methods contain promising results.