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China's total energy consumption and production rank first in the world. However, China's energy structure is not reasonable. Therefore, accurate prediction of future energy trends is of great significance for the Chinese government to adjust the energy structure. In this paper, we propose an optimized Grey Euler model with polynomial term, which is abbreviated as OSGEM(1,1,N), to forecast the total energy consumption and production of China in comparison with the commonly used prediction models. The data from 2002 to 2018 are used to simulate the parameters in the proposed model, and the data from 2019 to 2021 are used to test the improved approach. The results show that the OSGEM(1,1,N) model outperforms the other models. Finally, the OSGEM(1,1,N) model is used to forecast the total energy consumption of China from 2022 to 2025 and different results from the previous research results have been obtained. [ABSTRACT FROM AUTHOR]

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China's natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China's natural gas demand in the next 10 years. The results show that the annual growth rate of China's natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 1011 m3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China's natural gas demand from 2023 to 2033, and provides decision-making support for China's energy structure adjustment and natural gas import trade. [ABSTRACT FROM AUTHOR]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. [ABSTRACT FROM AUTHOR]

This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19. [ABSTRACT FROM AUTHOR]

Usually, the rational polynomial coefficient (RPC) model of spaceborne synthetic aperture radar (SAR) is fitted by the original range Doppler (RD) model. However, the radar signal is affected by two-way atmospheric delay, which causes measurement error in the slant range term of the RD model. In this paper, two atmospheric delay correction methods are proposed for use in terrain-independent RPC fitting: single-scene SAR imaging with a unique atmospheric delay correction parameter (plan 1) and single-scene SAR imaging with spatially varying atmospheric delay correction parameters (plan 2). The feasibility of the two methods was verified by conducting fitting experiments and geometric positioning accuracy verification of the RPC model. The experiments for the GF-3 satellite were performed by using global meteorological data, a global digital elevation model, and ground control data from several regions in China. The experimental results show that it is feasible to use plan 1 or plan 2 to correct the atmospheric delay error, no matter whether in plain, mountainous, or plateau areas. Moreover, the geometric positioning accuracy of the RPC model after correcting the atmospheric delay was improved to better than 3 m. This is of great significance for the efficient and high-precision geometric processing of spaceborne SAR images. [ABSTRACT FROM AUTHOR]

When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations (PM2.5) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons. [ABSTRACT FROM AUTHOR]

The government of China is now trying to use natural gas as partial substitute of other fuels, such as coal and gasoline. Developing the natural gas industry has been written in the 13th Five Year Plan of China, which is the most important official policy of China. Predicting the natural gas consumption comes to be very important for the government and energy companies to make decisions. In this paper, we propose a novel time-delayed polynomial grey prediction model, which is abbreviated as TDPGM(1, 1), based on the grey system theory to forecast the natural gas consumption of China in comparison with the commonly used prediction models. The data from 1995 to 2004 are used to build the prediction models, and the data from 2005 to 2013 are used to assess the modelling accuracy. The results show that the TDPGM(1, 1) model outperforms the other models. Finally the TDPGM(1, 1) model is used to forecast the natural gas consumption of China from 2014 to 2020 and different results from the previous research results have been obtained. [ABSTRACT FROM AUTHOR]

• A novel discrete GM(2,1) model with a polynomial term is proposed. • The generalization and adaptability of the proposed model were validated from the theoretical and practical perspectives. • The newly-designed model is applied to predict per capita living electricity consumption. The forecast of electrical energy demand has played an increasingly relevant role in sustainable electrical power system. This paper develops a new method for forecasting China's per capita living electricity consumption by grey modelling technique. Considering the multiple and mixed change patterns, a novel discrete grey model with polynomial term (abbreviated as DGM(2,1, kn)) is proposed in this study. Firstly, the polynomial term is introduced into the discrete DGM(2,1) model. Secondly, the Tikhonov regularization method is employed to solve the overfitting problem. Lastly, two published cases and China's per capita living electricity consumption are used to validate the generalization and adaptability of the newly-designed model. The numerical results of such experiments show that the proposed model outperforms other competitive models in terms of accuracy level. Therefore, the projections of China's per capita living electricity consumption in 2020 and 2025 have been made for providing a solid reference for the formulation of electrical power strategies. [ABSTRACT FROM AUTHOR]

