02384nas a2200253 4500008003900000245010000039210006900139260001200208300001200220490000700232520158600239653002401825653001501849653002201864653002201886653002101908653002501929653002401954100001401978700001201992700001902004700001702023856009002040 2015 d00aData Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China0 aData Analysis and Stochastic Modeling of Lighting Energy Use in  c01/2015 a275-2870 v863 a

Lighting consumes about 20% to 40% of the total electricity use in large office buildings in China. Commonly in building simulations, static time schedules for typical weekdays, weekends and holidays are assumed to represent the dynamics of lighting energy use in buildings. This approach does not address the stochastic nature of lighting energy use, which can be influenced by occupant behavior in buildings. This study analyzes the main characteristics of lighting energy use over various timescales, based on the statistical analysis of measured lighting energy use data from 15 large office buildings in Beijing and Hong Kong. It was found that in these large office buildings, the 24-hourly variation in lighting energy use was mainly driven by the schedules of the building occupants. Outdoor illuminance levels had little impact on lighting energy use due to the lack of automatic daylighting controls (an effective retrofit measure to reduce lighting energy use) and the relatively small perimeter area exposed to natural daylight. A stochastic lighting energy use model for large office buildings was further developed to represent diverse occupant activities, at six different time periods throughout a day, and also the annual distribution of lighting power across these periods. The model was verified using measured lighting energy use from the 15 buildings. The developed stochastic lighting model can generate more accurate lighting schedules for use in building energy simulations, improving the simulation accuracy of lighting energy use in real buildings.

10abuilding simulation10aenergy use10aLighting modeling10aoccupant behavior10aoffice buildings10aPoisson distribution10astochastic modeling1 aZhou, Xin1 aYan, Da1 aHong, Tianzhen1 aRen, Xiaoxin uhttps://simulationresearch.lbl.gov/publications/data-analysis-and-stochastic-modeling02285nas a2200241 4500008003900000245007400039210006900113260001200182300000900194490000700203520156400210653002301774653002401797653001501821653001601836653001801852653002201870653001801892100001701910700001201927700001901939856008501958 2015 d00aData Mining of Space Heating System Performance in Affordable Housing0 aData Mining of Space Heating System Performance in Affordable Ho c07/2015 a1-130 v893 a

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

10aaffordable housing10abuilding simulation10aclustering10adata mining10adecision tree10aoccupant behavior10aspace heating1 aRen, Xiaoxin1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/data-mining-space-heating-system02003nas a2200217 4500008003900000245008000039210006900119520131400188653002401502653001501526653001301541653001301554653002201567653002101589653002501610100001401635700001201649700001701661700001901678856008801697 2013 d00aData Analysis and Modeling of Lighting Energy Use in Large Office Buildings0 aData Analysis and Modeling of Lighting Energy Use in Large Offic3 a

Lighting consumes about 20 to 40% of total electricity use in large office buildings in the U.S. and China. In order to develop better lighting simulation models it is crucial to understand the characteristics of lighting energy use. This paper analyzes the main characteristics of lighting energy use over various time scales, based on the statistical analysis of measured lighting energy use of 17 large office buildings in Beijing and Hong Kong. It was found that the daily 24-hour variations of lighting energy use were mainly driven by the schedule of the building occupants. Outdoor illumination levels have little impact on lighting energy use in large office buildings due to the lack of automatic daylighting controls and relatively small perimeter areas. A stochastic lighting energy use model was developed based on different occupant activities during six time periods throughout a day, and the annual distribution of lighting power across those periods. The model was verified using measured lighting energy use of one selected building. This study demonstrates how statistical analysis and stochastic modeling can be applied to lighting energy use. The developed lighting model can be adopted by building energy modeling programs to improve the simulation accuracy of lighting energy use.

10abuilding simulation10aenergy use10alighting10amodeling10aoccupant behavior10aoffice buildings10aPoisson distribution1 aZhou, Xin1 aYan, Da1 aRen, Xiaoxin1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/data-analysis-and-modeling-lighting02500nas a2200253 4500008003900000022003900039245010600078210006900184260003900253300001200292490000600304520167200310653003701982653002702019653001502046653000902061653001302070653001502083100001602098700001902114700001202133700001702145856008402162 2013 d aPrint: 1996-3599; Online 1996-874400aA Detailed Loads Comparison of Three Building Energy Modeling Programs: EnergyPlus, DeST and DOE-2.1E0 aDetailed Loads Comparison of Three Building Energy Modeling Prog bTsinghua University Pressc09/2013 a323-3350 v63 a

Building energy simulation is widely used to help design energy efficient building envelopes and HVAC systems, develop and demonstrate compliance of building energy codes, and implement building energy rating programs. However, large discrepancies exist between simulation results from different building energy modeling programs (BEMPs). This leads many users and stakeholders to lack confidence in the results from BEMPs and building simulation methods. This paper compared the building thermal load modeling capabilities and simulation results of three BEMPs: EnergyPlus, DeST and DOE-2.1E. Test cases, based upon the ASHRAE Standard 140 tests, were designed to isolate and evaluate the key influencing factors responsible for the discrepancies in results between EnergyPlus and DeST. This included the load algorithms and some of the default input parameters. It was concluded that there is little difference between the results from EnergyPlus and DeST if the input values are the same or equivalent despite there being many discrepancies between the heat balance algorithms. DOE-2.1E can produce large errors for cases when adjacent zones have very different conditions, or if a zone is conditioned part-time while adjacent zones are unconditioned. This was due to the lack of a strict zonal heat balance routine in DOE-2.1E, and the steady state handling of heat flow through interior walls and partitions. This comparison study did not produce another test suite, but rather a methodology to design tests that can be used to identify and isolate key influencing factors that drive the building thermal loads, and a process with which to carry them out.

10abuilding energy modeling program10abuilding thermal loads10acomparison10adest10aDOE-2.1E10aenergyplus1 aZhu, Dandan1 aHong, Tianzhen1 aYan, Da1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/detailed-loads-comparison-three