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.
With development of smart grid, the stable operation of grid has put forward higher requirements for system dispatch. In particular, short-term load forecasting of power systems is a key factor of power grid management systems, which is related to the safety, economy, and stable operation of the smart grid. However, short-term electricity forecasting is affected by many external factors. It has complex characteristics, especially non-linear relationships, so it cannot be accurately predicted. Recently, Recurrent Neural Network based models have good performance in electricity forecasting because of their excellent ability to capture non-linear relationships. However, they cannot fully capture historical information, especially local historical information, which has an impact on prediction accuracy. In order to address these problems, we propose a scheme by combining STL decomposition and GRU model. Specifically, we first decompose the original time series into three different components by STL. The decomposition results are separately imported into the main prediction module, which uses two GRU networks with different structures to obtain the local and global dependencies of the data. We also add an autoregressive method to make the model more robust. The proposed scheme is validated based on real-world data, and the simulation results show that our proposed method can perfectly capture local and global information and achieve higher prediction accuracy than traditional models.
The forecasting of electricity consumption data plays an important role in the operation, planning, and security of the power grid. However, electricity data is affected by multiple factors and large fluctuations, which makes it difficult to accurately forecast. Traditionally, ARIMA and SVM are widely used for electricity forecasting based on historical consumption data. However, for non-stationary multi-feature data, traditional schemes cannot achieve deep feature mining of them, and the forecast results are inaccurate. To address this problem, this paper proposes an efficient short-term electricity forecasting approach based on EEMD-LSTM model. Firstly, we perform Savitzky-golay (SG) smoothing on the original data, and then introduce feature factors to the feature analysis. In particular, the proposed approach can reduce the random noise in data, as well as reduce the impact of data fluctuations, and effectively learn the long-term characteristics of the data. The simulation results show that, compared with ARIMA, LSTM, EMD-SVM, EMD-LSTM, the proposed approach can achieve better accuracy in the electricity forecasting.
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