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Yangyu Xu

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Proceedings Article | 30 August 2022 Paper

Short-term power load forecasting based on deep learning

Yangyu Xu, Yaner Feng

Proceedings Volume 12309, 123092Y (2022) https://doi.org/10.1117/12.2645056

KEYWORDS: Neural networks, Data modeling, Data processing, Convolutional neural networks, Feature extraction, Performance modeling, Convolution, Statistical modeling, Neurons

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In the process of our daily use of electricity, power load forecasting is very important. Short-term power load forecasting can effectively manage electric energy. In order to improve the intelligence of power grid, this paper studies the analysis and prediction methods of power load based on deep learning. Short-term power load forecasting can accurately predict the load and electricity consumption of a certain area, and provide reference for the operation of the power system. In daily life, our power load data are interfered by many factors, so we need to fully find out the temporal characteristics of power load data, so as to improve the accuracy of our power load forecasting. In this paper, a convolutional neural network-long-term and short-term memory neural network (CNN-LSTM) neural network prediction model based on convolution neural network and long-term and short-term memory neural network is proposed. The data characteristics are extracted by convolutional neural network (CNN), and then the accurate prediction of power load is completed by using the unique memory and prediction ability of long-term and short-term memory neural network (LSTM) neural network. The experimental results show that compared with the single LSTM neural.

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