Xin Ma

Southwest University of Science and Technology · School of Science

Natural gas is playing an important role in the reconstruction of the energy system of China. Natural gas supply and consumption indicators forecasting is an important decision-making support for the government and energy companies, which has attracted considerable interest from researchers in recent years. In order to deal with the more complex fe...

Purpose Considering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can p...

Natural gas is believed to be a critical transitional energy source. However, natural gas pipelines, once failed, will contribute to a large amount of greenhouse gas (GHG) emissions, including methane from uncontrolled natural gas venting and carbon dioxide from flared natural gas. However, the GHG emissions caused by pipeline incidents are not inc...

Energy forecasting based on univariate time series has long been a challenge in energy engineering and has become one of the most popular tasks in data analytics. In order to take advantage of the characteristics of observed data, a partially linear model is proposed based on principal component analysis and support vector machine methods. The prin...

With the continuous depletion of global fossil energy, optimizing the energy structure has become the focus of attention of all countries. With the support of policy and finance, renewable energy occupies an important position in the energy structure of the USA. Being able to predict the trend of renewable energy consumption in advance plays a vita...

Natural gas is playing a key role in the Carbon Neutral path, which is clean and abundant. However it is difficult to collect sufficient data of urban natural gas consumption in China, and such data sets often present high nonlinearity and complex features, making it difficult to make accurate forecasts for the mid-small cities based on small sampl...

The least squares support vector machine (LS-SVM) is an effective method to deal with classification and regression problems and has been widely studied and applied in the fields of machine learning and pattern recognition. The learning algorithms of the LS-SVM are usually conjugate gradient (CG) and sequential minimal optimization (SMO) algorithms...

Renewable energy has made a significant contribution to global power generation. Therefore, accurate mid-to-long term renewable energy generation forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, a novel structure adaptive new information priorit...

With worldwide activities of carbon neutrality, clean energy is playing an important role these days. Natural gas (NG) is one of the most efficient clean energies with less harmful emissions and abundant reservoirs. This work aims at developing a swarm intelligence-based tool for NG forecasting to make more convincing projections of future energy c...

Energy production and conversion have a significant impact on the economic development of all countries in the world. China’s energy production and conversion are large. Therefore, accurate mid-to-long term China’s energy production and conversion forecasting is becoming more and more important for integrating energy systems and energy strategic pl...

[https://authors.elsevier.com/a/1fFfx15eif4SbN] Building operations will be the most critical step in completing the "last mile" of global carbon neutrality. To seek the best practical path to decarbonize commercial building operations, this study assesses the decarboniza-tion progress of commercial building operations in 16 countries over the last...

Wave energy flux is a critical index of available wave energy in a region. The short-term wave energy flux prediction is conducive to implementing marine energy generation management. Due to the high degree of nonlinearity in the time series, it is challenging to achieve the accurate prediction of the short-term wave energy flux. The existing predi...

Coal-to-gas switching in urban areas plays an important role in accelerating the pace of carbon neutrality. Accurate urban gas load forecasting is beneficial in balancing the peak–valley difference and achieving high-efficiency gas utilization. This work aims to develop a new method based on Tanimoto kernel-based nonlinear autoregressive (NAR) mode...

China's total energy consumption and production ranks first in the world. However, China's energy structure is not perfect. Therefore, accurate prediction of future energy trends is of great significance for the Chinese government to adjust the energy structure. Therefore, this paper proposes a novel structural adaptive grey model FCSAGM(p, 1) with...

Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model t...

An accurate forecast of the area of drought disaster is vitally important for the government to take appropriate measures to prevent disaster. In the current study, a new conformable fractional discrete grey model is applied to study the trend of the area affected by drought disasters. Firstly, the new model, abbreviated as CFDGM(1,1), is proposed...

Crude oil resources are related to all aspects of people's life, and play a vital role in the development of the national economy. Using nonlinear discrete data to reasonably predict crude oil production can help the government adjust energy structure and formulate energy development strategy, which has great practical significance. In this paper,...

Building operations will be the most critical step in completing the “last mile” of global carbon neutrality. To seek the best practical path to decarbonize commercial building operations, this study assesses the decarbonization progress of commercial building operations in 16 countries over the last two decades considering socioeconomic, technical...

