A systematic procedure to study the influence of occupant behavior on building energy consumption

By Z. Yu, Benjamin C. M. Fung, F. Haghighat, H. Yoshino, E. Morofsky. 2011

586 citations recorded by the source corpus.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Occupancy schedules learning process through a data mining framework (2015, methodology): “The results showed that the analysis methodology was powerful in providing insights into energy patterns related to the occupant behavior, facilitating evaluation of building saving potential by improving users’ energy profiles as well as driving building energy policy formulation [23-26].”
A review of machine learning in building load prediction (2021, unknown): “Clustering techniques are also used for recognizing the pattern of building energy use [64, 164-167], outlier detection [37, 168], identification of occupancy behavior [169] and classification of lighting system [170].”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “Occupancy related data Occupancy related data, such as number of occupants and types of occupant activities, would affect internal gain and then influence the pattern of energy usage [35], [36].”
Are you an energy saver at home? The personality insights of household energy conservation behaviors based on theory of planned behavior (2021, background): “In addition, several researches added sustainable cooking behavior into this framework (Hager and Morawicki, 2013; Yu et al., 2011).”