02483nas a2200241 4500008004100000245008900041210006900130260001200199300001400211490000700225520173000232653002201962653002501984653002402009653001502033653002202048653001802070100001702088700001502105700001902120700001402139856008802153 2017 eng d00aSimulation and visualization of energy-related occupant behavior in office buildings0 aSimulation and visualization of energyrelated occupant behavior  c03/2017 a785–7980 v103 a

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.

10aBehavior Modeling10abuilding performance10abuilding simulation10aenergyplus10aoccupant behavior10avisualization1 aChen, Yixing1 aLiang, Xin1 aHong, Tianzhen1 aLuo, Xuan uhttps://simulationresearch.lbl.gov/publications/simulation-and-visualization-energy02209nas 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

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.

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

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.

10adata mining10aMachine learning10aoccupancy prediction10aoccupant presence1 aLiang, Xin1 aHong, Tianzhen1 aShen, Geoffrey, Qiping uhttps://simulationresearch.lbl.gov/publications/occupancy-data-analytics-and