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To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

The "Reverse power flow" (RPF) behavior of distributed photovoltaics (DPV) promotes the new energy consumption and has a negative impact on the adaptability of the distribution network (DN). Researching and constructing a DN adaptability assessment model considering the DPV's RPF behavior is an important method to explore the weaknesses of DN adaptability from the perspective of management decision-making, and then propose improvement solutions. Firstly, this paper constructs a set of hierarchical DN adaptability assessment index systems from the three dimensions of security, economy and low carbon. Secondly, the combination of ordered weighted averaging improved the Analytic Hierarchy Process and the Entropy weight method is used to weigh the indicators. Then, an assessment method based on the combination of credibility theory and cloud model is proposed to solve the ambiguity and randomness in the evaluation process. Finally, the model was applied practically in the DN of Qiantuan Village in Beijing, and the adaptation of the DN is compared and analyzed under three scenarios: DPV not grid-connected, grid-connected and fully local consumption, grid-connected and RPF behavior. The results show that the adaptability levels of Qiantan Village DN are classified as "Medium", "Good", and "Good", with scores of 78.839, 86.312, and 88.287, respectively, under the three different scenarios. Furthermore, regarding the first-level indicators, the DPV's RPF behavior reduces the security of the DN but improves the adaptability in general. This paper's research can accurately locate the weak points based on reflecting the level of DN adaptability, thereby providing a reference for the formulation of a DN adaptability improvement plan and the construction of a clean and low-carbon DN. • Constructed a set of distribution network adaptability assessment index systems. • Propose a Combined weighting method by combining Ordered Weighted Averaging improved AHP and the Entropy weight method. • Established a distribution network adaptability assessment model based on the credibility theory and cloud model. • The adaptability evaluation model is applied to the distribution network of Qiantuan village in Beijing. [ABSTRACT FROM AUTHOR]

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking. • An integrated data-driven model for large-scale EV operation analysis is proposed. • Real-world energy use patterns and load profiles of urban EV fleets are unveiled. • Urban-scale EV big data are utilized for fact-based assessments. • Diverse data sources are linked through spatiotemporal information. [ABSTRACT FROM AUTHOR]

A heat pump driven atmospheric water harvesting system, which can be operated under direct cooling mode and desiccant wheels assisted cooling mode, was proposed in this paper. Investigations were carried out to find out the optimal operation mode and operation parameters under different ambient conditions and daily water harvesting requirements. Optimization studies were firstly carried out on each operation mode to find the recommended operation parameters, namely evaporation temperature and daily operation hours, for different ambient conditions, daily water harvesting rate and maximum operating hours. Then, the optimal operation modes, which has lower daily power consumption under their own recommended operation parameters, were suggested on the psychrometric chart. The results showed that, for different combinations of daily water harvesting rate and maximum operating hours, there exist a critical humidity ratio and a critical relative humidity ratio for each case. When ambient air relative humidity and humidity are both higher than the critical values, it is recommended to use the direct cooling mode. Otherwise, the desiccant wheels assisted cooling mode should be used. Lastly, performances of the system in different climate zones were analyzed. Under the optimal operation mode, the annual average water harvesting efficiency was from 1.08 to 1.22 kg/kWh in Urumqi, from 1.50 to 1.78 kg/kWh in Beijing, and from 2.22 to 3.72 kg/kWh in Haikou, respectively, when daily water harvesting rate is from 50 to 200 kg/day and maximum operating hours is from 6 to 10 h. • Propose a water harvesting system that can be operated under two operating modes. • Study the optimized operating parameters and performances for each operating mode. • Suggest optimal mode based on ambient conditions and water harvesting requirements. • Study annual performances in three typical cities with different climate conditions. [ABSTRACT FROM AUTHOR]

Abstract Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock. Highlights • There are significant time-varying correlations and long-run persistence of shocks to the DCCs between three markets. • There is a bi-directional volatility spillover between the coal market and the new energy stock market. • The optimal weight is 82.65% for new energy stock and 17.35% for coal. • The optimal hedge ratio is 17.34% for new energy stock and 49.98% for coal. [ABSTRACT FROM AUTHOR]

Abstract Battery electric vehicles (BEVs) have positive effects on the reduction of petroleum dependence and vehicle emissions. However, limited driving range of BEVs contributes to the range anxiety of drivers. Therefore, accurately estimating remaining driving range is a critical issue for BEV manufacturers to help drivers alleviating range anxiety. In this paper, by using the real-world data collected from a BEV operating in Beijing, China, the nonlinear estimation models for remaining driving range under different temperature conditions are established based on the data-driven method. The models consider the State of Charge (SOC), speed and temperature conditions as the impacting factors for remaining driving range. The significant nonlinear relationship between speed and driving distance per SOC is explored and considered in the model. The robust nonlinear regression method is used to determine the parameters of the models. Model verification results confirm the accuracy of the model. Moreover, the models are used to explore the economical driving speeds for the BEV under different temperature conditions. The results indicate that the economical driving speeds have an increasing trend as temperature increases. The economical driving speeds under low, moderate and high temperate conditions equal to 48.97 km/h, 50.89 km/h and 51.37 km/h, respectively. Highlights • The data from Beijing are used to model the remaining driving range of BEVs. • Energy consumption under different speeds and temperature conditions is explored. • Robust nonlinear regression method is adopted to establish the models. • Economical driving speeds are explored by using the models. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing. • The energy rebound effect of Beijing are quantified at the level of overall economy, industry and sector. • The overall economy of Beijing appears backfire effect and partial rebound effect. • The energy rebound effect of the tertiary industry is higher than that of the secondary industry in most years. • Sectors with low energy efficiency may have higher rebound effects. [ABSTRACT FROM AUTHOR]

