Abstract: In order to improve the accuracy of short-term load forecasting and reduce the influence of non-stationary signals on model forecasting, this paper proposes a short-term load forecasting model that integrates data mining and multi-objective optimization networks. This method decomposes the power load data into several modal components with different frequencies through variational mode decomposition (VMD) technology, uses the phase space reconstruction (PSR) method to dynamically determine the training of the artificial neural network test ratio and neuron settings, uses the long short-term memory (LSTM) neural network to build models for each component, adds multi-objective grasshopper optimization algorithm (MOGOA) on the basis of LSTM to optimize the internal parameters of the network, and accumulates the predicted values of all component models to realize short-term load forecasting. The simulation results show that, compared with the statistical method and the hybrid model, the proposed model has higher prediction accuracy and stronger generalization ability in short-term prediction.

Key words: variational mode decomposition, phase space reconstruction, LSTM, multi-objective grasshopper optimization algorithm, short-term load forecasting