A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy

Luo, Xilin; Duan, Huiming; He, Leiyuhang

doi:10.1016/j.energy.2020.118085

Cited by 38 publications

(16 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the economic literature, forecasting models are also often used to predict nuclear and hydroelectric energy consumption. Applying the CCRGM (1,1) model, Luo et al (2020) predict that nuclear energy consumption will continue to increase over the decade 2019-2028 in North America, while hydroelectric energy consumption will peak and decline thereafter. Similarly, GRBM (1.1), Xiao et al (2020) show that China's coefficient of preference for total energy consumption (0.7103) is lower than that of India (0.8799) and that China's coefficient of preference for clean energy consumption (0.9665) is higher than that of India (0.7155).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the gap in enterprise access to renewable energy between rural and urban areas in Cameroon

Bodjongo

Ekome

Essomme

2021

Environmental Economics

View full text Add to dashboard Cite

Permanent access to energy is an essential pillar of economic development. However, there is a growing evidence that contemporary energy systems are not able to provide energy to the entire population on a sustainable basis and at affordable prices. In the face of these challenges, renewable energy can play an important role, especially in rural areas where access to centralized electricity grids is difficult. This paper aims to examine the access gaps of enterprises to renewable energy between rural and urban areas in Cameroon. The analysis is based on a sample of 209,482 enterprises, taken from the Second General Census of Enterprises in Cameroon (RGE-2) carried out by the National Institute of Statistics (NIS). The econometric estimations, obtained using the Blinder-Oaxaca decomposition, reveal that access rate to renewable energy for firms in rural areas is lower than that of firms located in urban areas. An increase in the level of education of the promoter of an enterprise, obtaining credit from banks, microfinance and savings, and the formalization of enterprises in rural areas can also contribute to reducing the gap in rural areas in terms of accessing to renewable energy. The discrimination suffered by rural enterprises related to the gender of entrepreneurs, the sector of activity, the business environment and professional experience tend to increase this gap. To reduce this gap, there is a need to promote access to finance for rural enterprises and their migration from the informal to the formal sector.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the gap in enterprise access to renewable energy between rural and urban areas in Cameroon

Bodjongo

Ekome

Essomme

2021

Environmental Economics

View full text Add to dashboard Cite

show abstract

“…From the above three types of models, it can be seen that the infectious disease model and time series model may need a large amount of data for accurate parameter identification. Literatures [12] , [13] , [14] point that the machine learning model may need a large amount of data for training and testing to achieve accurate results. In the research on the development trend of infectious diseases, Richards model [15] has been widely used in a variety of infectious diseases due to its advantages in processing saturated S-shaped data.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in systems that deal with incomplete information, the grey model stands out by virtue of its "simple model, strong adaptability, and easy parameter changes". It is widely used in energy, finance, transportation, environment, manufacturing, materials and other industries [ [12] , [13] , [14] , [20] , [21] , [22] , [23] , [24] , [25] ]. In the field of infectious diseases, the grey model is also widely used.…”

Section: Introductionmentioning

confidence: 99%

A novel grey model based on traditional Richards model and its application in COVID-19

Luo

Duan

2021

Chaos, Solitons & Fractals

Self Cite

View full text Add to dashboard Cite

Highlights A novel grey Richards model GERM(1,1, ) is proposed. The optimal nonlinear terms and background value of the novel model are determined by Genetic algorithm. The comparative study shows that the new model is superior to the other seven benchmark models. The predict the daily number of new confirmed cases of COVID-19 of four regions are projected.

show abstract

“…For example, because of the nonlinearity, uncertainty, and long memory characteristics of clean energy, Zhang and Mao et al combined the fractional derivative grey Bernoulli model and artificial intelligence model based on EEMD to predict the production and consumption of clean energy (Yonghong et al, 2020). Aiming at the S‐type characteristics of clean energy consumption trend, Luo and Duan improved the traditional grey Verhulst model by using the Riccati equation, constructed a grey prediction model based on the Riccati equation, which predicted the clean energy consumption of North America in the next decade (Luo et al, 2020). Instead, Wu et al developed a new grey Bernoulli model to forecast annual gas consumption (Wu et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent grey forecasting model based on periodic aggregation generating operator and its application in forecasting clean energy

Sui

Qian

2021

Expert Systems

View full text Add to dashboard Cite

Accurate prediction of long‐term and short‐term clean energy production is the basis for understanding short‐term clean energy supply capacity, long‐term clean energy development trend and evaluating the effect of energy policies. However, under the circumstances of the large time span, the insufficient data samples and the periodic characteristics of seasonal clean energy production make the traditional grey prediction model prone to produce forecasting deviations. Given this situation, a novel seasonal fractional‐order full‐order time power discrete grey prediction model is initially proposed to deal with long‐term clean energy production sequences featured with nonlinearity and periodicity. Based on the proposed model, we also propose a data‐based algorithm to select the model structure adaptively. To prove the practicability of the new model for nonlinear long‐term development trend, monthly periodic time series and quarterly periodic time series, this article uses the new model to predict annual hydropower capacity in North America, monthly natural gas production in China and quarterly solar power generation in China. And the prediction results are compared with the existing grey models and non‐grey prediction models. Different methods including GM (1,1), DGM (1,1), NGM (1,1), ARGM (1,1), ENGM (1,1), Verhulst, CCRGM (1,1), FOTP‐DGMr (1,1), PFSM (1,1), Holt‐winters model, SARIMA model, SGM, HP‐GM and DGGM are used as benchmarks. In experiments, the MAPE of the proposed model is 2.92%, 2.43%, and 7.87%, respectively. The results of empirical analysis indicate that the proposed model generally outperform the benchmark model as it can well capture nonlinear long‐term development trend and seasonal characteristics.

show abstract

A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy

Cited by 38 publications

References 38 publications

Analysis of the gap in enterprise access to renewable energy between rural and urban areas in Cameroon

Analysis of the gap in enterprise access to renewable energy between rural and urban areas in Cameroon

A novel grey model based on traditional Richards model and its application in COVID-19

Intelligent grey forecasting model based on periodic aggregation generating operator and its application in forecasting clean energy

Contact Info

Product

Resources

About