影响因子：8.9
DOI码：10.1109/TNNLS.2023.3335355
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：IEEE Transactions on Neural Networks and Learning Systems
刊物所在地：美国
项目来源：National Natural Science Foundation of China under Grant 61872168
关键字：Bootstrap method, fast ensemble empirical model decomposition (FEEMD), long short-term memory (LSTM) network, probabilistic load forecasting (PLF), seeker optimization algorithm (SOA), twin support vector regression (TWSVR).
摘要：A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient operation of power systems but also to the efficient use of energy resources. In order to estimate the uncertainties in forecasting models and nonstationary electric load data, this study proposes a probabilistic load forecasting model, namely BFEEMD-LSTM-TWSVRSOA. This model consists of a data filtering method named fast ensemble empirical model decomposition (FEEMD) method, a twin support vector regression (TWSVR) whose features are extracted by deep learning-based long short-term memory (LSTM) networks, and parameters optimized by seeker optimization algorithms (SOAs). We compared the probabilistic forecasting performance of the BFEEMD-LSTM-TWSVRSOA and its point forecasting version with different machine learning and deep learning algorithms on Global Energy Forecasting Competition 2014 (GEFCom2014). The most representative month data of each season, totally four monthly data, collected from the one-year data in GEFCom2014, forming four datasets. Several bootstrap methods are compared in order to determine the best prediction intervals (PIs) for the proposed model. Various forecasting step sizes are also taken into consideration in order to obtain the best satisfactory point forecasting results. Experimental results on these four datasets indicate that the wild bootstrap method and 24-h step size are the best bootstrap method and forecasting step size for the proposed model. The proposed model achieves averaged 46%, 11%, 36%, and 44% better than suboptimal model on these four datasets with respect to point forecasting, and achieves averaged 53%, 48%, 46%, and 51% better than suboptimal model on these four datasets with respect to probabilistic forecasting.
备注：National Natural Science Foundation of China under Grant 61872168
合写作者：董永泉
第一作者：张子晨
论文类型：期刊论文
通讯作者：洪维强
学科门类：工学
文献类型：J
卷号：36
期号：1
页面范围：1764-1778
ISSN号：2162-237X
是否译文：否
发表时间：2025
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/document/10333088