Power load prediction method and device, computer equipment and storage medium

Abstract

The invention discloses a power load prediction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring historical power consumption data, and performing empirical mode decomposition on the historical power consumption data to obtain a series of intrinsic mode function components and residual components; learning the residual component by using a single-layer LSTM network, and outputting a trend prediction result; learning a series of intrinsic mode function components by using a CNN-BiLSTM network fused with an attention mechanism, and outputting a series of corresponding fluctuation prediction results; and reconstructing a series of corresponding fluctuation prediction results and trend prediction results to obtain a final power consumption prediction result. The prediction model is constructed to learn the historical power consumption data, the trend prediction result and the fluctuation prediction result based on the historical power consumption data are obtained, and the obtained prediction result is reconstructed to obtain the final prediction result, so that the final prediction result is accurate and reliable.

Description

technical field [0001] The invention relates to the field of power forecasting, in particular to a power load forecasting method, device, computer equipment and storage medium. Background technique [0002] With the development of the electricity market and the improvement of user demand, the safety and economic operation of the power grid has become crucial. Accurate short-term forecasting of power load can effectively ensure the safe operation of the power grid, reduce power generation costs, meet user needs and improve social and economic benefits. Since the power system load has obvious periodic characteristics, and the influencing factors are complex, such as climate, rainfall, etc., it is necessary to choose an advanced and accurate short-term load forecasting method. [0003] At present, many modern intelligent power load forecasting methods have emerged, such as wavelet analysis, artificial neural network and support vector method. Compared with some traditional sho...

AI Technical Summary

Problems solved by technology

However, the historical information retained by RNNs will decay over time, that is, the historical gradient in the backpropagation process will dissipate

The shortcomings of RNNs make it impossible to apply data modeling of longer time series

Considering the sequential nature of power load data, RNN and LSTM neural networks have problems such as gradient disappearance and overfitting.

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