02209nas a2200181   4500008004100000245009800041210006900139520155000208653001901758653002401777653003101801653003301832653001401865100001501879700001901894700002701913856008701940       2016                        eng d00aImproving the accuracy of energy baseline models for commercial buildings with occupancy data0 aImproving the accuracy of energy baseline models for commercial 3 a<p>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&amp;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&amp;V and facilitate investment strategies of energy efficiency retrofit.</p>10abaseline model10abuilding energy use10aEnergy Efficiency Retrofit10aMeasurement and verification10aoccupancy1 aLiang, Xin1 aHong, Tianzhen1 aShen, Geoffrey, Qiping  uhttps://simulationresearch.lbl.gov/publications/improving-accuracy-energy-baseline01835nas a2200169   4500008003900000245005800039210005700097520128500154653001601439653002101455653002501476653002201501100001501523700001901538700002701557856008101584       2016                          d00aOccupancy data analytics and prediction: a case study0 aOccupancy data analytics and prediction a case study3 a<p>Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.</p>10adata mining10aMachine learning10aoccupancy prediction10aoccupant presence1 aLiang, Xin1 aHong, Tianzhen1 aShen, Geoffrey, Qiping  uhttps://simulationresearch.lbl.gov/publications/occupancy-data-analytics-and