A Power Load Prediction by LSTM Model Based on the Double Attention Mechanism for Hospital Building
Affiliation of Author(s):
建筑设备科学与工程学院
Key Words:
power load prediction; long short-term memory (LSTM); double attention mechanism; grey relational degree; hospital build‐
ing
Abstract:
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.
Paper Publications
