Deviation analysis method of runoff prediction of hydropower stations in large watershed under random inflow

Mengfei Xie; Yang Zhang; Bangcan Wang; Jianjian Shen

doi:10.1117/12.2640205

1 August 2022 Deviation analysis method of runoff prediction of hydropower stations in large watershed under random inflow

Mengfei Xie, Yang Zhang, Bangcan Wang, Jianjian Shen

Author Affiliations +

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570L (2022) https://doi.org/10.1117/12.2640205
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Inflow is an important factor affecting the accuracy of power generation dispatching of hydropower stations. Due to the randomness of Inflow, there are usually deviations in the runoff prediction of hydropower stations, especially for hydropower stations in large watersheds, the deviation of runoff prediction may greatly affect power generation dispatching. In this paper, a deviation analysis method of runoff prediction of hydropower stations in large watershed under the condition of random inflow is proposed. The ARIMA model in the time series analysis method is used for runoff prediction, and then the error distribution law between the prediction results and the actual results is analyzed by copula function, and the error distribution of annual and monthly runoff prediction and power generation capacity prediction of cascade hydropower stations is analyzed. Taking Xiluodu, Xiaowan and Nuozhadu hydropower stations as examples, the annual runoff prediction and error distribution law are analyzed, which can accurately identify the range of deviation.

Citation Download Citation

Mengfei Xie, Yang Zhang, Bangcan Wang, and Jianjian Shen "Deviation analysis method of runoff prediction of hydropower stations in large watershed under random inflow", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570L (1 August 2022); https://doi.org/10.1117/12.2640205

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