Energy affects the stable and sustainable development of social economy. Energy prediction plays an important role in the process of China’s energy market transformation. Scientific and reasonable energy predicting method can help government to make decisions effectively, and then adjust energy structure and industrial layout. The energy field is f...

With the increasing power consumption in China and the urgent demand for environmental protection, promoting the development of clean energy power generation industry is the only way to optimize the energy power generation structure. It is very important to effectively predict the development trend of China's clean energy power generation system wi...

Based on particle swarm optimization (PSO), a new exponential time delay fraction order grey prediction model is proposed in this paper. Firstly, the original data is preprocessed by fractional-order accumulation; on the basis of fractional-order accumulation, it is proved that the initial value of the original sequence satisfies the fixed point th...

A timely analysis for carbon emission reduction in buildings is an effective global response to the crisis of climate change. The logarithmic mean Divisia index (LMDI) decomposition analysis approach has been extensively used to assess the carbon emission reduction potential of the buildings sector. In order to simplify the calculation process and...

(https://authors.elsevier.com/c/1d-Pe15eif0g0d) The growing energy consumption in commercial building operations has slowed the pace of carbon reduction in the building sector, which has hindered China’s movement towards peaking emissions by 2030 and carbon neutrality by mid-century. Considering the technical, socio-economic, climatic, and behavior...

Please cite this upcoming study as: Xiang, X., Ma, X., Ma, Z., Ma, M. & Cai, W. (2022). Python-LMDI: A tool for index decomposition analysis of building carbon emissions. Buildings, 12(1), 83; DOI: https://doi.org/10.3390/buildings12010083

The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algor...

Compared to fossil fuels, natural gas is cleaner energy, which has developed rapidly in recent years. Studying the urban supply of natural gas has implications for the development of natural gas. In this paper, the new information priority accumulation method is integrated into the grey forecasting model with the hyperbolic sinusoidal driving term,...

To further improve the effectiveness and precision of multivariate time series forecasting, a conformable fractional derivative multivariate grey system model is proposed in this work. Firstly, the general solution of the model is deduced by the grey theory, the definition of the conformable fractional derivative, the theory of ordinary differentia...

Natural gas is one of the main energy resources for electricity generation. Reliable forecasting is vital to make sensible policies. A randomly optimized fractional grey system model is developed in this work to forecast the natural gas consumption in the power sector of the United States. The nonhomogeneous grey model with fractional-order accumul...

Electricity consumption is one of the most important indicators reflecting the industrialization of a country. Supply of electricity power plays an import role in guaranteeing the running of a country. However, with complex circumstances, it is often difficult to make accurate forecasting with limited reliable data sets. In order to take most advan...

The outbreak of coronavirus disease 2019 (COVID-19) has had a considerable impact on every industrial sector. As a pillar of economic development, the energy sector is experiencing difficult times during the global pandemic. This paper reviews the impact of the pandemic on the global energy sector in terms of demand, price, employment, government p...

Most machine learning models are essentially “Black-box” models, of which the performance heavily relies on large scale data sets. In this work the idea of “Grey-box” modelling is adopted in order to take most advantage of known information represented by deterministic structure, and then the neural grey system model is developed. Levenberg-Marquar...

Photovoltaic engineering is one of the most important ways for utilizing solar energy. With fast development and large investment, the photovoltaic market has become more complex, leading to less reasonable samples for accurate forecasting. In this work, a time-delayed power effect with high flexibility is considered to develop a new grey system mo...

This work proposes a novel Hausdorff fractional NGMC(p,n) prediction model based on the NGMC(1,n) model. The new model combines the Hausdorff fractional accumulation operator and the Grunwald-Letnikov fractional derivative with more freedom and simpler in calculations; the time response function and recurrence expressions of the new model are deduc...

Composite material property testing usually requires multiple experiments if multiple variables, is time-consuming. The practice in many fields indicates that machine learning models have great potential in solving this problem because they can predict the material properties through the existing data. In this paper, a hybrid model combining multi-...

