27 lines
1.5 KiB
TeX
27 lines
1.5 KiB
TeX
\section{Introduction}
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Buildings are a major consumer of energy, with more than 25\% of the total
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energy consumed in the EU coming from residential
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buildings~\cite{tsemekiditzeiranakiAnalysisEUResidential2019}. Combined with a
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steady increase in energy demand and stricter requirements on energy
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efficiency~\cite{europeancommission.jointresearchcentre.EnergyConsumptionEnergy2018},
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this amplifies the need for more accessible means of regulating energy usage of
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new and existing buildings.
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Data-driven methods of building identification and control prove very useful
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through their ease of implementation, foregoing the need of more complex
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physics-based models. On the flip side, additional attention is required to the
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design of these control schemes, as the results could vary greatly from one
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implementation to another.
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Gaussian Processes have been previously used to model building dynamics, but
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they are usually limited by a fixed computational budget. This limits the
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approaches that can be taken for identification and update of said models.
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Learning \acrshort{gp} models have also been previously used in the context of
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autonomous racing cars, but there the Sparse \acrshort{gp} model was built on
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top of a white-box model and only responsible for fitting the unmodeled
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dynamics.
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This project means to provide a further expansion of the use of black-box
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\acrlong{gp} Models in the context of building control, through online learning
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of building dynamics at new operating points as more data gets collected.
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