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The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

Windows are responsible for the largest amount of energy loss related to building envelope. Past studies on the energy consumption of windows have mainly focused on residential and commercial buildings, while few studies have explored the energy consumption of windows in educational buildings. The sizes and locations of classroom windows affect not only the building energy consumption but also the indoor environment quality, which is an issue that needs to be balanced. This paper takes a classroom in Guangzhou as the object. Under the premise of meeting the lighting standards of the classroom, two parameters—window height and sill height —were simulated in different corridors of north–south and east–west classrooms to analyse the impact of their changes on the indoor environment as well as energy consumption based on the parametric platform Grasshopper and its plug-ins Ladybug and Honeybee. The energy consumption of a number of fenestration combinations on both sides of the classroom was optimized automatically by genetic algorithm, and the sizes and positions of windows that benefitted energy savings were obtained. In the best case ~16.6% of energy consumed in a whole year can be saved compared with the worst case. This conclusion can provide a quick decision-making process for the design of window shapes for the purpose of energy savings in the early design stage of educational buildings in the Pearl River Delta such that energy savings can be quickly and clearly guaranteed in the subsequent window configuration. [ABSTRACT FROM AUTHOR]

Due to the complexity of the operation control of urban rail transit and diversity requirements for section running time standards, based on actual train operation data, this paper proposes a curve fitting method to find the interrelation between running time and energy consumption. According to features of the energy consumption-running time curve, the discriminant criterion of outliers is constructed to select the candidate fitting data set from the original data set. To fit the energy consumption-running time curve from two-dimensional scatter points, we propose a B-spline curve fitting method based on a genetic algorithm and the fitting method is proven to have high fitting accuracy and convergence speed. Furthermore, we propose an optimization method for the fitting curve based on dynamic adjustment of the fitting data set which is selected from the candidate fitting data set to obtain the optimal energy-running time curve. The validation of Guangzhou Metro's actual operation data shows that the energy-running time curve fitted and optimized by our method has lower energy and better continuity and smoothness and could be used for evaluation of train drivers' performance and energy consumption of train operation diagram. [ABSTRACT FROM AUTHOR]