Nonlinear grey Bernoulli multivariate model NGBMC (1, n) is known as a novel forecasting model for nonlinear time series with small samples. However, ill-posed problem would make it less efficient and even cause large errors. In order to improve its generality, a hybrid method combining Elastic Net and multi-objective optimization is introduced in...

The accurate prediction of energy price is critical to the energy market orientation, and it can provide a reference for policymakers and market participants. In practice, energy prices are affected by external factors, and their accurate prediction is challenging. This paper provides a systematic decade review of data-driven models for energy pric...

Electricity consumption has been affected due to worldwide lockdown policies against COVID-19. Many countries have pointed out that electricity supply security during the epidemic is critical to ensuring people’s livelihood. Accurate prediction of electricity demand would act a more important role in ensuring energy security for all the countries....

In the multi-variable grey forecasting model GM(1,N), the extreme value of the independent variable is one of the important factors that affect the simulation and prediction results of the dependent variable. In this study, a smooth generation method was used to weaken the influence of the extreme value on the performance of GM(1,N), and a novel mu...

Purpose PM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it...

Solar energy as one type of renewable energy is the cleanest and most abundant energy source available. It is mainly used for photovoltaics, solar heating and cooling, and solar power generation. With the crisis of energy and environment, the solar energy generation is becoming a research hotspot in clean energy production. In this paper, the solar...

Carbon dioxide transport plays a crucial role in carbon capture and storage systems. As an economical and convenient carrier, pipelines have huge advantages in the transport of carbon dioxide. In this paper, the development of carbon dioxide transport via pipeline is systematically reviewed from four aspects: pipeline design, process, risk and safe...

Fast-growing carbon emissions from the residential building sector are a hindrance for China to achieve its 2030 emission peak goal. To identify future low carbon roadmaps of residential buildings, this study is the first to assess the historical carbon mitigation and simulate the energy and emission peaks of China’s residential building sector usi...

Improving the proportion of natural gas consumption of the manufacturing industry would make significant contributions to the low-carbon and sustainable development of China, which is one of the largest manufacturers in the world. However, it is very difficult to catch the trend of natural gas consumption of the concerning manufacturing industry as...

Discharge for wastewater treatment plays a key role in improving the water quality, thereby guaranteeing living quality of citizens. With high-speed economics growth and economics reforming, total amount of China's discharge of wastewater treatment is sharing high uncertainty, leading to many difficulties in accurate forecasts of discharge of waste...

Tight gas, shale gas and coalbed gas are recognized as the three sources of unconventional natural gas in the world. Currently, China’s tight gas production is at an absolute advantage. Hence, a reasonable prediction of tight gas production is of great value to China’s government in formulating energy policies. In this study, the data characteristi...

The development of the wind power market has led various countries to begin shifting the construction of wind farms from land to offshore. Accurately predicting the short-term wind power of an offshore wind farm is significant for preventive control and scheduling. This paper proposes a novel two-stage hybrid model to predict short-term wind power....

This paper studies the China’s oil consumption and the China’s nuclear energy consumption by a grey Riccati model. The newly developed model is analysed by the trapezoidal formula of definite integrals, the theory of ordinary differential equations and the grey technique. And some special cases including the GM(1,1) model, the grey Verhulst model a...

Accurate prediction of building energy consumption is crucial for building energy management. However, the building energy consumption is affected by many factors and shows obvious nonlinear characteristics in the time series, which is difficult to predict. In this work, a novel hybrid model is proposed for predicting short-term building energy consum...

This paper studies the China’s oil consumption and the China’s nuclear energy consumption by a grey Riccati model. The newly developed model is analyzed by the trapezoidal formula of definite integrals, the theory of ordinary differential equations and the grey technique. And some special cases including the GM(1,1) model, the grey Verhulst model a...

Some characteristics of mean sensitivity and Banach mean sensitivity using Furstenberg families and inverse limit dynamical systems are obtained. The iterated invariance of mean sensitivity and Banach mean sensitivity are proved. Applying these results, the notion of mean sensitivity and Banach mean sensitivity is extended to uniform spaces. It is...

