Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-26
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Renewable energy has become popular compared with traditional energy like coal.
The relative demand for renewable energy compared to traditional energy is an important index to determine the energy supply structure.
Forecasting the relative demand index has become quite essential.
Data mining methods like decision trees are quite effective in such time series forecasting, but theory behind them is rarely discussed in research.
In this paper, some theories are explored about decision trees including the behavior of bias, variance, and squared prediction error using trees and the prediction interval analysis.
After that, real UK grid data are used in interval forecasting application.
In the renewable energy ratio forecasting application, the ratio of renewable energy supply over that of traditional energy can be dynamically forecasted with an interval coverage accuracy higher than 80% and a small width around 22, which is similar to its standard deviation.
American Psychological Association (APA)
Zhao, Xin& Nie, Xiaokai. 2020. Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141521
Modern Language Association (MLA)
Zhao, Xin& Nie, Xiaokai. Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1141521
American Medical Association (AMA)
Zhao, Xin& Nie, Xiaokai. Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141521
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141521