Short-term power consumption prediction method based on I-LSTM

Abstract

The invention relates to a short-term power consumption prediction method based on I-LSTM, and belongs to the field of power system prediction. The method comprises the following steps: S1, collecting historical data of a power system, and processing incomplete data and abnormal values; S2, dividing the data into a training set, a verification set and a test set according to time; S3, constructing an (Improved Long Short Term Memory) I-LSTM network model, and inputting the training set into the I-LSTM network model for training; S4, setting a network loss function, an optimization algorithm, a learning rate and a batchsize of the I-LSTM network model; and S5, predicting the test set, and obtaining a prediction result of the test set according to a model obtained according to the accurate change condition of the verification set. According to the method, the key information in the user historical data sequence and the characteristic relationship between the user power consumption data can be better mined, and the user power consumption prediction precision and stability are effectively improved.

Description

technical field [0001] The invention belongs to the field of power system prediction, and relates to a short-term power consumption prediction method based on I-LSTM. Background technique [0002] With the improvement of industry and people's living standards, higher requirements are put forward for the supply quantity and supply efficiency of electric energy. Smart grid has been popularized in people's life. Therefore, it is necessary to provide a short-term forecasting method of power system with high accuracy. [0003] With the continuous development of deep learning technology, the power grid field has gradually turned its attention to deep learning. The commonly used method based on deep learning is neural network (Long Short-Term Memory, LSTM). LSTM introduces components with memory function, which can better capture the timing rules before and after data, so it is widely used in timing prediction. However, the ultra-long memory period makes the LSTM model have the ...

AI Technical Summary

Problems solved by technology

However, the ultra-long memory period makes the LSTM model have the problem of gradient disappearance, so it cannot better capture the ultra-long-term trend of the power time series

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