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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]

In the context of prominent energy crisis, photovoltaic power (PV) generation has received increasing attention, then accurate PV generation forecasting is crucial for ensuring the smooth operation of power stations. However, existing research is insufficient in comprehensively analyzing the impact of PV generation volatility. To fill the gaps and enhance the prediction accuracy, this paper proposes a new hybrid forecasting method. We first introduce the Locally Weighted Scatterplot Smoothing (LOWESS) method to process the data and enhance the data stability, and use Pearson correlation coefficient (PCC) and Random Forests (RF) for feature selection to improve the quality of input data. Then we use Attention mechanism and Convolutional Neural Network (CNN) layer to optimize Bi-directional Gate Recurrent Unit (BiGRU) model and form a new hybrid model. Finally, based on the Bagging algorithm, we use ensemble learning to further optimize the hybrid BiGRU model to enhance the depth and performance. The proposed method is validated through case analysis results from two different locations, Xuhui District in Shanghai, China and the DKASC area in Alice Springs, Australia. The results demonstrate that, compared with other models, the developed method exhibits exceptional prediction performance and effectively enhances the accuracy of PV generation forecasting. Keywords : photovoltaic power generation; Locally Weighted Scatterplot Smoothing; feature selection; ensemble learning; Bi-directional Gate Recurrent Unit. • LOWESS smoothing technology is used to reduce the variability of PV generation data. • Pearson correlation coefficient and Random Forest are used to for extract feature. • Attention mechanism and CNN layer are used to optimize BiGRU model. • Two BiGRU layers are stacked to improve the depth and prediction performance. • Ensemble learning is introduced to further optimize the hybrid BiGRU model. [ABSTRACT FROM AUTHOR]

Due to the high dehumidification performance realized by removing adsorption heat by inner cooing, desiccant coated heat exchanger (DCHE) has been proposed and widely studied. The heat pump is usually used as the cooling and heat sources of the dehumidification system composed of DCHE because the cooling and heat capacity could be provided by the heat pump at the same time. Moreover, considering the advantage of low-grade thermal energy utilization, solar thermal energy can be used to further enhance the regeneration of DCHE to improve dehumidification performance and energy efficiency. In this paper, the solar-enhanced fresh air dehumidification system using DCHE driven by heat pump was proposed and a series of experiments were conducted under typical Shanghai summer high humidity conditions. The typical moisture removal of the system can be increased by about 15% to 10.14 g/kgDA and the COP could be further increased by over 25% to 5.39. In addition, the influence of different regeneration temperatures enhanced by solar thermal energy on the system has also been studied. Considering the dehumidification performance improvement and energy utilization efficiency, the optimal regeneration temperature is between 60 °C and 65 °C for the system proposed in this paper. • Moisture removal and COP can be increased by 6%–28% by solar enhanced. • Performance enhanced is more obvious when regeneration temperature is below 65 °C. • Solar enhanced dehumidification 1.43 (g·kgDA−1)/kW and 1.12 kg/kWh can be achieved. • Higher efficiency solar collectors will achieve higher solar enhanced performance. [ABSTRACT FROM AUTHOR]

Efficient and trustable food delivery systems have a great impact on our daily life. Considering environmental protection, Electric Cold-chain Vehicle (ECV) has been adopted to support the food delivery system in urban area. However, the drivers of ECV has the motivation to power off the refrigerator for saving electricity, which would lead to the food safety issue. Driven by this, we proposed a novel blockchain-based Electric Cold-chain Delivery System (BECDS) and concentrated on the EV charging and scheduling problem in the distribution network. In terms of methodology innovation, the Dynamic charging and service schedule model (DCSSM) is proposed based on the multi-agent-based simulation. Meanwhile, Internet of Things (IOT) devices are used to monitor the environment data of food, which would be recorded in the blockchain. Furthermore, this paper carries out numerical experiments to verify the feasibility of this system via realistic data. Interestingly, the result shows that the new cold-chain delivery system could serve retailers more efficiently with zero emissions. Hence, this paper could broaden and defined a promising green and digital urban food delivery prototype in the future. • This paper proposed an intelligent urban food delivery system supported by electric vehicles and blockchain. • Detailed service and charging simulation model is constructed considering the dynamic green delivery scenarios. • Elaborately technological framework is designed to make the intelligent blockchain-based system more applicable. • The proposed novel green delivery system is verified by the realistic business data in Shanghai, China. [ABSTRACT FROM AUTHOR]

This paper seeks to study the waste-to-energy facility location planning problems under uncertainty from an integrated perspective. The extended exergy accounting method is first used to develop a multi-criteria decision analysis framework that unique in unifying multiple criteria of material, energy, social, economic and environmental aspects into homogeneous exergy terms, thus employing multiple criteria to obtain an integrated decision-making result. Robust optimization and mixed-integer linear programming mathematical techniques are applied to formulate the problem as a two-stage robust waste-to-energy facilities location model based on this framework to minimize the exergy expenditure, and determine the technology type, location and capacity size of waste-to-energy facilities over a large urban region. The uncertainty of municipal solid waste supply quantity in each waste generation site is considered. The proposed model was demonstrated using a case study for household food waste treatment in Shanghai, and two treatment scenarios are proposed. Under the same uncertainty, the deployment strategy in scenario II feeds annual 2,655,140 GJ more equivalent primary exergy into the urban ecosystem than the scenario I, indicating that the only deployed on-site biogas plant to disposal household food waste performs better than the centralized incineration and on-site biogas plant combined deployment. A further sensitivity analysis reveals several valuable insights into municipal solid waste management. If the uncertainty is not considered, it may result in a municipal solid waste treatment capacity gap of up to 11.5% and a waste-to-energy system breakdown. Introducing the uncertain budget and range can help the decision-maker adjust the planning scheme flexibly, but the results become more conservative as their values increase. In this case study, it is optimal for decision makers to choose uncertain range m ˆ i = 12.5 % and uncertain budget Γ = 30. [Display omitted] • Applying extended exergy accounting method to develop a multi-criteria analysis framework. • Formulating a two-stage robust waste-to-energy facilities location model. • Considering the municipal solid waste supply uncertainty and constructing its uncertainty set. • Proposed model is used to plan locations of waste-to-energy facilities in Shanghai. • Conducting sensitivity analysis to study influencing factors in location planning. [ABSTRACT FROM AUTHOR]

