A time-varying grey Riccati model based on interval grey numbers for China's clean energy generation predicting

• Published in: Grey systems 2022-05, Vol.12 (3), p.501-521
• Main Authors: Guo, Sandang, Jing, Yaqian
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Business valuation; Clean energy; Clean technology; Differential equations; Energy consumption; Energy policy; Forecasting; Hydroelectric power; Hydroelectric power generation; Lower bounds; Mathematical models; Neural networks; Nuclear electric power generation; Optimization; Ordinary differential equations; Parameters; Riccati equation; Sequences; Statistical analysis; System theory; Trapezoids; Trends; Wind power; Wind power generation
• ISSN: 2043-9377; 2043-9385
• DOI: 10.1108/GS-04-2021-0057

PurposeIn order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.Design/methodology/approachBy combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.FindingsBased on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.Practical implicationsDue to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.Originality/valueThe main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.