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Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling
Xiao, Ling1,2; Li, Miaotong2; Zhang, Shenghui3
2022-11-01
Source PublicationEnergy Reports
Volume8Pages:6672-6686
Abstract

Short-term power load forecasting plays a vital role in the planning of distribution network and the development of social economy. Many researchers have devoted their attention to construct point forecasting models. However, the traditional point forecasting regards the forecasting result as a deterministic variable. Since the deviation existed in load forecasting is simply unavoidable and significant, which has great volatility and randomness, point forecast methods are difficult to capture the fluctuation of power load. Probability forecast models are proposed to obtain the uncertain information in the load power. In this paper, firstly, some error information is obtained from the deterministic forecasting results of point forecasting; secondly, the interval of time series data is divided according to the deterministic error information; finally, the Bootstrap method is used to estimate the confidence interval of the deteministic error information to obtain the uncertainty information in the power load data. The instability and randomness of the deterministic error are otained by combining the interval forecasting method so as to improve the accuracy of power load forecasting. Therefore, probability forecasting is combined with point forecasting to obtain more accurate results. The proposed model is used to forecast power load for Queensland, Australia and capital district of New York State. The experimental results show that the proposed method performs better than other comparative models. The experimental results show that the ELM-AdaBoost model has better forecast performance in both long-term and short-term load datasets, and can overcome the seasonality of power load time series data.

KeywordAdaptive Boosting Algorithm Extreme Learning Machine Nonparametric Bootstrap Sampling Power Load Interval Forecasting
DOI10.1016/j.egyr.2022.05.016
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:000806042100011
Scopus ID2-s2.0-85130560615
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLi, Miaotong
Affiliation1.School of Mathematics and Statistics, Xuzhou Institute of Technology, Xuzhou, 221018, China
2.School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
3.State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science Organization, University of Macau, 999078, China
Recommended Citation
GB/T 7714
Xiao, Ling,Li, Miaotong,Zhang, Shenghui. Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling[J]. Energy Reports,2022,8:6672-6686.
APA Xiao, Ling,Li, Miaotong,&Zhang, Shenghui.(2022).Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling.Energy Reports,8,6672-6686.
MLA Xiao, Ling,et al."Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling".Energy Reports 8(2022):6672-6686.
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