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In order to build Beijing into a world-class harmonious and livable capital city, its energy and environmental issues deserve special attention. Considering London’s experience and achievements in tackling smog crisis, New York’s world-class city positioning and influence, and Tokyo’s geographical and cultural characteristics, these three world-class megacities are taken as examples in this study for investigating and analyzing the establishment and optimization of an urban energy system which is clean, efficient, safe, and sustainable. The urban planning strategies, planning experience, and related energy policy of these three megacities for building low-carbon cities are reviewed and their enlightenment to Beijing is discussed. The study of international cases has a certain reference effect on the energy structure adjustment and urban planning practices related to Beijing’s construction of a low-carbon city.

Metallic nanowires (NWs) and their different compounds display incredible prospects for their use in various applications including media storage, sensor and solar cell devices along with the biological drug delivery systems. In this research work, the metallic NWs like nickel nanowires (Ni-NWs) are synthesized successfully by employing electrodeposition process. Anodic aluminum oxide (AAO) templates are employed as a platform with copper metal coating which acts as an active cathode. The synthesized Ni-NWs are examined through various characterization techniques including X-ray diffraction (XRD), scanning electron microscope (SEM) and vibrating sample magnetometer (VSM) to study the crystal structure, surface morphology and magnetic properties, respectively. The XRD analysis shows the development of various diffraction planes like Ni (111), Ni (200), Ni (220) which confirms the formation of polycrystalline nickel NWs. The SEM analysis reveals that the range of diameter and length of nickel NWs are found to be ∼160 to 200 and ∼4 to 11 micron respectively showing high aspect ratio (ranged from ∼200 to 300). The ferromagnetic behavior of Ni-NWs is confirmed by the hysteresis loop carried out for parallel and perpendicular configurations having Hc = 100 and 206 Oe, respectively. The obtained results suggest that the synthesized Ni- NWs may be used for high-density media storage devices.

Ionic liquids (ILs) are promising electrolytes or solvents for numerous applications owing to their unique properties. However, it is a challenge to design the ideal IL with the required properties. Variational autoencoders (VAEs) trained by significantly large datasets have shown good performance in drug discovery. However, low generation efficiency and small sparse datasets prevent their application on IL. In this work, we propose a high generation efficiency molecular design model for IL, which realizes multi-objective optimization on a small dataset. The model combines VAE, multilayer perceptron, and particle swarm optimization for property prediction and molecule optimization. The thermal conductivity and heat capacity of the ILs are chosen as a case to verify the advantages of our model. The results shows that by setting molecular validity judgments to optimization target, 98% output of our method are valid molecules. Besides, the heat capacity and thermal conductivity are improved by 39% and 15%, respectively. Our model improves the applicability to small sparse datasets and the generation efficiency of VAE-like generation model. By multi-objective design ILs for given properties, our model can provide guidance for the design and application of ILs.

The coordinated development of the Beijing-Tianjin-Hebei region has become China's national strategy with great and far-reaching significance. The construction of Energy Internet is an important measure to strengthen the coordination of green energy development in Beijing-Tianjin-Hebei region and promote the development of renewable energy. In this paper, based on the development status of renewable energy in the Beijing-Tianjin-Hebei region, the problems and challenges existing in the renewable energy development in the Beijing-Tianjin-Hebei region and related considerations for their solution are reviewed and discussed. Energy Internet has also become a booster for the rapid development of clean and renewable energy. The policies on the Energy Internet Construction issued by Beijing, Tianjin, and Hebei during the 13th Five-Year Plan Period (2016–2020) and at the beginning of the 14th Five-Year Plan Period (2021–2025) are summarized. The focuses of all these policy documents are analyzed. The development status of seven Energy Internet demonstration projects in Beijing-Tianjin-Hebei region is reviewed. On this basis, some considerations on Energy Internet construction in the Beijing-Tianjin-Hebei region are analyzed and proposed.

Energy saving and environmental protection have become the core development concept of China construction industry. There are still a large number of old urban buildings in Beijing, whose energy consumption far exceeds the current building energy efficiency standards. The energy-saving renovation of these old buildings has been becoming the focus and difficulty of urban building energy-saving. In this paper, a calculation model based on the TNM Decree is proposed to calculate and analyze the energy consumption of heating and domestic hot water supply in winter before and after the renovation of old urban buildings in Beijing. The calculation results show that the thermal insulation performance of building envelope is a key factor affecting the energy consumption of old urban buildings. After the renovation, the energy-saving rate of the old urban buildings completed between 1978 and 1985 can reach 65.53%. The energy-saving rate of the buildings completed between 1986 and 1995 and that between 1996 and 2005 can reach 55.04% and 32.79%, respectively.

As a powerful tool to solve nonlinear problems, artificial neural network method (ANN) gets a wide range of applications in data regression. However, the overfitting often occurs during the ANN training process, which results in high accuracy of correlating the training data but poor prediction performance. At the same time, the principle of k-Nearest Neighbor method (kNN) makes it impossible to make an accurate prediction exceeding the range of the training data, but it can confine the overfitting of ANN. In this work, combining ANN and kNN, a new machine learning method called ANN-kNN combination method (AKC) for thermophysical property prediction of material is proposed. To evaluate the performance of AKC, we take the thermophysical properties of natural gas as an example. The inputs of AKC are temperature, pressure and the components of natural gas, the outputs are the compressibility factor, speed of sound and viscosity. AKC not only overcomes the overfitting problem but also needs less training data than ANN. The average absolute relative deviation of AKC for prediction are 2.5%, which are better than ANN (5.9%) and kNN (19.2%).

Bus rides have become a routine choice for the large population to travel in Beijing. By the end of November 2020, 82.84% of all buses in Beijing had switched to electric vehicles. A solution to the problem of charging a huge number of battery electric buses (BEBs) in urban areas, especially in crowded cities with large populations, has to be found. In the present paper, a calculation model based on the levelized cost of electricity (LCOE) is established for comparison and analysis of the BEB charging. The calculation results show that the hybrid charging mode is economically feasible. Moreover, from the perspectives of optimizing operation shifts and responding to emergency situations, hybrid charging mode is applicable to the current electric bus system. The calculation results also show that the use of BEBs as the main ground public transportation in Beijing can substantially reduce CO2 emissions and save huge fuel costs. Its application is a very wise choice. The analogy calculation on Tongzhou District, the sub center of Beijing which is under construction, shows that the use of BEBs in Tongzhou can reduce CO2 emissions by about 34.6% on average compared with fuel buses. The population is the main influencing factor of analogy calculation.