Abstract

To provide accurate renewable energy forecasts that adapt to the country's sustainable development, a novel seasonal model combined with the data-restacking technique is proposed in this paper. Specifically, the data-restacking technique is initially utilized to eliminate the seasonal fluctuations of the collected observations, which can eliminate the fundamental flaws in conventional seasonal grey models. Subsequently, the time function term is originally designed to incorporate into the dynamic structure to reflect the cumulative time effects, which can smoothly describe the dynamic changes and significantly improve the robustness of the novel model. Further, the self-adaptive parameters optimized using particleswarmoptimization can effectively enhance the adaptability and generalization of the proposed model. For elaboration and verification purposes, experiments on forecasting American monthly renewable energy consumption in the commercial sector and industrial solar energy have been implemented compared to a range of benchmark models, including other prevalent grey prediction models, statistical approaches, and machine learning methods. Experimental results demonstrate that this new model presents more successful outcomes than the other benchmarks in overall and restacking performance.

Full Text

Published Version
Open DOI Link

Get access to 250M+ research papers

Discover from 40M+ Open access, 3M+ Pre-prints, 9.5M Topics and 32K+ Journals.

Sign Up Now! It's FREE

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call