Affiliation:
1. School of Mathematics, Southeast University, Nanjing 211189, China
2. School of Automation, Southeast University, Nanjing 210096, China
Abstract
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.
Funder
Fundamental Research Funds for the Central Universities
Subject
Multidisciplinary,General Computer Science
Cited by
5 articles.
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