LSTM energy consumption prediction method based on dual feature selection and particle swarm optimization

Abstract

The invention discloses an LSTM energy consumption prediction method based on dual feature selection and particle swarm optimization. The method comprises the following steps: 1, performing correlation analysis on time and feature dimension of an original data set by adopting an MI mutual information method, and selecting first N'-dimensional features which are most effective for an energy consumption prediction target value; 2, performing secondary feature selection on the N-dimensional features to obtain N''-dimensional features after PMI feature selection; 3, performing model training and prediction on data after PMI dual feature selection by adopting an LSTM model to obtain an initial prediction sequence y(t); 4, performing optimization on hyper-parameters units, dropout, and batch size of the LSTM model by adopting a PSO algorithm, thereby improving the prediction precision of the LSTM model, and finally obtaining a PMI-LSTM-PSO model. The method has the advantages of high prediction precision, high algorithm efficiency, and stable prediction performance.

Description

technical field [0001] The invention relates to the technical field of building energy consumption prediction, more specifically, it is an LSTM energy consumption prediction method based on dual feature selection + particle swarm optimization. Background technique [0002] With the widespread application of more and more complex technological products, the demand for electricity is currently increasing globally, requiring the control of the power grid to achieve sustainable development of electricity. In the era of artificial intelligence, the power Internet of Things has gradually been integrated into daily life, and the development of smart grids also requires corresponding testing capabilities, and smart meters have emerged as the times require. The continued expansion of smart meter infrastructure around the world is also laying the groundwork for the introduction of active energy systems into smart grids. Since launching the "Strong Smart Grid" program in 2009, State G...

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Problems solved by technology

[0045] However, the existing MI mutual information algorithm, LSTM model, and PSO particle swarm optimization algorithm have low accuracy in energy consumption prediction, and the prediction performance is unstable, which does not meet the requirements of building energy consumption prediction

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