An efficient optimization and design method has been proposed and developed for Enriched-gas Water-Alternating-Gas (EWAG) injection process. The proposed technique is able to quantitatively determine the sizes of the enriched-gas slug and water slug for each cycle of the water-alternating-gas (WAG) injection process, as well as the total number of...

Water resources are the foundation of people’s life and economic development, and are closely related to health and the environment. Accurate prediction of water quality is the key to improving water management and pollution control. In this paper, two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate sho...

Abstract It is well known that differential equations with piecewise constant arguments is a class of functional differential equations, which has fascinated many scholars in recent years. These delay differential equations have been successfully applied to diverse models in real life, especially in biology, physics, economics, etc. In this work, w...

The structure defect of the traditional grey Verhulst model is a key factor leading to its unstable performance. A new-structure grey Verhulst model (N_Verhulst) was proposed by introducing a new non-homogeneous exponential function. The N_Verhulst model has a better structure and stronger modeling ability; Meanwhile, it overcomes the shortcomings...

This paper investigates the natural gas consumption of the United States, Germany, the United Kingdom, China and Japan by a new Grey Bernoulli model. Analytical formulations of the time response function, restored values, and linear parameters estimation are derived. Further, the nonlinear parameter is determined by the particle swarm optimization...

Motivated by reducing carbon emissions, carbon trading market have been opened to promote environmental protection. Accurate carbon trading volume and price forecasts have far-reaching implications for environmental and energy policy formulation. As the country with the most massive carbon emissions in the world, China’s carbon price trends, carbon...

Nowadays, climate change is one of the most important global issues to the international community. And nearly thirty kinds of greenhouse gases have been found in the atmosphere, of which the carbon dioxide plays a crucial role. In this paper, the carbon dioxide emissions of BRICS (Brazil, Russia, India, China and South Africa) countries are invest...

Natural gas (NG) is a vital energy in the energy structure transition, and its consumption prediction is a significant issue in energy structure management and energy security. As the second largest energy consumer and producer in the world, the status of NG in the United States (US) energy system has been increasing since the “An America First Ene...

Carbonate reservoir is one of the important reservoir in the world. Because of the characteristics of carbonate reservoir, horizontal well and acid fracturing have become a key technology for efficiently developing carbonate reservoir. Establishing corresponding mathematical models and analyzing transient pressure behaviors of this type of well‐res...

GM(1,1) is a univariate grey prediction model with incomplete structural information, in which the real number form of the simulation or prediction data does not conform to the Nonuniqueness Principle of Grey theoretical solution. In light of the network model of GM(1,1), the connotation of grey action quantity is systematically analyzed and the in...

Wind energy makes a significant contribution to global power generation. Predicting wind turbine capacity is becoming increasingly crucial for cleaner production. For this purpose, a new information priority accumulated grey model with time power is proposed to predict short-term wind turbine capacity. Firstly, the computational formulas for the ti...

The fractional order grey models have appealed considerable interest of research in recent years due to its high effectiveness and flexibility in time series forecasting. However, the existing fractional order accumulation and difference are computationally complex, which leads to difficulties for theoretical analysis and applications. In this pape...

Wind energy is one of the most important renewable resources and plays a vital role in reducing carbon emission and solving global warming problem. Every country has made a corresponding energy policy to stimulate wind energy industry development based on wind energy production, consumption, and distribution. In this paper, we focus on forecasting...

In this note, it is shown that there exist two non-syndetically sensitive cascades defined on complete metric spaces whose product is cofinitely sensitive, answering negatively the Question 9.2 posed in Miller and Money (2017) [12]. Moreover, it is shown that there exists a syndetically sensitive semiflow (G,X) defined on a complete metric space X...

Energy consumption plays a key role in economics development for all countries. Catching the future trend of energy consumption is very important for the governments and energy companies. In this paper, the primary energy consumption of Saudi Arabia, India, Philippines and Vietnam are systematically studied by various forecasting models. Based on t...

Air pollution forecasting plays an important role in helping reduce air pollutant emission and guiding people's daily activities and warning the public in advance. Nevertheless, previous articles still have many shortcomings, such as ignoring the importance of outlier point detection and correction of original time series, and random initial parame...

Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy com...