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Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R. China) as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold-Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market. [ABSTRACT FROM AUTHOR]

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China's electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China's power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China's renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China's renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030. [ABSTRACT FROM AUTHOR]

The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two commonly used ML methods, namely the artificial neural network (ANN) and support vector machine (SVM), confirms the superiority of GBDT in terms of generalization capability and robustness. Furthermore, relative importance scanning and univariate analysis based on the constructed GBDT model were conducted, which showed that the fixed carbon and ash contents are primary factors that significantly affect the adsorption isotherms for the coal samples in this study. [ABSTRACT FROM AUTHOR]

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects the lamination degree and the mechanical properties of the shale, which is critical for the effective implementation of hydraulic fracturing. The calculated elastic anisotropy of the shale provides more accurate models for seismic modeling and inversion. The obtained bedding fracture parameters provide insights into reservoir permeability. Therefore, the proposed method provides valuable information for identifying favorable oil zones in the study area. [ABSTRACT FROM AUTHOR]

The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021. [ABSTRACT FROM AUTHOR]

With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China's coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China's coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations. [ABSTRACT FROM AUTHOR]

The design of a four-bed three-stage adsorption cycle has been proposed to reduce the volume of the six-bed three-stage adsorption cycle. A simulation model for the proposed innovative cycle was developed to analyse the influence of cycle time on the system performance identifying the specific cooling power (SCP) and coefficient of performance (COP). A particle swarm optimization (PSO) technique was used to optimize the cycle time enabling us to maximize the SCP. PSO results showed that the optimal cycle time was decreased with heat source temperature and SCP value was proportional to heat source temperature. It was found that the proposed cycle could be driven by waste heat as low as 40 °C, along with coolant at 30 °C. Comparative study of optimized result indicated that the proposed cycle increased the performance significantly over a whole range of temperatures from 40 to 70 °C and reduced two adsorbent beds, compared to the six-bed three-stage cycle. [ABSTRACT FROM AUTHOR]

In order to reduce the harm of induced earthquakes in the process of geothermal energy development, it is necessary to analyze and evaluate the induced earthquake risk of a geothermal site in advance. Based on the tectonic evolution and seismogenic history around the Qiabuqia geothermal field, the focal mechanism of the earthquake was determined, and then the magnitude and direction of in-situ stress were inversed with the survey data. At the depth of more than 5 km, the maximum principal stress is distributed along NE 37°, and the maximum principal stress reaches 82 MPa at the depth of 3500 m. The induced earthquakes are evaluated by using artificial neural network (ANN) combined with in-situ stress, focal mechanism, and tectonic conditions. The predicted earthquake maximum magnitude is close to magnitude 3. [ABSTRACT FROM AUTHOR]