Energy storage device control method based on ensemble empirical mode decomposition and LSTM

Abstract

The invention relates to an energy storage device control method based on ensemble empirical mode decomposition and LSTM. The method comprises the following steps: firstly, performing normalization processing and ensemble empirical mode decomposition on historical short-term load data in first n + 2 time periods in continuous n + 2 time periods to obtain a subsequence or a residual component as aninput item; training an LSTM model by taking a subsequence or a residual component corresponding to historical short-term load data in a later time period as a theoretical output item; preprocessingthe data of the current time period and the historical short-term loads of n time periods closest to the current time period, and inputting the preprocessed data into the trained LSTM model; and afterthe trained LSTM model is utilized to output predicted values, all the predicted values are reconstructed and subjected to reverse normalization processing to obtain a prediction result, and finally,the energy storage device is controlled to charge and discharge according to the prediction result. The method has the advantages of high prediction precision and reasonable charge-discharge operation of the energy storage device.

Description

technical field [0001] The invention belongs to the technical field of power load dispatching, and relates to an energy storage device control method based on ensemble empirical mode decomposition and LSTM. Background technique [0002] As an important part of the economic dispatching of the power system, accurate load forecasting can economically and reasonably arrange the start-up and shutdown of the generating units inside the power grid, ensure the stable operation of the power grid, and provide reliable information for power grid dispatching planning, equipment maintenance, and power grid reconstruction and expansion. data support. [0003] In recent years, with the continuous expansion of the field of electricity consumption, the number of users continues to increase, and the penetration rate of new energy in the power grid is getting higher and higher. Due to the intermittent and uncertain output of new energy, the peak and valley load of the power grid Therefore, on...

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Problems solved by technology

In view of the uncertainty of load forecasting, the above methods are often not ideal in terms of forecasting accuracy. With the rise of neural networks, their powerful learning ability and self-adaptive ability enable them to achieve great success in many fields such as pattern recognition, intelligent robots, and automatic control. Due to the uncertainty of the load, the neural network can be used for learning to improve the accuracy of load forecasting

[0004] At present, some people use wavelet analysis combined with neural network to predict time series, but wavelet analysis needs to select the appropriate mother wavelet and set the number of feasible decomposition layers. The adaptive effect of the decomposition of nonlinear and non-stationary signals is poor, and the prediction accuracy is still low. needs improvement

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