Modeling plug-in equipment load patterns in private office spaces

• Published in: Energy and buildings 2016-06, Vol.121, p.234-249
• Main Authors: Gunay, H. Burak, O’Brien, William, Beausoleil-Morrison, Ian, Gilani, Sara
• Format: Article
• Language: English
• Subjects: Building energy model; Electricity; Energy use; Evening; Mathematical models; Model forms; Motion sensors; Occupant behavior; Office equipment; Office spaces; Plug-in equipment loads; Random sampling
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2016.03.001

•Concurrent occupancy and plug load data were gathered in ten private office spaces.•A data-driven model form was proposed to predict plug-in equipment load patterns.•75% of the plug load in private offices takes place during unoccupied periods.•Plug load during unoccupied periods decreases with the duration of absence. The uncertainty of plug-in office equipment choices and usage patterns is a major challenge in making proper design and control decisions by using building energy models. In this paper, the factors contributing to the plug-in equipment load patterns were investigated through an office equipment survey conducted with 203 participants, and the concurrent motion sensor and plug load data gathered in ten private office spaces. Results indicate that over 75% of the plug-in equipment electricity use in private offices takes place during unoccupied periods; and the plug load during the unoccupied periods exhibits a relationship with the duration of absence following departures. Drawing on these findings, this paper puts forward a data-driven model form to predict plug-in equipment load patterns in office spaces. The model is built on the plug-in equipment load patterns during five different time periods: (a) occupancy, (b) intermediate breaks, (c) weekday evenings, (d) weekends, and (e) vacations. The model inputs the predictions of an occupancy model and employs random sampling over the learned plug load patterns to generate plug load forecasts. The data gathered from the ten private offices were used to assess the accuracy and appropriateness of the model form. It was found that the models can accurately generate plug-in equipment load forecasts.