The national and municipal government of China enacted a series of regulations and policies to stimulate/promote the development of new energy vehicles, in order to mitigate the increasingly serious carbon emissions, environmental pollution, and energy shortage. As a large metropolitan and populated city subject to the notorious air pollution, Beijing has been making a remarkable progress in the large-scale demonstration of new energy vehicles in recent years, which could result in a significant impact on both transport and electricity sectors. As a result, there is an urgent necessity to study the characteristics of the large-scale new energy vehicles adoption for a deep understanding of operational status (e.g., energy consumption and battery charging patterns) and benefits, as well as charging facilities. Based on the operational data collected from realistic electric-taxi demonstration in Beijing, the driver behavior and charging characteristics are examined in this paper. The energy consumption and efficiency of two representative electric-taxi platforms are compared, and the influence of the driving schedules is discussed. The results show that the average driving distance per day of these electric taxes is 117.98 km, and 92% of drivers recharge their cars twice per day. Further study shows that the drivers make two trips per day, and the two peaks in the distribution of departure and arrival times coincide with the rush hour in the morning and evening. The taxi recharge duration is largely influenced by the charging power. Generally, the associated battery SOC (state of charge) swing is between 40% and 100%. By evaluating the energy consumption of 282 trips recorded in 2013 and 2014, we find that the two platforms have similar energy efficiency. The micro-trips method is utilized to probe the correlation of energy consumption and average speed. [ABSTRACT FROM AUTHOR]

Urban-scale energy model can be a crucial tool for evaluating and in turn propelling the implementation of energy efficiency and low carbon programs for buildings and cites. A variety of disputes on integrity and correctness lie in basic statistical data of China's building energy use. Inadequate information and performance gap caused by input uncertainties are the common obstacles to produce reliable energy modelling results. This paper develops a bottom-up approach to depict energy use intensity (EUI) for space heating of building in the urban level accurately, even if the data resource is inadequate and imprecise or the data are influenced by uncertain issues. A hybrid probability model is developed and the model parameters are calibrated by Bayesian inference and Markov Chain Monte Carlo simulation using data set of meter reading from 2062 stochastic-sampled heating substations in Beijing. Preparation and simulation efforts of using this energy model are discussed, following this framework and adjusting according to local climate conditions, typological building characteristics and morphological urban-scale parameters. The results show that the approach is reliable and efficient to calibrate parameters in building energy models. The urban-scale benchmarks of EUI for space heating in Beijing is successfully performed to demonstrate the proposed methodology. • Reliability of top-down and bottom-up approach are discussed in urban-scale modelling. • An urban-scale energy model is proposed even if the data are affected by uncertainty. • The framework of selection, calibration and validation of modelling are presented. • Study on urban-scale Benchmark for heating in Beijing shows how well the methodology. [ABSTRACT FROM AUTHOR]

Station-skipping and deadheading for subway operation regulations are two effective ways to reduce the effects of train variations, such as large passenger flow and unexpected incidents. These variations, if not properly eliminated through strategies, will lead to a gap in the train and eventually increase passenger waiting time and energy consumption. This paper addresses the last-train station-skipping, transfer-accessible, and energy-efficient scheduling problem for the subway system by optimizing the subway schedule and the last train station-skipping scheme. First, an integrated last train operational model was developed to achieve energy savings and better performance of transfer waiting and in-train travel times by adjusting train acceleration, cruising, coasting, and braking times on each rail segment. Second, a heuristic evaluation-based optimization algorithm was designed to solve a real-life case study of the Beijing Subway to demonstrate the effectiveness of our methods. Two operational strategies (station-skipping and deadheading) for the last trains were designed and compared quantitatively. The results indicate that the station-skipping plan shows an advantage in minimizing the in-train travel time and energy consumption. (1) This paper proposes the integrated last-train station-skipping, transfer-accessible, and energy-efficient scheduling problem for the subway system. (2) By optimizing train acceleration, cruising, coasting, and braking times on each rail segment, energy consumptions and train operating times are reduced. (3) Station-skipping and deadheading strategies for the last train operations are designed. (4) A heuristic evaluation-based optimization algorithm is designed to solve a real-life case study of the Beijing Subway. [ABSTRACT FROM AUTHOR]

With Beijing's energy demand shifting to the household side in the past decade, uncovering the driving factors of Beijing's energy consumption plays an essential role for policy design. To this end, the structural changes in Beijing's residential and productive energy consumption are decomposed using the logarithmic mean Divisia index (LMDI) method in this paper. Empirical analysis is conducted focusing on Beijing's energy changes during 2009–2018. Results suggest that: (1) The contribution of residential energy consumption has exceeded that of the productive sector, meanwhile the expected decrease in both sectors' energy consumption are not observed considering the energy efficiency improvements; (2) The optimization of Beijing's energy consumption cannot solely rely on the technical development, the influence due to the upgrade of industrial structure in Beijing also matters, especially after 2011; (3) In the residential sector, all factors display stronger impacts on energy consumption changes in urban areas than in rural areas, among which the living standard is the most significant. Based on these results, suggestions are provided to promote the decrease of Beijing's energy consumption. • The structural changes in Beijing's residential and productive energy consumption are decomposed. • The phenomenon of "Jevons paradox" appear in both Beijing's productive and residential sectors. • The energy optimization in Beijing cannot solely rely on technical improvement due to the rebound effect. • All factors display stronger impacts on energy consumption changes in urban areas than in rural areas. [ABSTRACT FROM AUTHOR]