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12 November 2024 Short-term power load forecasting based on improved back propagation neural network
Qirui Wang, Cheng Peng, Yujun Zhou
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133952R (2024) https://doi.org/10.1117/12.3046151
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
To solve the problem of short-run power load forecasting, this article proposes a model using particle swarm optimization (PSO) to adjust the parameters of the backpropagation (BP) neural network, namely the PSO-BP model. Based on this, the GPSO-BP-NN short-term power load forecasting model is constructed. For the sake of verifying the performance of GPSO-BP-NN, actual data from a certain region in China is selected for experimentation. In view of the analysis of the fitness function outcome, by comparing the prediction results of GPSO-BP-NN, PSO-BP-NN, and BPNN models, it is found that the mean absolute error of the GPSO-BP-NN model is 2.21%, which is lower than the 2.39% of the PSO-BP-NN and the 3.53% of the BP-NN. Through the analysis of prediction accuracy, algorithm comparison, and time cost, GPSO-BP-NN is superior to the other two prediction models, proving the efficiency of the improved algorithm
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Qirui Wang, Cheng Peng, and Yujun Zhou "Short-term power load forecasting based on improved back propagation neural network", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133952R (12 November 2024); https://doi.org/10.1117/12.3046151
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KEYWORDS
Particle swarm optimization
Neural networks
Particles
Artificial neural networks
Education and training
Data modeling
Evolutionary algorithms
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