This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.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 attention 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 accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.
The estimation of sequence or symmetrical components and frequency in three-phase unbalanced power system is of great importance for protection and relay.This paper proposes a new H∞filter based on sparse model to track the sequence components and the frequency of three-phase unbalanced power systems.The inclusion of sparsity improves the error convergence behavior of estimation model and hence short-duration non-stationary PQ events can easily be tracked in the time domain.The proposed model is developed using l1 norm penalty in the cost function of H∞filter,which is quite suitable for estimation across all the three phases of an unbalanced system.This model uses real state space modeling across three phases to estimate amplitude and phase parameters of sequence components.However,frequency estimation uses complex state space modeling and Clarke transformation generates a complex measurement signal from the unbalanced three-phase voltages.The state vector used for frequency estimation consists of two state variables.The proposed sparse model is tested using distorted three-phase signals from IEEE-1159-PQE database and the data generated from experimental laboratory setup.The analysis of absolute and mean square error is presented to validate the performance of the proposed model.
