影响因子：7.6
DOI码：10.1016/j.knosys.2024.112943
所属单位：哈尔滨工程大学船舶工程学院
教研室：哈尔滨工程大学船舶工程学院
发表刊物：Knowledge-Based Systems
刊物所在地：荷兰
项目来源：National Natural Science Foundation of China under Grant No. 61872168 and in part by Natural Science
关键字：Twin support vector regression; Bi-directional gated recurrent unit; Seasonal mechanism; Ensemble Learning; Electric load forecasting
摘要：Accurate electric load forecasting has numerous benefits, including cost reduction in power generation and valuable guidance for decision-makers in the power industry. Support Vector Regression (SVR) has gained attention for its strong mathematical foundation and high performance in electric load forecasting. However, SVR encounters challenges in computational cost, fitting large-scale datasets and seasonal pattern capturing. To overcome these limitations, this paper proposes SCBTIA, an intelligent load forecasting model that integrates data pre-processing, deep learning model, an improved meta-heuristic optimization algorithm and rolling seasonal mechanism into twin support vector regression (TWSVR). TWSVR improves upon SVR by delivering superior performance and reduced computational time, alleviating its limitations. Further, TWSVR is integrated with bi-directional gated recurrent unit (BiGRU) deep learning model using complementary ensemble empirical mode decomposition (CEEMD). This work also introduces an improved arithmetic optimization algorithm (IAOA) for dynamically selecting optimal hyper-parameter combinations in TWSVR. A seasonal mechanism that applying rolling-window method is also applied in the proposed model. Experiments encompass ablation studies, comparisons with SVR and TWSVR models, as well as single basic models and state-of-the-art models. These experiments utilize two real-world electric load datasets, highlighting SCBTIA's superior performance. Experiments illustrate that the proposed SCBTIA model has ability to address the challenges faced in electric load forecasting and provide an advanced solution that outperforms existing approaches.
备注：National Natural Science Foundation of China under Grant No. 61872168 and in part by Natural Science Foundation of Jiangsu Province, China under Grant No. BK20241052.
合写作者：Chenglong Zhang
第一作者：张子晨
论文类型：期刊论文
通讯作者：董永泉,洪维强
论文编号：112943
学科门类：工学
文献类型：J
卷号：310
ISSN号：0950-7051
是否译文：否
发表时间：2025-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0950705124015776?via%3Dihub