1. 武汉科技大学计算机科学与技术学院
2. 智能信息处理与实时工业系统重点实验室
纸质出版:2024
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廖雪超, 黄相. 基于周期特征提取的DLnet预测模型研究[J]. 传感器与微系统, 2024,(10):46-49+54.
廖雪超, 黄相. 基于周期特征提取的DLnet预测模型研究[J]. 传感器与微系统, 2024,(10):46-49+54. DOI: 10.13873/J.1000-9787(2024)10-0046-04.
现有的预测方法很少独立分析能源消耗的周期性特征。本文提出了一个短期办公建筑能耗预测模型(DLnet)
以解决周期性能耗数据利用效率低下的问题。首先,利用STL对能耗数据的周期成分进行分解,通过网格搜索算法寻找能耗数据的最优周期;然后,根据最优周期构建周期块;再根据周期块的数据形状构建时间序列块数据;之后,利用长短期记忆(LSTM)对时间序列块数据和周期块数据进行训练和学习;最后,通过线性回归将时间序列块数据和周期块数据的预测结果进行融合。事实证明,所提出的模型的4个预测精度指标分别比LSTM模型高7%
21%
25%和26%。
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Business School, Sichuan University, Chengdu, 610064, China,Business School, Sichuan University, Chengdu, 610064, China,Business School, Sichuan University, Chengdu, 610064, China,Management Faculty, Chengdu University of Information Technology, Chengdu, 610103, China,School of Public Administration, Sichuan University, Chengdu, 610064, China.A hybrid model based on selective ensemble for energy consumption forecasting in China[J].Energy,2018.
School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China,Center for Research of Regulation & Policy, Zhejiang University of Finance & Economics, Hangzhou 310018, China,School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China,School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China.A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors[J].Energy,2018.
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