An approach to carbon emissions prediction using generalized regression neural network improved by genetic algorithm

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhida Guo ◽  
Jingyuan Fu
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Total Carbon ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Target Values ◽  
Generalized Regression

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era, and actively responding to climate change policy. Through the analysis of the application of the generalized regression neural network (GRNN) in prediction, this paper improved the prediction method of GRNN. Genetic algorithm (GA) was adopted to search the optimal smooth factor as the only factor of GRNN, which was then used for prediction in GRNN. During the prediction of carbon dioxide emissions using the improved method, the increments of data were taken into account. The target values were obtained after the calculation of the predicted results. Finally, compared with the results of GRNN, the improved method realized higher prediction accuracy. It thus offers a new way of predicting total carbon dioxide emissions, and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

The Distribution of Teaching Staff in Colleges and Universities based on Neuropsychology

2021 ◽  
Vol 7 (5) ◽  
pp. 4682-4692
Author(s):  
Ruolin Yang ◽  
Dan Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Colleges And Universities ◽  
Quality Education ◽  
Generalized Regression Neural Network ◽  
Teaching Staff ◽  
Actual Situation ◽  
Teacher Resources ◽  
Calculation Process ◽  
Generalized Regression

Objectives: At present, quality education has gradually been recognized by the whole society, and a consensus has been reached on its importance, which has put forward stricter requirements for the distribution of faculty in universities. Methods: In this paper, based on neuropsychology, the distribution of teaching staff in colleges and universities was studied, and the model of talent evaluation and distribution was constructed. Results: Firstly, the generalized regression neural network was optimized by genetic algorithm. Then, the genetic algorithm’s generalized regression neural network calculation process was designed. Conclusion: Finally, with the example of teacher resources in a university, the algorithm in this paper was tested. The results show that the results of the generalized regression neural network optimized by genetic algorithm can match the actual situation very well, and the method is feasible with certain advantages.

scholarly journals The Paradoxical Relationship between Renewable Energy and Economic Growth: A Cross-National Panel Study, 1990-2013

2017 ◽  
Vol 23 (2) ◽  
pp. 540-564 ◽  
Author(s):  
Ryan P Thombs
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Total Carbon ◽  
Renewable Energy Consumption ◽  
Negative Effect ◽  
Cross National

This cross-national study employs a time-series cross-sectional Prais-Winsten regression model with panel-corrected standard errors to examine the relationship between renewable energy consumption and economic growth, and its impact on total carbon dioxide emissions and carbon dioxide emissions per unit of GDP. Findings indicate that renewable energy consumption has its largest negative effect on total carbon emissions and carbon emissions per unit of GDP in low-income countries. Contrary to conventional wisdom, renewable energy has little influence on total carbon dioxide emissions or carbon dioxide emissions per unit of GDP at high levels of GDP per capita. The findings of this study indicate the presence of a “renewable energy paradox,” where economic growth becomes increasingly coupled with carbon emissions at high levels of renewable energy, and the negative effect of economic growth on carbon emissions per unit of GDP lessens as renewable energy increases. These findings suggest that public policy should be directed at deploying renewable energy in developing countries, while focusing on non-or-de-growth strategies accompanied with renewable energy in developed nations.

scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

scholarly journals Truck Arrivals Scheduling with Vessel Dependent Time Windows to Reduce Carbon Emissions

2019 ◽  
Vol 11 (22) ◽  
pp. 6410 ◽  
Author(s):  
Mengzhi Ma ◽  
Houming Fan ◽  
Xiaodan Jiang ◽  
Zhenfeng Guo
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Time Window ◽  
Container Terminal ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Total Carbon ◽  
Queuing Model ◽  
Marine Container Terminal

Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A two-phase queuing model is established to describe the queuing process of trucks at gate and yard. An optimization model is established to assign time window and appointment quota for each vessel in a marine container terminal running a terminal appointment system (TAS) with VDTWs. The objective is to minimize the total carbon dioxide emissions of trucks and rubber-tired gantry cranes (RTGCs) during idling. The storage capacity constraints of each block and maximum queue length are also taken into consideration. A hybrid genetic algorithm based on simulated annealing is developed to solve the problem. Results based on numerical experiments demonstrate that this model can substantially reduce the waiting time of trucks at gate and yard and carbon dioxide emissions of trucks and RTGCs during idling.

scholarly journals The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

2022 ◽  
Vol 19 (2) ◽  
pp. 730
Author(s):  
Shihong Zeng ◽  
Gen Li ◽  
Shaomin Wu ◽  
Zhanfeng Dong
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Technology Innovation ◽  
Green Technology ◽  
Total Carbon ◽  
Marginal Rate ◽  
Spatial Spillover ◽  
Carbon Neutrality ◽  
The Impact

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

2017 ◽  
Vol 28 (5) ◽  
pp. 835-848
Author(s):  
Zhi-da Guo ◽  
Jing-Yuan Fu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Supply And Demand ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Railway Freight ◽  
Freight Volume ◽  
Construction Operation

Abstract Railway freight transportation is an important part of the national economy. The accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision-making of railways. Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume. The relation between some information is easily ignored when the traditional method of railway freight volume forecasting is used for prediction based on causality or time series. After analyzing the application status of the generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model using an improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of the GRNN, and then the optimal spread is used for forecasting in the GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared to the results of the GRNN, higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method and this improved method can provide a new approach for predicting the railway freight volume.

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