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广西师范大学学报(自然科学版) ›› 2024, Vol. 42 ›› Issue (6): 101-116.doi: 10.16088/j.issn.1001-6600.2023122402
段沁宇1, 薛贵军1,2*, 谭全伟1, 谢文举1
DUAN Qinyu1, XUE Guijun1,2*, TAN Quanwei1, XIE Wenju1
摘要: 精准高效的热负荷预测对于保障热力系统稳定运行和合理规划热力资源至关重要。为了提升热负荷预测的准确性,本文提出一种基于逐次变分模态分解(successive variational mode decomposition,SVMD)和改进白鲸优化算法(improved Beluga whale optimization,IBWO)的TimesNet短期热负荷预测模型。首先,利用SVMD将原始热负荷数据进行分解,去除噪声后得到若干个平稳且有规律的模态分量;其次,根据每个模态分量的特点选择合适的特征作为输入;然后,引入3种策略来改进白鲸优化算法,从而建立IBWO-TimesNet预测模型;最后,通过算例对模型的预测性能进行详细评估。结果表明:SVMD-IBWO-TimesNet模型的MAE、RMSE和R2分别为0.647、1.190和99.1%。与其他主流预测模型相比,该模型具有更高的预测精度。同时,在减少训练样本的情况下,SVMD-IBWO-TimesNet模型仍能有效预测热负荷,具有较强的泛化能力。验证了所提出模型的有效性,为热力系统供热负荷的精准调控提供了参考。
中图分类号: TM715
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[1] | 谭全伟, 薛贵军, 谢文举. 基于VMD和RDC-Informer的短期供热负荷预测模型[J]. 广西师范大学学报(自然科学版), 2024, 42(5): 39-51. |
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