A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices

Xinsong Niu, Jiyang Wang, Danxiang Wei and Lifang Zhang

Renewable Energy, 2022, vol. 201, issue P1, 46-59

Abstract: With the severe situation of climate and environmental issues, carbon emissions have aroused great attention from the academic community and industry. Building and improving the carbon trading market has become the focus, among which the prediction of carbon emissions trading prices is crucial. However, randomness and instability make it a challenging task to predict price series accurately. To obtain accurate point prediction results, a combined prediction idea is constructed in the study. In addition, considering that the point prediction framework contains less data information, in order to bridge this gap, an interval prediction frame is developed in this research. The optimal distribution of data sequence is obtained by using distribution function and optimization techniques, and successfully achieve different levels of uncertainty prediction according to the point prediction results. Several comparison experiments were carried out using the daily price of carbon emission futures of the European Union Emissions Trading System and the forecasting performance of the proposed forecasting framework was verified through experimental analysis. Experiments and discussion demonstrate the superior performance of the proposed prediction scheme and its excellent forecasting ability for carbon emissions trading price prediction.

Keywords: Carbon trading price; Point prediction and interval prediction; Artificial intelligence; Optimization method; Optimal distribution (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122015208
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:renene:v:201:y:2022:i:p1:p:46-59

DOI: 10.1016/j.renene.2022.10.027

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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