Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition

Thomas Wu, Ruifeng Hu, Hongyu Zhu, Meihui Jiang, Kunye Lv, Yunxuan Dong and Dongdong Zhang

Energy, 2024, vol. 288, issue C

Abstract: Accurate photovoltaic power prediction is important to ensure stable and safe operation of microgrids. However, due to the high volatility of photovoltaic power data, the prediction accuracy of traditional prediction models is often unsatisfactory. To ensure stable operation of microgrids, this study proposes a combined improved extreme gradient boosting-kernel extreme learning machine short-term photovoltaic power prediction model consisting of multidimensional similar day clustering and dual decomposition. Initially, gray relation analysis, Pearson correlation coefficient, and Kmeans++ are used for clustering to obtain high-precision similar days. Subsequently, a dual signal decomposition model based on variational modal decomposition and complete ensemble empirical mode decomposition with adaptive noise is proposed. Finally, predictions are made using a combination of predictive models with complementary strengths and weaknesses, and the prediction results of each component are fitted with under three weather conditions, the average root mean square error is reduced by 78.02%,62.99%, and 62.48%, and the average mean absolute error is reduced by 82.55%, 71.13%, and 67.07% in comparison with the baseline model. The results show that the model is effective in improving the prediction accuracy in a variety of different environments.

Keywords: Photovoltaic prediction; Similar day clustering; Dual decomposition; Composite model; Golden jackal optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422303164X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s036054422303164x

DOI: 10.1016/j.energy.2023.129770

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().