{\rtf1\ansi\deff0\deftab360

{\fonttbl
{\f0\fswiss\fcharset0 Arial}
{\f1\froman\fcharset0 Times New Roman}
{\f2\fswiss\fcharset0 Verdana}
{\f3\froman\fcharset2 Symbol}
}

{\colortbl;
\red0\green0\blue0;
}

{\info
{\author Biblio 7.x}{\operator }{\title Biblio RTF Export}}

\f1\fs24
\paperw11907\paperh16839
\pgncont\pgndec\pgnstarts1\pgnrestart
Wang, Zhe, Tianzhen  Hong, and Mary Ann Piette. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach." Applied Energy 240 (2019) 386 - 398.\par \par Wang, Zhe, Tianzhen  Hong, Mary Ann Piette, and Marco  Pritoni. "Inferring occupant counts from Wi-Fi data in buildings through machine learning." Building and Environment 158 (2019) 281 - 294.\par \par Wang, Zhe, Tianzhen  Hong, and Mary Ann Piette. "Predicting plug loads with occupant count data through a deep learning approach." Energy 181 (2019) 29 - 42.\par \par Wang, Zhe, Thomas  Parkinson, Peixian  Li, Borong  Lin, and Tianzhen  Hong. "The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes." Building and Environment 151 (2019) 219 - 227.\par \par Wang, Zhe, Tianzhen  Hong, and Ruoxi  Jia. "Buildings.Occupants: a Modelica package for modelling occupant behaviour in buildings." Journal of Building Performance Simulation (2018) 1 - 12.\par \par }