In this study, we investigated the impact of the lighting environment on psychological perception, physiology, and productivity and then designed lighting control strategies based on the experimental results. The research was conducted in a smart lighting laboratory, and 67 subjects were tested in different illuminances and correlated color temperatures (CCTs). During the experiment, the physiological data of subjects were continuously recorded, while the psychology and productivity results were evaluated by questionnaires and working tests, respectively. The experimental results found that both illuminance and CCT could significantly influence the feeling of comfort and relaxation of the subjects. Warm CCT and higher illuminance (3000 K–590 lux) made subjects feel more comfortable. Productivity reached its maximum value with illuminance above 500 lux and equivalent melanopic lux (EML) higher than 150. The brain-wave and heart-rate changes did not have a close relationship with either illuminance or CCT, but the heart rate slightly increased in the adjustable lighting mode. Regardless of the initial value setting, the subjects preferred intermediate CCT (4200 K) and bright illumination (500 lux) after self-adjustment. Finally, we proposed three comprehensive lighting control strategies based on psychology, productivity, circadian rhythm, and energy-saving.

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value.