Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting
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 method...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3567894 |
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| _version_ | 1849304506826752000 |
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| author | Xin Zhao Xiaokai Nie |
| author_facet | Xin Zhao Xiaokai Nie |
| author_sort | Xin Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-10afcaecc7324f1397fc9ebde0827de5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-10afcaecc7324f1397fc9ebde0827de52025-08-20T03:55:44ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/35678943567894Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply ForecastingXin Zhao0Xiaokai Nie1School of Mathematics, Southeast University, Nanjing 211189, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaRenewable 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.http://dx.doi.org/10.1155/2020/3567894 |
| spellingShingle | Xin Zhao Xiaokai Nie Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting Complexity |
| title | Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting |
| title_full | Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting |
| title_fullStr | Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting |
| title_full_unstemmed | Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting |
| title_short | Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting |
| title_sort | prediction error and forecasting interval analysis of decision trees with an application in renewable energy supply forecasting |
| url | http://dx.doi.org/10.1155/2020/3567894 |
| work_keys_str_mv | AT xinzhao predictionerrorandforecastingintervalanalysisofdecisiontreeswithanapplicationinrenewableenergysupplyforecasting AT xiaokainie predictionerrorandforecastingintervalanalysisofdecisiontreeswithanapplicationinrenewableenergysupplyforecasting |