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An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, where the model was set up for coal, oil and natural gas separately, and the enhanced GM was better with relative error about 9.18% than original GM on validation data between 2010 and 2020. All results are available for reference on adjusting of energy structure and developing alternative energy resources. [ABSTRACT FROM AUTHOR]

In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting. [ABSTRACT FROM AUTHOR]

This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks. [ABSTRACT FROM AUTHOR]

In this work, based on the concept of collaborative water–energy development, a multi-scenario system dynamics simulation model of a water–energy coupling system was constructed by using the system dynamics modeling method. The model was composed of four subsystems: society, economic, water resources, and energy. Taking Ningbo City as the research location to run the simulation model, the analysis of the validity of the model showed that the relative error between the historical data and the simulation results of the model was less than 10%, which proved that the model passed the test. In this paper, based on the scenario of business an usual (BAU), three scenarios of water-saving scenario (WSS), energy-saving scenario (ESS), and comprehensive savings (CS, the comprehensive scenario considers water-saving and energy-saving together) were designed, and the simulation indexes in the three scenarios were refined in order to strengthen the control of water-saving policies, improve the effective use of water, optimize the industrial energy structure, improve the level of energy-saving-related technologies, and advance the urbanization process. The simulation results for Ningbo City from 2010 to 2030 show that the water–energy coupling system is affected by many factors, and the adjustment of a driving factor of any subsystem will have an impact on the water–energy coupling system. There are two driving factors: the first is a constant variable related to water resources, energy, society, and economic, and the second is a variable affected by time. The coupling system is based on the law of real development and is composed of causal and functional relationships between variables. Therefore, within the prediction range of 2030, the driving factors in the coupling system are controllable, and there is no uncontrollable situation. The strengthening of water-saving policies and the improvement of the coefficient of the effective utilization of water will have the optimal saving effects on water resources and energy at both the single and the coupling level; this also demonstrates that the water resource management in Ningbo City plays an extremely important role in the relationship of the water–energy coupling. The results of this study are expected to provide a valuable reference for the management and conservation of water–energy coupling in Ningbo City. [ABSTRACT FROM AUTHOR]

Bioethanol will play a significant role in energy structure adjustment and greenhouse gas mitigation in the future, especially in the transport sector. As bioethanol production with grain crops may become obsolete due to food security concerns, the Chinese government has advocated the development of non-grain bioethanol. According to the current actual situation of bioethanol development and China's Liquid Biofuel Development Roadmap, we defined three stages of bioethanol development. We focused on the assessment of bioethanol feedstock resources and bioethanol yield potential in different stages using a comprehensive evaluation system integrating statistical methods, crop growth process models, and geographic information system (GIS) techniques. The considered feedstocks included corn, sweet sorghum, cassava, switchgrass, crop straw, and forest residues. The spatial–temporal characteristics of the regional bioethanol distribution were discussed. The results indicate that the total resources of corn, sweet sorghum, cassava, switchgrass, crop straws, and forest residues were about 257.17, 2083.55, 44.36, 357.56, 1031.62, and 924 million tons at different time points, respectively. In the first stage, the year 2020, the potential bioethanol totaled 21.55 million tons. An advantage in bioethanol development was demonstrated by Northeast China. A positive development situation was also identified in East China, such as in Tianjin. In the second stage, from 2020 to 2030, the potential bioethanol production is estimated to be 145.42 million tons. The bioethanol development potential will increase in South China, in areas such as Yunnan, Guangxi, and Guizhou. In the third stage, the potential bioethanol based on switchgrass is estimated to be 92.99 million tons. The potential bioethanol based on crop straws and forest residues will be 14.76 million tons if 5% of these feedstocks are fully used for producing bioethanol. Regions with a large development potential will be further expanded. Interregional bioethanol flows and international cooperation will help meet the whole nation's requirement. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate CO2 emissions in the production of coal sources at the prefecture level and to analyze their spatial distribution and regional differences based on the spatial autocorrelation and standard deviational ellipse analysis. The results indicate that Chinese coal production from 2018 will most likely generate 485.23 million tons of CO2 emissions, and there still exists an obvious gap between the five coal development districts in terms of their CO2 emissions. A significant clustering pattern and positive spatial autocorrelation are revealed in the coal production-based carbon emissions in China. In addition, the spatial pattern of coal production-based CO2 emissions has an obvious central tendency and directional trend, and the ellipse direction is quite consistent with the Aihui–Tengchong Line. Our findings suggest that energy policy-makers should be concerned about the carbon emission effect when implementing regional coal development plans and actively guide the formation of a low-carbon spatial strategic pattern of coal production with a directional distribution of CO2 emissions perpendicular to the Aihui–Tengchong Line. [ABSTRACT FROM AUTHOR]

In view of the complexity of the energy system and its complex relationship with socio-economic factors, this study adopts the Long-range Energy Alternative Planning (LEAP) model, a technology-based, bottom-up approach, scenario-based analysis, to develop a systematic analysis of the current and future energy consumption, supply and associated Green House Gas (GHG) emissions from 2015 to 2050. The impact of various energy policies on the energy system in Hebei Province was analysed by considering four scenarios: a Reference Scenario (REF), Industrial Structure Optimization Scenario (ISO), Terminal Consumption Structure Optimization Scenario (TOS) and Low-carbon Development Scenario (LCD). By designing strategic policies from the perspective of industrial adjustment, aggressive energy structure policies and measures, such as the ISO and the TOS, and even more aggressive options, such as the LCD, where the percentage of cleaner alternative energy sources has been further increased, it has been indicated that energy consumption will have increased from 321.618 million tonnes of coal equivalent (Mtce) in 2015 to 784.88 Mtce in 2050 in the REF, with a corresponding increase in GHG emissions from 920.56 million metric tonnes (Mt) to 2262.81 Mt. In contrast, the more aggressive policies and strategies involved in the LCD, which combines the ISO with the policy-oriented TOS, can lower energy consumption by 50.82% and CO2 emissions by 64.26%. The results shed light on whether and how these scenarios can shape the energy-carbon emission reduction trajectories and develop the low-carbon pathways in Hebei Province. [ABSTRACT FROM AUTHOR]