Clustering-based small sample load prediction method and device, equipment and storage medium

Abstract

The invention discloses a clustering-based small sample load prediction method and device, equipment and a storage medium, and the prediction method comprises the steps of: carrying out feature extraction of a historical power load and a to-be-predicted power load to obtain a feature vector; performing integrated clustering on the historical power load and the to-be-predicted power load according to the obtained feature vector to obtain a clustering result; carrying out noise reduction on the clustering result by adopting a wavelet noise reduction algorithm, and carrying out equalization processing on the data after noise reduction to obtain time sequence data with a preset length; inputting the time sequence data with the preset length into the second-order long-short-term memory neural network to obtain a prediction result of the power load; wherein the second-order long-short-term memory neural network is subjected to data training of historical power load and to-be-predicted power load. The method still has excellent prediction performance under the condition that the to-be-predicted power load is scarce.

Description

technical field [0001] The present application relates to the technical field of power load forecasting, and in particular to a cluster-based small-sample load forecasting method, device, equipment and storage medium. Background technique [0002] Time series data (hereinafter referred to as time series data) forecasting methods have been widely used in the electricity market. Traditional statistical models have advantages in interpretability but are weak in predictive accuracy and other aspects. In recent years, machine learning-based algorithms have gained a lot of attention in the field of time series forecasting due to their higher forecasting accuracy. [0003] Another closely related technique is few-shot learning. Generally speaking, large-scale training sets are essential for deep learning, and the purpose of small-sample learning is to improve existing deep learning algorithms in order to achieve excellent training results on small-scale training sets. [0004] H...

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Problems solved by technology

[0006] This application provides a cluster-based small-sample load forecasting method, device, equipment, and storage medium to solve the problem of low accuracy of forecast results in the prior art when the training set of electric load is small

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