A Grid-based Numerical Weather Prediction Method for Multi-output Prediction of Regional Photovoltaic Power

doi:10.11930/j.issn.1004-9649.202305029

Abstract

Abstract:

The short-term power prediction of regional photovoltaic (PV) is one of the important bases for the provincial and above power grid control center to make power generation plans and improve the photovoltaic consumption. In essence, short-term photovoltaic power prediction is to build a mapping model between numerical weather prediction and actual power. In order to improve the prediction accuracy, the grid-based numerical weather prediction and residual network were used in this paper to establish a multi-output prediction model of regional photovoltaic, and fully explore the correlation between the distribution of meteorological resources and the power of each photovoltaic power station in the regional photovoltaic space, thus identifying the grid-based numerical weather prediction as input and the power of each photovoltaic power station in the region as output. The simulation was carried out by use of actual operation data. The results showed that the proposed method was superior to the existing proven methods in predicting the power and total power of each photovoltaic power station.

Key words: photovoltaic power prediction, gird numerical weather prediction, residual network, multi-output forecast model

Wenhua ZHAN, Jianfeng CHE, Bo WANG, Yu DING. A Grid-based Numerical Weather Prediction Method for Multi-output Prediction of Regional Photovoltaic Power[J]. Electric Power, 2024, 57(3): 144-151.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202305029

https://www.electricpower.com.cn/EN/Y2024/V57/I3/144

Figures/Tables 9

Fig.1 Single meteorological parameter of grid-based NWP at a certain time

Fig.2 Multiple meteorological parameters of grid-based NWP at a certain time

Fig.3 Resource distribution of a certain region in Northwest China at the same time

Fig.4 Distribution of power and resource of each PV station in the region at the same time

Fig.5 Example of typical residual element structure

Fig.6 Steps of selection of meteorological parameters

Table 1 Performance of different methods for total power prediction of 46 PV stations

算法	E_mae /%	E_rmse /%	r
BPNN	5.87	8.25	0.958
XGBoost	6.60	8.97	0.952
本文方法	5.74	8.00	0.960

Fig.7 Performance of different methods for power prediction of 46 PV stations

Fig.8 Prediction results of total power

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