Grey BP Neural Network Research Articles

Short-term Power Load Forecasting Based on EMD-GWO-BP

Abstract The short-range prediction of electricity demand holds immense importance in the strategic planning and advancement of the energy sector. However, the actual load sequence data reflects multiple complex properties, such as nonlinearity, non-stationarity, and temporal variation. In order to accurately forecast the load, this paper presents a three-level hybrid integrated short-term load prediction method composed of empirical mode decomposition (EMD), Grey Wolf Optimization (GWO) and BP neural network (BP). EMD is used to decompose load data to obtain good power consumption characteristics; GWO is used to obtain the optimal weight and threshold required for BP prediction. The hybrid integration method (EMD-GWO-BP) was evaluated using the 2017 annual data of Chaoyang County, Liaoning Province. The EMD-GWO-BP method was compared with the other two mainstream coupling methods (BP, GWO-BP). The statistical analysis Indicates that the suggested method within this document shows better forecast precision on three standard scales of MAPE, MAD and RMSE, which reflects the advanced nature of this method.

Introduct Risk Prediction Protection Study Based on GA-BP and K-means Clustering

This paper mainly focuses on the risk management problems caused by frequent extreme weather events. Firstly, a grey bp neural network model is used as a heuristic optimization algorithm to construct an IWM model, which is used to decide whether insurance company provides insurance under extreme weather conditions. Secondly, based on the Comprehensive evaluation method (RSR), a DDI evaluation model was constructed to analyze the data of 100 buildings in 10 provinces in China to help developers evaluate the rationality of decisions. Finally, a PM evaluation model is constructed based on AHP and EWM-CRITIC, and K-means clustering is used to conduct comprehensive rating and grading evaluation of 30 low-insurance landmarks, and corresponding protection strategies are designed. The comprehensive application of these models provides useful suggestions and decision support for insurance company underwriting, real estate bidding, community building protection and so on.

Accurate measurement techniques and prediction approaches for the in-situ rock stress

The precise calculation and evaluation of the in-situ rock stress tensor is a crucial factor in addressing the major challenges related to subsurface engineering applications and earth science research. To improve the accuracy of in-situ stress measurement and prediction, an improved overcoring technique involving a measurement circuit, temperature compensation, and calculation method is presented for accurately measuring the in-situ rock stress tensor. Furthermore, an embedded grey BP neural network (GM–BPNN) model is established for predicting in-situ rock stress values. The results indicate that the improved overcoring technique has significantly improved the stress measurement accuracy, and a large number of valuable stress data obtained from many mines have proved the testing performance of this technique. Moreover, the mean relative errors of the prediction results of GM(0, 1) for the three principal stresses all reach 6–30%, and the accuracy of the model fails to meet the requirements. The average relative errors of the prediction results of the BPNN model are all less than 10%, and the model accuracy meets the requirements and has sufficient credibility. Compared with the GM and BPNN models, the embedded GM–BPNN model produces the best results, with mean relative errors of 0.0001–4.8338%. The embedded GM–BPNN model fully utilizes the characteristics of grey theory and BP neural network, which require a small sample size, weaken the randomness of the original data, and gradually approach the accuracy of the model, making it particularly suitable for situations with limited stress data.

Green technology innovation, trade deficit and carbon emission transfer in agriculture under the new “dual circulation” development pattern of China

Under the new development pattern of domestic and international “dual circulation” in China, studying the relationship among green technology innovation, agricultural trade deficit (ATD) and carbon emission transfer embodied in agricultural trade (ATCT) becomes crucial for achieving coordinated and unified high-quality economic development and efficient environmental protection. Based on this, the paper uses environmental input–output model, “two-stage” decoupling and recoupling model, panel vector auto-regressive model, spatial econometric model and “rolling” Grey-BP neural network prediction model to explore present status of ATD, ATCT and their relationship, driving role of green technology innovation, as well as whether that relationship will be improving in the future. The results reveal the following: (1) China's ATD and ATCT show an increasing trend year by year, and their inter-provincial differences are gradually significant over time. The overall relationship between ATD and ATCT does not display ideally, which makes it necessary to quickly transform that relationship into reasonable and ideal state. (2) Green technology innovation has significant positive “U-shaped” and inverted “U-shaped” local effects on ATD and ATCT respectively, with a reasonable range of ATD reduction while ATCT increase valuing from 0 to 0.290, and has significant positive “U-shaped” spatial spillover effect on both without any reasonable range. The short-term effect of green technology innovation on coordinated promotion between ATD and ATCT is superior to the long-term one. (3) Green technology innovation has a significant regional heterogeneity. Regional differences must be taken into consideration seriously, and the measures adapted to local conditions are crucial. The role of fundamental and progressive green technology innovation has heterogeneity, which means that targeted development and utilization of green technology products are important. (4) Regardless of the time horizon, the relationship between ATD and ATCT will improve, which can provide a key step for coordinated development of economy and environment in agricultural trade. Therefore, in order for China to implement and promote further opening-up, reduce agricultural trade dependence, ensure food security and important agricultural product supply, as well as achieve the unity of high-quality economic development and ecological efficient protection, it is necessary to reasonably reverse trade deficit and increase carbon emission transfer in agriculture.

Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Abstract Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

Prediction of Carbon Dioxide Emission in Anhui Province Based on Grey-BP Neural Network

Prediction of Carbon Dioxide Emission in Anhui Province Based on Grey-BP Neural Network

Research on the Evaluation of Training Effect of “Internet + Teaching” Based on Grey Correlation Analysis and BP Neural Network

Research on the Evaluation of Training Effect of “Internet + Teaching” Based on Grey Correlation Analysis and BP Neural Network

Demand forecasting of cold-chain logistics of aquatic products in China under the background of the Covid-19 post-epidemic era.

