Variable input structure user load forecasting method based on user load state identification

Min Luo; Shangli Zhou; Jinfeng Yang; Leping Zhang; Yuchen Lai; Yangyun Guo; Sheng Li; Minna Chen; Yingnan Zhang

doi:10.1117/12.3015842

6 February 2024 Variable input structure user load forecasting method based on user load state identification

Min Luo, Shangli Zhou, Jinfeng Yang, Leping Zhang, Yuchen Lai, Yangyun Guo, Sheng Li, Minna Chen, Yingnan Zhang

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129795T (2024) https://doi.org/10.1117/12.3015842
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

In recent years, with the comprehensive construction of smart grids, systems such as electricity information collection and distribution automation have been continuously improved. This has led to an explosive growth in load data generated during the distribution and utilization of electricity. There are significant differences between load characteristics at the distribution and utilization stages and those of the overall system load. Traditional system forecasting methods are difficult to apply in this context, making it urgently necessary to develop a predictive method suitable for the load characteristics at the distribution and utilization stages. To accommodate the large volume and strong volatility of user load data, this paper first proposes a variable input structure prediction method. This method involves mining key features that conform to load state characteristics and constructing a support vector machine prediction model based on state analysis. The model searches for historical loads with the same state as the day to be predicted as input factors, thereby mitigating the adverse effects of random user load fluctuations on predictions. This effectively enhances the accuracy of user load forecasting.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Min Luo, Shangli Zhou, Jinfeng Yang, Leping Zhang, Yuchen Lai, Yangyun Guo, Sheng Li, Minna Chen, and Yingnan Zhang "Variable input structure user load forecasting method based on user load state identification", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129795T (6 February 2024); https://doi.org/10.1117/12.3015842

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KEYWORDS

Data modeling

Power consumption

Power grids

Matrices

Support vector machines

Artificial neural networks

Stochastic processes

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