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With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line. • A novel multi-objective optimization model based on the reinforcement learning is proposed to search for the optimal control sequence. The embedding matrix is used to formulate the control sequence. • An intelligent train operation algorithm based on the gated recurrent unit network is proposed to compute the optimal speed trajectory without the precise train dynamics or the prior knowledge from the experienced drivers. • The energy consumption is reduced by 3.17%, 0.71% and 6.77% respectively comparing with fuzzy predictive control, genetic algorithm and field test data. [ABSTRACT FROM AUTHOR]

In this paper, the prospect of low-GWP refrigerants used in solar-driven ejector-compression hybrid refrigeration system was analyzed and evaluated based on the solar radiation data in summer of Beijing and Guangzhou. Prominent environmental friendly alternative refrigerants, including HCFO-1233zd(E), HFO-1336mzz(Z), HFO-1234ze(Z), HCFO-1224yd(Z) and HC-600 were chosen as the working fluids to address the environmental concerns, while HFC-245fa was used as the baseline refrigerant. The hybrid refrigeration system composed of an ejector refrigeration system as the upper stage and a vapor compression system as the bottom stage, respectively. The results indicated that the system with HFO-1234ze(Z) showed the highest COP. HFO-1234ze(Z) was followed by HCFO-1233zd(E) and HC-600, and all of them showed higher COP than that of HFC-245fa. The system with HC-600 needed more electric energy than any other systems, while HFO-1336mzz(Z) consumed the lowest electric power. Meanwhile, the performances of HFO-1234ze(Z) and HFC-245fa systems were further compared. It was found that for both working fluids, the heat load of the condenser (Q e,c), total electric energy consumption (W e) and electric energy of compressor (W com) in August were the highest in Beijing, while in Guangzhou, the highest of them was achieved in September. It was also suggested that the solar radiation had no influence on the relative magnitude of the Q e,c , W e and W com between HFO-1234ze(Z) and HFC-245fa systems. The Q e,c , W e and W com of the system with HFO-1234ze(Z) were higher than those of HFC-245fa by 0.8%, 4.44%, and 3.55%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper is the second in a series of three papers aimed at understanding the emissions of vehicles in China by conducting on-board emission measurements. This paper focuses on light-duty gasoline vehicles. In this study, we measured 57 light-duty gasoline vehicles (LDGVs) in three Chinese mega-cites (Beijing, Guangzhou, and Shenzhen), covering Euro 0 through Euro IV technologies, and generated CO, HC, and NOx emission factors and deterioration rates for each vehicle technology. The results show that the vehicle emission standards have played a significant role in reducing vehicle emission levels in China. The vehicle emission factors are reduced by 47–81%, 53–64%, 46–71%, and 78–82% for each phase from Euro I to Euro IV. Euro 0 vehicles have a considerably high emission level, which is hundreds of times larger than that of Euro IV vehicles. Three old taxis and four other Euro I and Euro II LDGVs are also identified as super emitters with equivalent emission levels to Euro 0 vehicles. Of the measured fleet, 23% super emitters were estimated to contribute 50–80% to total emissions. Besides vehicle emission standards, measures for restricting super emitters are equally important to reduce vehicle emissions. This study is intended to improve the understanding of the vehicle emission levels in China, but some key issues such as emission deterioration rates are yet to be addressed with the presence of a sufficient amount of vehicle emission measurements. [Copyright &y& Elsevier]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

Abstract: External wall insulation plays an important role in building energy consumption through affecting cooling and heating energy uses. In this paper, a study on the effects of external wall insulation thickness on annual cooling and heating energy uses under different Chinese climates is reported. Firstly, two indexes: Energy Gain Ratio (EGR) and Relative Variation Ratio (RVR) of the sum of annual cooling and heating energy uses, to assess the effects of external wall insulation thickness on energy saving potential are introduced. Secondly, a model space having four different exterior zones facing four different orientations in an office building is presented and the settings of the model space described. Lastly, the simulated annual cooling and heating energy uses for the four different exterior zones at different external wall insulation thicknesses under three different climates in China, Guangzhou, Shanghai and Beijing, are reported. The results suggested that energy saving was significant by increasing the external wall insulation thickness in exterior zones facing all orientations under Beijing’s climate, since the heating energy use was dominant and can be reduced remarkably with the increase in insulation thickness. Under Shanghai’s climate, increasing external wall insulation thickness to over 26mm would not reduce the sum of annual heating and cooling energy uses in the south-facing exterior zone, but would help save energy in exterior zones facing the other three orientations. For all exterior zones under Guangzhou’s climate, it was, however, hardly possible to reduce the sum of annual heating and cooling energy uses by increasing the external wall insulation thickness. [Copyright &y& Elsevier]

• Passengers' behavioural responses toward passenger flow control strategies. • NL model for departure time and mode shift decisions. • Mathematical programming with NL-based SUE constraints. • Trade-off between operational efficiency and service fairness. • Empirical case studies of Guangzhou and Beijing metros. This paper presents a methodology for developing demand-responsive passenger flow control strategies for oversaturated metro networks. A stated preference-off-revealed preference survey was conducted to capture passengers' behavioural responses to the passenger flow control strategies, and revealed significant behavioural change intentions in terms of departure times and mode choices. Currently, such behavioural responses are often ignored, leading to deviations in estimating the size of a target group and performance of a proposed strategy. To address this issue, in this study, a mathematical programming with equilibrium constraints (MPEC) approach was developed to optimise demand-responsive passenger flow control strategies. The proposed MPEC model highlights the importance of balancing the operational efficiency of a metro system with service fairness perceived by passengers. In particular, a nested logit-based stochastic user equilibrium problem was included to accommodate potential changes in demand patterns driven by candidate passenger flow control strategies. Two empirical cases based on the Guangzhou and Beijing metros were used to demonstrate the effectiveness of the proposed model and solution algorithm. The results show that the inclusion of service fairness considerations does not contradict the pursuit of efficiency. Instead, dual emphases on service fairness and passengers' behavioural responses help create a win–win situation for both metro operators and passengers. In the Guangzhou and Beijing metros, the highest section load rate decreased from 126.6% to 105.7% and from 122.98% to 106.87%, respectively, under the optimal passenger flow control strategies. The corresponding network load Gini coefficients improved from 0.278 to 0.259 and from 0.269 to 0.248, demonstrating the remarkable performance of this approach in regards to peak cutting and load balancing. [ABSTRACT FROM AUTHOR]