Research on short-term power load forecasting based on big data and optimized neural network

Xiazhe Tu; Juhua Hong; Shicheng Huang; Linyao Zhang; Zhenda Hu; Yichao Zou; Weiwei Lin

doi:10.1117/12.2671440

1 June 2023 Research on short-term power load forecasting based on big data and optimized neural network

Xiazhe Tu, Juhua Hong, Shicheng Huang, Linyao Zhang, Zhenda Hu, Yichao Zou, Weiwei Lin

Proceedings Volume 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022); 126252T (2023) https://doi.org/10.1117/12.2671440
Event: International Conference on Mathematics, Modeling and Computer Science (MMCS2022),, 2022, Wuhan, China

Abstract

During the operation of power system, short-term load forecasting is a basic component of energy management system, and also an important basis for the operation of power dispatching system. Accurate forecasting of short-term load is helpful for managers to put forward standard power generation plan during work, take reasonable measures to protect the performance of power grid system, effectively control the cost of power generation, and improve the social and economic benefits of power system operation. Therefore, on the basis of understanding the application status of load forecasting technology and according to the application advantages of artificial neural network, this paper constructs a short-term load forecasting model of neural network with big data as the core. The final experimental results show that the improved particle swarm optimization algorithm can further improve the accuracy and efficiency of load forecasting, which has a positive impact on the long-term development of power system.

Citation Download Citation

Xiazhe Tu, Juhua Hong, Shicheng Huang, Linyao Zhang, Zhenda Hu, Yichao Zou, and Weiwei Lin "Research on short-term power load forecasting based on big data and optimized neural network", Proc. SPIE 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 126252T (1 June 2023); https://doi.org/10.1117/12.2671440

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