In the background of the post-epidemic era, the consumption demand and market scale of cold chain logistics in China are expanding, but there is still an obvious gap with developed countries. To complete the balance between the supply and demand for aquatic products and the rational allocation of logistics resources and promote the rapid development trend of aquatic product cold chain logistics, it is particularly important to forecast and analyze the demand for aquatic product cold chain logistics. This article selects six main factors that affect the demand for aquatic products in cold chain logistics, uses the traditional grey model and the grey-BP neural network model to simulate and predict the demand for aquatic products in cold chain logistics in China from 2012 to 2021, and compares and analyzes the simulation results. Generally speaking, the demand for aquatic products from Chinese residents is on the rise. In the simulation prediction process, the prediction error of the grey-BP neural network is reduced compared to the traditional grey model, and the processing ability of the nonlinear system is ideal. The results show that the grey-BP neural network model is an effective method to predict the demand for cold chain logistics of aquatic products. Finally, suggestions are made on the future development of aquatic cold chain logistics in the post-epidemic era from the economic, social, and environmental aspects, which provide valuable decision-making reference for the development of marine aquaculture enterprises and cold chain logistics industry.

CO2 emission forecasting based on nonlinear grey Bernoulli and BP neural network combined model

CO2 emission forecasting based on nonlinear grey Bernoulli and BP neural network combined model

Dynamic Prediction and Driving Factors of Carbon Emission in Beijing, China, under Carbon Neutrality Targets

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development.

Prediction of global temperature based on the BP neural networks

The problem of global warming is getting worse as global temperature has risen year by year to an alarming level. This paper is first based on historical data on global temperatures, two prediction models were developed using Gray Prediction Method and BP Neural Network model, whose results show that the root mean square error test value of BP neural network is about 0.03 lower than that of the gray prediction method (0.1627).Thus, the BP neural network prediction model is proved effective, and then the model was analyzed by gray correlation theory, which found that carbon dioxide concentration, forest cover area and human population are affecting the global temperature, and the Gray correlation of the fisrt two factors is as high as 0.8, which makes the two the main factors. In the end the paper gave measures to contain global warming.

Water Pollution Prevention and Prediction Based on Grey BP Neural Network Model

Water Pollution Prevention and Prediction Based on Grey BP Neural Network Model

Retracted: Highway Traffic Flow Prediction Model Construction Based on the Gray Theory and BP Neural Network.

[This retracts the article DOI: 10.1155/2022/1120491.].

Long‐Term Optimal Operation of the Cascade Hydro‐Wind‐Photovoltaic Hybrid System considering Transmission Section Constraints

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro‐wind‐photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long‐term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario‐generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro‐wind‐PV complementary operation; finally, the long‐term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption.

Establishment and Study of an Evaluation Model for the Exploitation Effect of University Scientific and Technological Patents

Due to the lack of sufficient understanding by the public of the process and mechanism of how university patents are exploited, the exploitation effect of university patents is not satisfactory. Therefore, it is necessary to effectively evaluate the exploitation effect of university patents in order to explore a reasonable path for and expand the role of university patent exploitation. Currently, there has been few research on how to evaluate the exploitation effect of university patents. To this end, this paper focuses on university scientific and technological patents and explores the effective evaluation method for the exploitation effect of these patents. First, market share, market benefit, patented technology maturity, enterprise nature and government support were selected as the evaluation indicators for the exploitation effect of the scientific and technological patents in colleges and universities in the study area. It was proposed that the five influencing factors and the exploitation effect of university scientific and technological patents interact with and influence each other. Then a combined model based on grey prediction GM (1,1) model and BP neural network model was constructed for the evaluation of the exploitation effect of scientific and technological patents in colleges and universities, which proved to have better prediction performance. The experimental results proved the effectiveness of the prediction model, and also verified the interactions between the five influencing factors and the exploitation effect of university scientific and technological patents.

Research on macroeconomic Forecasting model based on grey theory

In the modern economic innovation and development, the grey theory and BP neural network algorithm are used to build a new macroeconomic prediction model. On the one hand, the input data can be comprehensively preprocessed, on the other hand, the improved BP neural network algorithm can be used to predict the final results. This paper studies the feasibility and effectiveness of the new macroeconomic forecasting model based on the grey forecasting principle and BP neural network algorithm. The final results show that both grey prediction and BP algorithm can help the staff to predict and analyze the macro economy as soon as possible, so as to grasp more valuable data information and put forward targeted management decisions.

Research on macroeconomic Forecasting model based on grey theory

In the modern economic innovation and development, the grey theory and BP neural network algorithm are used to build a new macroeconomic prediction model. On the one hand, the input data can be comprehensively preprocessed, on the other hand, the improved BP neural network algorithm can be used to predict the final results. This paper studies the feasibility and effectiveness of the new macroeconomic forecasting model based on the grey forecasting principle and BP neural network algorithm. The final results show that both grey prediction and BP algorithm can help the staff to predict and analyze the macro economy as soon as possible, so as to grasp more valuable data information and put forward targeted management decisions.

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change.

In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

Highway Traffic Flow Prediction Model Construction Based on the Gray Theory and BP Neural Network.

The short-term traffic flow prediction and modeling of highways are the core content and important foundation of highway management decision-making support systems. It is of great significance to improving the level of highway management. Based on the macrodynamic traffic flow model, this article establishes a method for establishing a highway traffic flow prediction model based on the BP neural network theory and gray theory. We collected and carry out modeling and prediction of highway traffic flow data near a certain station. It is learned from the prediction results that the traffic flow prediction model based on the BP neural network and gray theory has a high degree of reliability.