Clean energy, represented by hydropower, wind power, and nuclear power, plays a crucial role in addressing the issue of power shortage and promoting the transition of the energy structure. Scientific and accurate forecasting of clean energy generation volume can provide the necessary reference for relevant authorities to formulate reasonable energy policies. This paper proposes a novel fractional time-delay polynomial discrete grey model, FTDP-DGM(1,1), for forecasting clean energy generation volume in China. Firstly, both the structural parameters and the time response function are derived directly from the discrete equation. Secondly, the genetic algorithm is employed to determine the optimal value of the nonlinear parameter in the model, with the optimization objective of minimizing the mean absolute percentage error. Then, the proposed model is applied to the simulation and prediction of hydro, wind, and nuclear energy generation volume in China. The results show the validity and superiority of the model. Finally, out-of-sample forecasts are performed, and the forecasting results show that the proposed model has good performance with values of 1.29%, 2.94%, and 2.32% for mean absolute percentage error(MAPE), respectively. It is expected that in 2021–2025, China's hydro, wind, and nuclear power generation volume will continue to grow at a rate of 1.45%, 10.74%, and 10.66% respectively. Based on the forecast results, policy recommendations are suggested. [ABSTRACT FROM AUTHOR]

Abstract: Accurate sensors are the prerequisite for any building control and management operation to succeed. However, due to the lack of awareness of the importance, sensors are often out of calibration. This paper proposes a preventive approach to determine sensor maintenance requirements, which aims to explicitly quantify the importance of sensors and determine a proper calibration frequency for sensors with high importance. The approach takes account of the sensor bias growth mechanism and relates the sensor importance to a user-defined building performance index. The sensor importance is evaluated under a stochastic framework, which essentially is the way in which sensor bias grows. The calibration frequency is identified using a polynomial function which describes the degradation rate of the building performance index. The proposed approach is applied to a building located in Hong Kong. Results show that the proposed approach can effectively identify important sensors and their calibration frequency. [Copyright &y& Elsevier]

Abstract: Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe''s probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency. [Copyright &y& Elsevier]

This paper presents a synthesis technique of the dual offset shaped reflector antenna using Differential Evolution with Global and Local neighborhoods (DEGL) algorithm. The main reflector and subreflector are parameterized by Zernike polynomial expansion. With these expansion coefficients, the antenna is optimized to achieve a contoured beam covering the China territory in Ku-band. The performance of the antenna is calculated by Physical Optics (PO) and Physical Theory of Diffraction (PTD) techniques. The calculated results are validated by the commercial software GRASP9.3.01. [ABSTRACT FROM AUTHOR]

Chang'E-5 (CE-5) is China's first lunar sample return mission. This paper focuses on the trajectory determination of the CE-5 lander and ascender during the landing and ascending phases, and the positioning of the CE-5 lander on the Moon. Based on the kinematic statistical orbit determination method using B-spline and polynomial functions, the descent and ascent trajectories of the lander and ascender are determined by using ground-based radiometric ranging, Doppler and interferometry data. The results show that a B-spline function is suitable for a trajectory with complex maneuvers. For a smooth trajectory, B-spline and polynomial functions can reach almost the same solutions. The positioning of the CE-5 lander on the Moon is also investigated here. Using the kinematic statistical positioning method, the landing site of the lander is 43.0590°N, 51.9208°W with an elevation of −2480.26 m, which is less than 200 m different from the LRO (Lunar Reconnaissance Orbiter) image data. [ABSTRACT FROM AUTHOR]