Artificial neural networks for energy analysis of office buildings with daylighting

An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters - four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash-Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%.Energy analysis Artificial neural networks Daylighting Office buildings Optimisation

Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong

Past and future trend of electricity use for air conditioning in the entire commercial sector in subtropical climates using 1979-2008 measured meteorological data as well as predictions for 2009-2100 from a general circulation model (MIROC3.2-H) was investigated. Air conditioning consumption showed an increasing trend over the past 30 years from 1979 to 2008. Principal component analysis (PCA) of measured and predicted monthly mean dry-bulb temperature, wet-bulb temperature and global solar radiation was conducted to determine a new climatic index Z for 1979-2008 and future 92 years (2009-2100) based on two emissions scenarios B1 and A1B (low and medium forcing). Through regression analysis, electricity use in air conditioning for the 92-year period was estimated. For low forcing, average consumption in 2009-2038, 2039-2068 and 2069-2100 would be, respectively, 5.7%, 12.8% and 18.4% more than the 1979-2008 average, with a mean 12.5% increase for the entire 92-year period. Medium forcing showed a similar increasing trend, but 1-4% more. Standard deviations of the monthly air conditioning consumption were found to be smaller suggesting possible reduction in seasonal variations in future years.Commercial sector Electricity use Air conditioning Global warming

Long-term trends of heat stress and energy use implications in subtropical climates

Past and future trends of human comfort in terms of heat and cold stresses under the local subtropical climates using measured meteorological data as well as predictions from general climate models were investigated. Summer discomfort showed an increasing trend (and winter discomfort a decreasing trend) over the past 41 years from 1968 to 2008. Monthly mean minimum and maximum temperatures and moisture content predictions from a general climate model (MIROC3.2-H) were used to determine summer and winter discomfort for future years (2009-2100) based on two emissions scenarios B1 and A1B (low and medium forcing). The 92-year (2009-2100) mean cold stress would be reduced from the 41-year (1968-2008) mean value of 8.7 to about three for both emissions scenarios. The 92-year mean heat stress would be 115.9 and 120.6 for B1 and A1B, respectively, representing 31.6% and 36.9% increase over the 1968-2008 long-term average of 88.1. These suggest that the already small winter heating requirement in subtropical Hong Kong would become even more insignificant in future years, whereas the increasing trend of summer discomfort would result in more cooling demand in the built environment.Comfort index Climate change Building energy Subtropical Hong Kong

An analysis of energy-efficient light fittings and lighting controls

Electric lighting is one of the major energy consuming items in many non-domestic buildings. Using appropriate energy-efficient light fittings with dimming controls and proper daylighting schemes can help reduce the electrical demand and contribute to visual comfort and green building development. This paper presents a study on the energy and lighting performances for energy-efficient fluorescent lamps associated with electronic ballasts and high frequency photoelectric dimming controls installed in a school building. Electricity expenditures and indoor illuminance levels for a workshop and a classroom employing high frequency dimming controls were analyzed. Simple prediction methods were used to illustrate the lighting savings. The findings provide the operational and performance information, which would be applicable to other spaces with similar building layouts and lighting schemes.Energy-efficient lamps Electronic ballasts Daylighting High frequency dimming controls On-off controls