Using Google Trends as a proxy for occupant behavior to predict building

• Main Authors: Fu, Chun, Miller, Clayton
• Other Authors: THE BUILT ENVIRONMENT
• Format: Article
• Language: English
• Published: ELSEVIER SCI LTD 2022
• Subjects: Science & Technology; Technology; Energy & Fuels; Engineering, Chemical; Engineering; Google Trends; Machine learning; Kaggle competition; Model error reduction; Building energy prediction; Energy model; ENERGY-CONSUMPTION; COOLING LOAD; SIMULATION; MACHINE
• Online Access: https://scholarbank.nus.edu.sg/handle/10635/229410
• Institution: National University of Singapore

10.1016/j.apenergy.2021.118343