Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

With the advancement of modernization, high energy consumption buildings can no longer meet the needs of social development. Under the background of low carbon and energy saving, the development of green buildings has become the only way, but its energy-saving design effect needs to be further studied. Aiming at lighting and energy consumption, this study carried out multi-factor optimization analysis based on genetic algorithm on factors such as windowing ratio, wall heat transfer coefficient, window heat transfer coefficient, window transmittance and roof insulation coefficient. Firstly, the theory and technical scheme of applying data mining technology to solve the energy-saving design problems of different buildings are proposed and implemented, including the design of new and existing buildings, as well as the determination of decisive parameters and non-decisive parameters. Secondly, computer simulation and theoretical analysis are used to optimize the analysis of the building scheme, so as to find the optimal design range of each influencing factor and the optimal design method of green low-energy building. Multi-factor optimization theory and genetic algorithm principle are summarized, and the heat transfer coefficient of external wall and window of the building is selected as the optimization variable, so as to achieve low energy consumption and enclosure cost of the building. Aiming at better thermal comfort, an optimization model was established. Finally, through empirical research, an energysaving plan was designed, and genetic algorithm was used to obtain the optimal solution for maximizing the incremental benefits obtained by unit input incremental cost. The results indicate that the ideal incremental benefits come from a reasonable and effective combination of technologies, mainly from air conditioning systems and lighting systems; the setting of the benchmark return rate will directly affect the optimization effect of energy-saving plans, providing decision-makers with the optimal combination of energy-saving technologies

Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

With the advancement of modernization, high energy consumption buildings can no longer meet the needs of social development. Under the background of low carbon and energy saving, the development of green buildings has become the only way, but its energy-saving design effect needs to be further studied. Aiming at lighting and energy consumption, this study carried out multi-factor optimization analysis based on genetic algorithm on factors such as windowing ratio, wall heat transfer coefficient, window heat transfer coefficient, window transmittance and roof insulation coefficient. Firstly, the theory and technical scheme of applying data mining technology to solve the energy-saving design problems of different buildings are proposed and implemented, including the design of new and existing buildings, as well as the determination of decisive parameters and non-decisive parameters. Secondly, computer simulation and theoretical analysis are used to optimize the analysis of the building scheme, so as to find the optimal design range of each influencing factor and the optimal design method of green low-energy building. Multi-factor optimization theory and genetic algorithm principle are summarized, and the heat transfer coefficient of external wall and window of the building is selected as the optimization variable, so as to achieve low energy consumption and enclosure cost of the building. Aiming at better thermal comfort, an optimization model was established. Finally, through empirical research, an energysaving plan was designed, and genetic algorithm was used to obtain the optimal solution for maximizing the incremental benefits obtained by unit input incremental cost. The results indicate that the ideal incremental benefits come from a reasonable and effective combination of technologies, mainly from air conditioning systems and lighting systems; the setting of the benchmark return rate will directly affect the optimization effect of energy-saving plans, providing decision-makers with the optimal combination of energy-saving technologies

Vitamin C Protected Human Retinal Pigmented Epithelium from Oxidant Injury Depending on Regulating SIRT1

The purpose was to investigate the protective effects of Vitamin C (Vit C) and the regulatory mechanism between Vit C and sirtuin 1 (SIRT1) in PREs during oxidative stress as Vit C and SIRT1 exerted famous effects as antioxidants. We found that moderate Vit C (100 µM) prevented ARPE-19 cells from damages induced by H2O2, including increasing viability, reducing apoptosis, and attenuating intracellular ROS levels. But lower and higher concentration of Vit C had no effects. Further results indicated that Vit C caused the dysregulation of some stress responses factors (SIRT1, p53 and FOXO3) in ARPE-19 cells response to H2O2. Moreover we found that SIRT1 activator resveratrol (SRV) stimulated significantly the protective effects of moderate Vit C, provided the property of antioxidative stress for the lower and higher concentration of Vit C in ARPE-19 cells as well. Consistently, nicotinamide (NA) relieved the protective functions of moderate Vit C. Interestingly, data also revealed the dysregulation of p53 and FOXO3 was dependent on the regulation of SIRT1 rather than Vit C. Summarily, the protective effect of Vit C against oxidative stress was involved in regulation of SIRT1. It suggested that combined application of Vit C and RSV might be a promising therapeutic method for AMD