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Data analysis on building load profiles: A stepping stone to future campus
For the sustainable development of smart cities across the globe, energy efficiency is becoming a major factor to the maintenance and planning of buildings. In order to demonstrate the potential of data driven approaches in understanding building energy usage, we conduct a data analysis study based on a 10-year data set from 361 buildings of a university campus that are equipped with 1951 smart meters. The preliminary results obtained from our analysis is presented. Results of both clustering analysis and prediction analysis offer a better understanding of common building energy usage as well as a better identification of anomalous behaviors in the usage patterns.
Data analysis on building load profiles: A stepping stone to future campus
For the sustainable development of smart cities across the globe, energy efficiency is becoming a major factor to the maintenance and planning of buildings. In order to demonstrate the potential of data driven approaches in understanding building energy usage, we conduct a data analysis study based on a 10-year data set from 361 buildings of a university campus that are equipped with 1951 smart meters. The preliminary results obtained from our analysis is presented. Results of both clustering analysis and prediction analysis offer a better understanding of common building energy usage as well as a better identification of anomalous behaviors in the usage patterns.
Data analysis on building load profiles: A stepping stone to future campus
Wang, Long (Autor:in) / Ding, Yong (Autor:in) / Riedel, Till (Autor:in) / Miclaus, Andrei (Autor:in) / Beigl, Michael (Autor:in)
01.09.2017
408132 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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