This paper examines whether the efficient market hypothesis (EMH) holds in the Chinese carbon trading pilots by employing the Sequential Panel Selection Method (SPSM) which is combined with the Panel KSS unit root tests with Fourier function. This approach serves as a highly valid tool in controlling for cross-sectional dependence and heterogeneity as well as structural shifts and nonlinearities. In virtue of the SPSM, the paper could divide the whole panel into two groups and clearly identify which and how many series belong to stationary or non-stationary group. The results show that carbon prices follow mean reversion process, indicating that the EMH does not hold in the Chinese emissions trading scheme (ETS), with the exception of Shanghai. The inefficiency of ETS pilots can be explained by irrational behaviors, poor information transparency, imperfect market mechanism and transaction costs. Accordingly, the failure of EMH implies the existence of profitable arbitrage opportunities for market participants. To establish the nationwide ETS market, policy lessons should be learned from Shanghai as this pilot is demonstrated to be an efficient market. Additionally, policymakers should improve the trading information disclosure system and formulate uniform guidelines, which is propitious to enhance market efficiency of the Chinese ETS. • Market efficiency of the Chinese ETS pilots and optimal scheme are assessed. • Nonlinearities and structural breaks in ETS markets are taken into account. • Carbon prices follow mean reversion process for 5 of 6 pilots, except for Shanghai. • The inefficiency is due to poor information transparency and market mechanism. • The nationwide ETS market should draw on policy experience of Shanghai pilot. [ABSTRACT FROM AUTHOR]

The energy crisis is a major challenge to sustainable development. Governments therefore constantly seek ways to improve energy conservation. In this paper, our goal was to promote energy conservation and simultaneously increase employment. First, based on China's current regional energy conservation model, social energy costs and employment factors are comprehensively considered to construct an inter-provincial cooperative energy conservation model, the minimal core method is used to construct a cooperative interprovincial benefit-distribution model. Second, representative regions of China (Beijing, Shanghai, Shanxi, and Sichuan) were selected to demonstrate the model in an empirical analysis of cooperative provincial energy conservation. It was found that the total employment in Beijing, Shanghai, Shanxi, and Sichuan can be increased by 9.84 × 106 persons, and the total social energy cost can be decreased by US$59.22 × 106. The cooperative benefit distribution model can increase the total benefit by US$65.28 × 106. Finally, to verify the robustness and feasibility of the model, the sensitivity of the model to these changes is analyzed by changing the parameter values, and it was found that the model was both robust and feasible. The research results provide a new way to improve China's current energy conservation model. • The inter-provincial cooperation model for employment and social energy costs (i.e. bi-objective model). • Constructs a social energy cost function. • Considers energy structure factors. • Uses the minimal core method for benefit distribution. [ABSTRACT FROM AUTHOR]

Providing effective charging infrastructure is an essential step in popularizing electric vehicles, which is conductive to improving pollution induced by traditional fuel vehicles and thus improving the environmental sustainability. The most crucial factor in escaping the dilemma of charging infrastructure construction lies in the synergies of multi-sectorial. This paper develops a stochastic evolutionary game model grounded on the strategy adoption of charging infrastructure construction involving governments (public), companies (private), and consumers (civil). We collect the data of Shanghai for simulation to compare the influence of strategy adoption. Our findings suggest that ⅰ) unlike the public and civil sector, in the current situation, the private sector will adopt the defector strategy quickly and lock in the defection state as time goes on; ⅱ) the additional cost effect caused by public-civil co-construction scenario is significant positive to the public sector but almost no impact on the civil sector; ⅲ) civil's strategy adoption has directional differences under two cost sharing scenarios (private-civil and public-civil) of charging infrastructures construction. In the private-civil scenario, if the cost of charging infrastructures construction is transferred to the civil sector, not only will it not change the private-sectorial defection state, but it will also cause civil sector to shift from cooperation to defection. However, at the same sharing ratio of the public-civil scenario, the civil sector is still willing to cooperate. ⅳ) reducing incentives of electric vehicles or increasing costs of internal combustion engine vehicles is necessary, which will not change the lock-in cooperation state of civil sector, and it can improve the cooperation willingness of publicsector. • A multi-agent stochastic evolutionary game model with charging infrastructure adoption is constructed. • Simulation with real-world values of game parameters for the case of Shanghai. • Parameters sensitivity to the strategy adoption are discussed by computational experiments. • Civil's strategy adoption has directional differences under two scenarios of charging infrastructure construction. • It's time to consider eliminating the subsidy for EVs because of the lock-in state of civil cooperation. [ABSTRACT FROM AUTHOR]

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences. • Understand the spatial and temporal dynamics of urban energy use through visual analysis and visualization. • A visual analysis tool to facilitate the discovery of energy consumption patterns for smart energy systems. • Development of dimension reduction and visualization methods for high-dimensional time series. • Discovery of typical energy consumption patterns and spatio-temporal shift patterns through an empirical case study. [ABSTRACT FROM AUTHOR]