Improving the accuracy of energy baseline models for commercial buildings with occupancy data. (1st October 2016)

• Record Type: Journal Article
• Authors: Liang, Xin; Hong, Tianzhen; Shen, Geoffrey Qiping
• Is Part Of: Applied energy. Volume 179(2016)
• Page Start: 247
• Page End: 260
• Publication Date: 2016-10-01
• Subjects: Baseline model -- Occupancy -- Building energy use -- Measurement and verification -- Energy efficiency retrofit; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.06.141
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We evaluated several baseline models predicting energy use in buildings. Including occupancy data improved accuracy of baseline model prediction. Occupancy is highly correlated with energy use in buildings. This simple approach can be used in decision makings of energy retrofit projects. Abstract: More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by "measurement and verification" (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the "baseline" of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.