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Abstract: In subtropical Hong Kong, buildings consume the most of the electricity use. Computer simulation technique is a useful tool for predicting building thermal and energy performance. Simulation computer programs require weather data input to drive the mathematical models within the simulation tools for comparative studies and annual energy estimation. Typical weather year data representing the long-term climatic conditions are often used. Recently, there has been an increasing interest in incorporating daylight in the architectural and building designs to reduce building energy consumption. However, daylight parameters are not always available. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering probably the whole spectrum of the usual skies in the world. Once the skies have been identified, the basic solar irradiance and daylight illuminance at various inclined planes can be obtained. This paper presents the work on the development and evaluation of two typical weather datasets, namely Typical Meteorological Year (TMY) and Test Reference Year (TRY). Measured hourly weather data for the 30-year period from 1979 to 2008 were analysed. Building energy simulations were conducted to evaluate the two weather datasets. Daylight parameters were derived using luminous efficacy approach and the sky conditions were categorized based on common climatic variables. [Copyright &y& Elsevier]

Abstract: Principal component analysis of prevailing weather conditions in subtropical Hong Kong was conducted, and a new climatic index Z (as a function of the dry-bulb temperature, wet-bulb temperature and global solar radiation) determined for past (1979–2008, measurements made at local meteorological station) and future (2009–2100, predictions from general circulation models) years. Multi-year (1979–2008) building energy simulations were carried out for a generic office building. It was found that Z exhibited monthly and seasonal variations similar to the simulated cooling/heating loads and building energy use. Regression models were developed to correlate the simulated monthly building cooling loads and total energy use with the corresponding Z. Error analysis indicated that annual building energy use from the regression models were very close to the simulated values; the difference was about 1%. Difference in individual monthly cooling load and energy use, however, could be up to 4%. It was also found that both the DOE-simulated results during 1979–2008 and the regression-predicted data during 2009–2100 indicated an increasing trend in annual cooling load and energy use and a gradual reduction in the already insignificant heating requirement in cooling-dominated office buildings in subtropical climates. [Copyright &y& Elsevier]

Abstract: 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%. [Copyright &y& Elsevier]

Abstract: The objective was to investigate whether energy use in buildings from simulation could be correlated with a new composite climatic index, which would account for the influences of major climatic variables such as temperature, humidity and solar radiation. Principal component analysis of prevailing weather conditions in sub-tropical Hong Kong was conducted, and a new climatic index Z determined. Multi-year (1979–2007) building energy simulations were carried out for a generic office building. It was found that Z exhibited daily and seasonal variations similar to the simulated cooling load and building energy use. Regression models were developed to correlate the simulated daily cooling load and building energy use with the corresponding daily Z. Error analysis indicated that annual building energy use from the regression models were very close to the simulated values; the difference was less than 2%. Difference in individual daily energy use, however, could be up to 6%. It was also found that both the simulated annual building energy use and the new climatic variable Z appeared to show an increasing trend during the 29-year period, indicating a subtle, but gradual change of climatic conditions that might affect energy use in buildings in future years. [Copyright &y& Elsevier]

Abstract: Two-parameter regression models were used to predict global solar radiation (GSR) for the nine major thermal zones and sub-zones – severe cold (I, II and III), cold (I, II and III), hot summer and cold winter, mild, and hot summer and warm winter – across China. The impact of using modelled GSR on building energy simulation was investigated. A total of nine cities were selected, one for each thermal zone. Generic office buildings were developed for each of the nine cities according the local design/energy codes. Two sets of energy simulation were conducted for each city, one with the measured GSR and the other the modelled data. The computed results were analysed in four aspects – solar heat through the windows, building heating loads, building cooling loads and the corresponding building energy consumption. It was found that simulated annual heating/cooling loads could differ by 0.1–6.2% and total building energy consumption 0.1–1.2%. These differences were also compared with the mean bias error and root-mean-squared error of the modelled GSR. [Copyright &y& Elsevier]

Abstract: This short technical note presents an attempt to develop a new climatic variable Z for assessing climate change (global warming) with respect to the built environment. Principal component analysis of long-term (30-year) dry-bulb temperature (DBT), wet-bulb temperature and global solar radiation data was conducted in the 5 major climate zones in China. It was found that while the long-term trend was increasing for the DBT in all 5 major climates, hot summer and warm winter climate zone showed a decreasing trend (though slightly) in the annual and seasonal average Z values. This suggests that climate change might not necessarily result in higher cooling loads in buildings. [Copyright &y& Elsevier]

