Forecast of clean energy generation in China based on new information priority nonlinear grey Bernoulli model.

The forecast of clean energy power generation is of major prominence to energy structure adjustment and the realization of sustainable economic development in China. In order to scientifically predict clean energy power generation data, a structure-adaptive nonlinear grey Bernoulli model submitted to the new information priority criterion (abbreviated as IANGBM) is established. Firstly, an improved conformable fractional accumulation operator that conforms to the priority of new information is proposed, which can effectively extract the information from small samples. Then, IANGBM is derived from the Bernoulli differential equation, and the perturbation bound theory proves that this model is suitable for the analysis of small sample data. In addition, the grey wolf optimization algorithm is utilized to optimize the model parameters to make the model more adaptable and generalized. To verify the superiority of the model, two cases consisting of wind and nuclear power generation prediction are implemented by comparing eight benchmark models involving IANGBM, GM, FGM, FANGBM, LR, SVM, BPNN, and LSTM. The experiment results demonstrate that the proposed model achieves higher prediction accuracy compared to the other seven competing models. Finally, the future nuclear and wind power generation from 2023 to 2030 are predicted by adopting the IANGBM(1,1) model. For the next 8 years, nuclear power generation will maintain stable development, while wind energy power generation will grow rapidly.

A novel fractional discrete grey model with variable weight buffer operator and its applications in renewable energy prediction.

With the continuous depletion of global fossil energy, optimizing the energy structure has become the focus of attention of all countries. With the support of policy and finance, renewable energy occupies an important position in the energy structure of the USA. Being able to predict the trend of renewable energy consumption in advance plays a vital role in economic development and policymaking. Aiming at the small and changeable annual data of renewable energy consumption in the USA, a fractional delay discrete model of variable weight buffer operator based on grey wolf optimizer is proposed in this paper. Firstly, the variable weight buffer operator method is used to preprocess the data, and then, a new model is constructed by using the discrete modeling method and the concept of fractional delay term. The parameter estimation and time response formula of the new model are deduced, and it is proved that the new model combined with the variable weight buffer operator satisfies the new information priority principle of the final modeling data. The grey wolf optimizer is used to optimize the order of the new model and the weight of the variable weight buffer operator. Based on the renewable energy consumption data of solar energy, total biomass energy and wind energy in the field of renewable energy, the grey prediction model is established. The results show that the model has better prediction accuracy, adaptability and stability than the other five models mentioned in this paper. According to the forecast results, the consumption of solar and wind energy in the USA will increase incrementally in the coming years, while the consumption of biomass will decrease year by year.

Forecasting greenhouse gas emissions with the new information priority generalized accumulative grey model.

Air pollution and other environmental problems caused by excessive emissions of greenhouse gases have become a comprehensive problem requiring joint global treatment. To consider the characteristics of different regions and different countries in terms of greenhouse gas emissions for accurate prediction, a new information priority generalized accumulative grey model (NIPGAGM(1,1,k)) is proposed. The new model maintains the structure of the traditional grey model and the basic result characteristics of its features. This research further deduces the calculation formulas of the model's time response sequence and parameter estimation. Furthermore, an optimization model is established to search the parameters using a detailed optimization algorithm. The optimization value of the new model is determined by the intelligent optimization algorithm. Then, the new model is applied to the greenhouse gas emission prediction of the Shanghai Cooperation Organization (SCO) member states. The numerical results are compared with those of existing models. Finally, according to the forecast results of greenhouse gas emissions in these regions, reasonable suggestions for clean energy production are proposed.

Forecasting carbon emissions using MGM(1,m|λ,γ) model with the similar meteorological condition.

Carbon emission is a common concern of the international community and effectively predicting its future trend is necessary for emission reduction planning. Considering that the change trend of carbon emissions is unstable, more attention should be paid to the correction effect of new information on the development trend. Therefore, based on the traditional MGM(1,m) model, this paper introduces the new information priority operator λ and nonlinear parameter γ to strengthen the role of new information, further constructs three comparison models of MGM(1,m|λ), MGM(1,m|γ) and MGM(1,m|λ,γ).Then we apply the new model to the carbon emission prediction of different regions (cities, countries and continents) and different trends (fluctuating, rising and declining). The results illustrate that the new model has higher prediction accuracy, and adding dynamic parameters is a scientific and practical method to improve the forecasting ability of the grey forecasting model. Finally, we analyze the current situation and future development trend of carbon emissions, and put forward reasonable suggestions.