scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

A time-varying grey Riccati model based on interval grey numbers for China's clean energy generation predicting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandang Guo ◽  
Yaqian Jing
Keyword(s):  
Clean Energy ◽  
Energy Generation ◽  
Random Disturbance ◽  
Time Varying ◽  
The Novel ◽  
Content Type ◽  
Time Varying Parameter ◽  
Number Sequences ◽  
Disturbance Term ◽  
Grey Number

PurposeIn order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.Design/methodology/approachBy combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.FindingsBased on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.Practical implicationsDue to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.Originality/valueThe main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.

Forecasting smog in Beijing using a novel time-lag GM(1,N) model based on interval grey number sequences

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jia Shi ◽  
Pingping Xiong ◽  
Yingjie Yang ◽  
Beichen Quan
Keyword(s):  
Time Lag ◽  
Upper And Lower Bounds ◽  
Simulation Accuracy ◽  
Content Type ◽  
Model Based ◽  
Proposed Model ◽  
Lag Effect ◽  
Number Sequences ◽  
The Government ◽  
Grey Number

PurposeSmog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.Design/methodology/approachThis paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.FindingsIn order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.Practical implicationsThe proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.Originality/valueBased on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

scholarly journals Indices of Bad Governance by African Political Leaders: The Case Study of Central African Republic, Democratic Republic of the Congo and Uganda

2021 ◽  
Vol 4 (1) ◽  
pp. 1506-1514
Author(s):  
Blaise Ngambinzoni Kombeto ◽  
Romain Bakola Dzango ◽  
Modeste Ndaba Modeawi ◽  
Gédéon Bongo Ngiala ◽  
Muhammad Ridwan ◽  
...  
Keyword(s):  
Democratic Republic ◽  
Modern Society ◽  
The Novel ◽  
Political Leaders ◽  
The People ◽  
Democratic State ◽  
Republic Of The Congo ◽  
The Government ◽  
The Individual

Marcel SONY LabouTansi, the author of the novel "The Shameful State", denounces the dictatorial system often practiced by most African leaders in the management of the "res publica". He paints the barbarity of man in relation to his fellow man. It also presents the duality between the traditional society characterized by democracy, peace ... and the modern society based on dictatorship in which the government behaves as a state, as absolute master, and the governed in the eternal "- mute", "voiceless". It invites the recipients to renounce to the bad principle in order to establish democracy, a system that respects the individual freedom of the people, that of human rights, of professional promotion for the harmonious development of a sovereign and democratic State. The novel "The Shameful State" unfolds the spiral of the unpleasant reign of a megalomaniacal, criminal and lustful president, Colonel Martillimi Lopez, who "shamefully" manages power and ends with the crying and gnashing of his constituents' teeth. After having committed: pedophilia, adultery, assassination of opponents, he was deposed by his relatives who created an insurrection and was forced to hand over power to civilians to return to his native village.

scholarly journals A Three-Stage Hierarchical Model for An Earthquake Shelter Location-Allocation Problem: Case Study of Chaoyang District, Beijing, China

2019 ◽  
Vol 11 (17) ◽  
pp. 4561 ◽  
Author(s):  
Xiujuan Zhao ◽  
Jianguo Chen ◽  
Wei Xu ◽  
Shiyan Lou ◽  
Peng Du ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Hierarchical Model ◽  
Particle Swarm ◽  
Economic Loss ◽  
Swarm Optimization ◽  
Evacuation Time ◽  
Modified Particle Swarm Optimization ◽  
The Government ◽  
Beijing China

Earthquakes are one type of natural disaster that causes serious economic loss, deaths, and homelessness, and providing shelters is vital to evacuees who have been affected by an earthquake. Constructing shelters with reasonable capacity in the right locations and allocating evacuees to them in a reasonable time period is one disaster management method. This study proposes a multi-objective hierarchical model with three stages, i.e., an immediate shelter (IS) stage, a short-term shelter (STS) stage, and a long-term shelter (LTS) stage. According to the requirements of evacuees of IS, STS, and LTS, the objective of both the IS and STS stages is to minimize total evacuation time and the objectives of the LTS are to minimize total evacuation time and to minimize total shelter area. A modified particle swarm optimization (MPSO) algorithm is used to solve the IS and STS stages and an interleaved modified particle swarm optimization algorithm and genetic algorithm (MPSO-GA) is applied to solve the LTS stage. Taking Chaoyang District, Beijing, China as a case study, the results generated using the model present the government with a set of options. Thus, according to the preferences of the government, the determination can be made regarding where to construct ISs, STSs, and LTSs, and how to allocate the evacuees to them.

Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters

2021 ◽  
pp. 1-12
Author(s):  
Pingping Xiong ◽  
Lushuang Xiao ◽  
Yuchun Liu ◽  
Zhuo Yang ◽  
Yifan Zhou ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Linear Time ◽  
Multiple Regression Model ◽  
Anhui Province ◽  
Grey Model ◽  
Traditional Model ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Based ◽  
Time Varying Parameters

Faced with serious growing global warming problem, it is important to predict carbon emissions. As there are a lot of factors affecting carbon emissions, a novel multi-variable grey model (GM(1,N) model) based on linear time-varying parameters discrete grey model (TDGM(1,N)) has been established. In this model, linear time-varying function is introduced into the traditional model, and dynamic optimization of fixed parameters which can only be used for static analysis is carried out. In order to prove the applicability and effectiveness of the model, this paper compared the model with the traditional model and simulated the carbon emissions of Anhui Province from 2005 to 2015. Carbon emissions in the next two years are also predicted. The results show that the TDGM(1,N) model has better simulation effect and higher prediction accuracy than the traditional GM(1,N) model and the multiple regression model(MRM) in practical application of carbon emissions prediction. In addition, the novel model of this paper is also used to predict the carbon emissions in 2018–2020 of Anhui Province.

Linear time varying model based model predictive control for lateral path tracking

2018 ◽  
Vol 75 (1/2/3/4) ◽  
pp. 1
Author(s):  
Charles Pinto ◽  
Ione Nieva ◽  
Sara Mata ◽  
Itziar Cabanes ◽  
Asier Zubizarreta
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Time ◽  
Path Tracking ◽  
Time Varying ◽  
Model Based
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