Energy Forecast Model Based on Combination of GM(1,1) and Neural Network

Liu, Ren-yuan; Zhang, Jue; Huang, Qiang; Lei, Bao-dong

doi:10.1109/cise.2009.5364394

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Rising production costs, changes in consumer demand, and intensified market competition have all highlighted the uncertainty of the peanut industry [1]. Accurate and effective forecast research on the production and consumption of peanuts in China will help enhance the certainty of production, help domestic peanut industry practitioners adjust the planting scale and distribution direction of peanuts, and help relevant government agencies adjust and formulate policies Responding to the ever-changing domestic and foreign peanut markets can promote the sustainable development of China's peanut industry [2].…”

Section: Topic Background and Research Significancementioning

confidence: 99%

Construction of Improved GM(1,1) Model and Its Use of Peanut Production and Consumption

Cai,

Zhao,

Zhang

2023

Advances in Transdisciplinary Engineering

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This paper aims at the influence of the disorder original data on the prediction accuracy of the GM(1,1) model, a waveform data generation method is proposed to make the disorder data become monotonically increasing order data. Then the initial condition of the time response function is reset according to the new information first principle, and the accuracy of the improved model is tested. The prediction accuracy shows that in terms of peanut yield, the posterior error ratio and average relative error obtained by using the improved GM(1,1) model are 0.117 and 0.0499, respectively, which are smaller than the posterior error ratio and average relative error of traditional models. In terms of consumption, the posterior error ratio predicted by using the improved GM(1,1) model is 0.078, which is smaller than the posterior error ratio of traditional models, which is 0.093. Compared to the traditional model, the improved GM(1,1) model can better predict China’s peanut production and consumption with higher prediction accuracy. This article uses an improved GM(1,1) model with high prediction accuracy, and China’s peanut production and consumption will continue to increase from 2023 to 2025.

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Section: Topic Background and Research Significancementioning

confidence: 99%

Construction of Improved GM(1,1) Model and Its Use of Peanut Production and Consumption

Cai,

Zhao,

Zhang

2023

Advances in Transdisciplinary Engineering

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“…Some use BPNN and GM, respectively, to get the forecasting results and then combine those results according to the weights [8], which may be lack of some evidence of weights. Some use the grey numbers to improve the BPNN but need complicated deduction [9].…”

Section: Introductionmentioning

confidence: 99%

A grey theory based back propagation neural network model for forecasting urban water consumption

Wang

Jiang

2014

Proceedings of the 33rd Chinese Control Conference

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Forecasting urban water consumption is a complicated task due to its unavoidable huge fluctuation caused by uncertain factors. Back propagation neural network (BPNN) is known for its strong ability to deal with nonlinear problems but is limited by the requirement for large samples and relatively high computation complexity, while grey theory has advantages such as requiring little samples and easy modeling and computing. Therefore, a combined grey theory and BPNN model named GM-BPNN is proposed is proposed to forecast urban water consumption in Hangzhou. Simulation results show that GM-BPNN can reduce the value of mean absolute percentage error (MAPE) by 6.25% and 4.62% compared with GM (1,1) and original BPNN which means GM-BPNN achieves higher prediction accuracy.

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“…Yuchao, Zhongfu T. et al [19] applied the GM (1,1) model in the grey model to analyze the relationship between national economic growth and energy consumption and constructed a function to predict the changes in energy consumption and trends in 2006-2010 in China. Liu R Y, Zhang J, Qiang H, et al [20]. obtained a more accurate prediction of the total energy consumption by establishing a combined model which combined the grey prediction method with the BP neural network; Yan X. and Mu L. [21] applied the genetic algorithm to improve the model based on the grey prediction model theory and then obtained the optimized prediction model by actual case analysis.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

Zhao

Fan

2019

Sustainability

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In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region.

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Energy Forecast Model Based on Combination of GM(1,1) and Neural Network

Cited by 7 publications

References 3 publications

Construction of Improved GM(1,1) Model and Its Use of Peanut Production and Consumption

Construction of Improved GM(1,1) Model and Its Use of Peanut Production and Consumption

A grey theory based back propagation neural network model for forecasting urban water consumption

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

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