A LSTM-based time series prediction model for China's power supply and its performance evaluation
DOI:
https://doi.org/10.54097/3gbp7648Keywords:
Long short-term memory network (LSTM), Power supply, Time series prediction, Data preprocessing, Performance evaluationAbstract
With the rapid development of China's economy and the acceleration of urbanisation, the stability and sustainability of power supply has become increasingly important. In order to cope with the continuous growth of power demand, accurate power supply prediction has become an essential research direction. In this paper, a time series prediction model for power supply is constructed based on the long short-term memory network (LSTM). Power supply data is organised using methods such as data pre-processing and correlation analysis, and the LSTM model is used to predict future power supply. The performance of the prediction results was evaluated, and the results showed that the model can effectively capture the changing trends of power supply, with high reliability and accuracy, providing important support for scientific management and policy formulation in the power industry.
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