Abstract
This 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.
基金项目
Shaanxi Provincial Education Department 2022 Key Research Program Project(22JS022)
National Natural Science Foundation of China(51808428)
NETL
NSTL
维普
万方数据