所属单位：建筑设备科学与工程学院

发表刊物：武汉大学自然科学杂志

关键字：power load prediction; long short-term memory (LSTM); double attention mechanism; grey relational degree; hospital build‐ ing

摘要：his work proposed a LSTM (long short-term memory) model based on the double attention mechanism for power load predic‐ tion, to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospi‐ tal. Firstly, the key influencing factors of the power loads were screened based on the grey relational degree analysis. Secondly, in view of the characteristics of the power loads affected by various factors and time series changes, the feature attention mechanism and sequential at‐ tention mechanism were introduced on the basis of LSTM network. The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features, and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects. In the end, the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accu‐ racy and stability than the conventional LSTM, CNN-LSTM and attention-LSTM models.

第一作者：冯增喜

论文类型：期刊论文

卷号：28(3): 223-236

是否译文：否

发表时间：2023-07-13