Abstract: Correlation between clearness index and sunshine duration is useful to the estimation of the solar radiation for areas where measured solar radiation data are not available. Regression techniques were used to investigate the correlations between daily global solar radiation and sunshine duration for different climates in China. Measurements made during the 30-year period (1971–2000) from 40 measuring stations covering major thermal and solar climatic zones across China have been gathered and analysed. The correlations were developed for each individual station as well as for each of the major climates. It was found that the Angstrom–Prescott equation tended to give a good estimation of global solar radiation based on the corresponding measured sunshine hours. A simple two-parameter linear regression equation was proposed for each of the major thermal and solar climatic zones. [Copyright &y& Elsevier]

Abstract: The correlation between the clearness index and sunshine duration is useful in the estimation of the solar radiation for areas where measured solar radiation data are unavailable. Regression techniques and artificial neural networks were used to investigate the correlations between daily global solar radiation (GSR) and sunshine duration for different climates in China. Measurements made during the 30-year period (1971–2000) from 41 measuring stations covering 9 thermal and 7 solar climate zones and sub-zones across China were gathered and analysed. The performance of the regression and the ANN models in the thermal and solar zones was analysed and compared. The coefficient of determination (R 2), Nash–Sutcliffe efficiency coefficient (NSEC), mean bias error (MBE) and root-mean-square error (RMSE) were determined. It was found that the regression models in both the thermal and the solar climate zones showed a strong correlation between the clearness index and sunshine duration (R 2=0.79–88). There appeared to be an increasing trend of larger MBE and RMSE from colder climates in the north to warmer climates in the south. In terms of the thermal and solar climate zone models, there was very little to choose between the two models. [Copyright &y& Elsevier]

Abstract: Artificial neural networks (ANNs) were used to develop prediction models for daily global solar radiation using measured sunshine duration for 40 cities covering nine major thermal climatic zones and sub-zones in China. Coefficients of determination (R 2) for all the 40 cities and nine climatic zones/sub-zones are 0.82 or higher, indicating reasonably strong correlation between daily solar radiation and the corresponding sunshine hours. Mean bias error (MBE) varies from −3.3MJ/m2 in Ruoqiang (cold climates) to 2.19MJ/m2 in Anyang (cold climates). Root mean square error (RMSE) ranges from 1.4MJ/m2 in Altay (severe cold climates) to 4.01MJ/m2 in Ruoqiang. The three principal statistics (i.e., R 2, MBE and RMSE) of the climatic zone/sub-zone ANN models are very close to the corresponding zone/sub-zone averages of the individual city ANN models, suggesting that climatic zone ANN models could be used to estimate global solar radiation for locations within the respective zones/sub-zones where only measured sunshine duration data are available. [Copyright &y& Elsevier]

Abstract: Recently, the International Commission on Illumination (CIE) has adopted a range of 15 standard sky distributions representing the whole probable spectrum of actual skies in the world. Each sky standard has its own well-defined sky luminance pattern which can be conveniently used to calculate the sky radiance and luminance for a given sky patch and the solar irradiance and daylight illuminance on inclined surfaces facing various orientations. The crucial issues are whether the skies could be correctly identified. This paper presents the work on the classification of the CIE Standard General Skies using various climatic parameters and indices. Meteorological variables namely luminance distribution for the whole sky including zenith luminance, global, direct-beam and sky-diffuse illuminance on a horizontal surface and vertical sky illuminance, and horizontal and vertical solar irradiance data are adopted for analysis. The results demonstrate that there are a number of appropriate climatic parameters for sky classification and the selection depends on their availability, accuracy and sensitivity. The approaches could contribute to the estimation of solar irradiance and daylight illuminance which are essential to the renewable and sustainable developments and energy-efficient building designs. [Copyright &y& Elsevier]

Abstract: Solar irradiance and outdoor illuminance, particularly on vertical surfaces are crucial to energy-efficient building designs and daylighting schemes. In Hong Kong, only hourly horizontal global solar radiation data have been systematically recorded for a long period but no measurements of daylight illuminance exist. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering the whole probable spectrum of skies in the world. Standard skies of the same category would have the identical well-defined sky radiance and luminance distributions. Once the skies are identified, the basic solar irradiance and daylight illuminance at the surfaces of interest can be obtained, involving simple mathematical expressions. This study presents a numerical approach to predict the vertical solar irradiance and daylight illuminance based on the CIE standard skies. Climatic parameters recorded between January 2004 and December 2005 are used in the analysis. The performance of the calculation method is evaluated against data measured in the same period. The annual RMSEs were found ranging from 17.7% to 20.8% for daylight illuminance prediction and 17.9%–19.8% for solar irradiance estimation. The findings provide an alternative to compute solar irradiance and daylight illuminance on vertical surfaces facing various orientations. [Copyright &y& Elsevier]