A study on the transferability of computational models of building electricity load patterns across climatic zones

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A study on the transferability of computational models of building electricity load patterns across climatic zones
Title:
A study on the transferability of computational models of building electricity load patterns across climatic zones
Other Titles:
Energy and Buildings
Publication Date:
16 February 2021
Citation:
Ward, R., Wong, C. S. Y., Chong, A., Choudhary, R., & Ramasamy, S. (2021). A study on the transferability of computational models of building electricity load patterns across climatic zones. Energy and Buildings, 237, 110826. doi:10.1016/j.enbuild.2021.110826
Abstract:
Significant reduction in energy demand from non-domestic buildings is required if greenhouse emission reduction targets are to be met worldwide. Increasing monitoring of electricity consumption generates a real opportunity for gaining an in-depth understanding of the nature of occupant-related internal loads and the connection between activity and demand. The stochastic nature of the demand is well-known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. This paper presents evidence that it is feasible to generate stochastic models of activity-related electricity demand based on monitored data. Two machine learning approaches are used to develop stochastic models of plug loads; an autoencoder (AE) and a Functional Data Analysis (FDA) model. Using data from two office buildings located in different countries, the transferability of models is explored by training the models on data from one building and using the trained models to predict demand for the other building. The results show that both models predict plug loads satisfactorily, with a good agreement with the mean demand and quantification of the uncertainty.
License type:
Publisher Copyright
Funding Info:
This work was supported by the Lloyd’s Register Foundation and The Alan Turing Institute Data-Centric Engineering Programme, together with the EPSRC Strategic Priorities Fund - AI for Science, Engineering, Health and Government, under grant EP/T001569/1.
Description:
ISSN:
0378-7788
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