A PV Power Forecasting Based on Mechanism Model-Driven and Stacking Model Fusion-Reference-Cited by-同舟云学术

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A PV Power Forecasting Based on Mechanism Model-Driven and Stacking Model Fusion

Published:2024-04-12 Issue: Volume: Page:
ISSN:1975-0102
Container-title:Journal of Electrical Engineering & Technology
language:en
Short-container-title:J. Electr. Eng. Technol.

Author:

Chen Fan,Ding Jinjin,Zhang Qian,Wu Junjie,Lei Fan,Liu Yifan

Funder

State Grid Company Technology Project

2022 Collaborative Innovation Projects between Universities and Hefei Comprehensive National Science Center

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42835-024-01906-8.pdf

Reference33 articles.

1. Cheng L, Zang H, Wei Z, Ding T, Xu R, Sun G (2021) Short-term solar power prediction learning directly from satellite images with regions of interest. IEEE Trans Sustain Energy 13(1):629–639

2. Shahbaz M, Raghutla C, Chittedi KR, Jiao Z, Vo XV (2020) The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index. Energy 207:118162

3. Chang GW, Lu H-J (2018) Integrating gray data preprocessor and deep belief network for day-ahead pv power output forecast. IEEE Trans Sustain Energy 11(1):185–194

4. Wen H, Du Y, Chen X, Lim E, Wen H, Jiang L, Xiang W (2020) Deep learning based multistep solar forecasting for pv ramp-rate control using sky images. IEEE Trans Industr Inf 17(2):1397–1406

5. Marion B (2008) Comparison of predictive models for photovoltaic module performance. 2008 33rd IEEE Photovoltaic Specialists Conference. IEEE, pp 1–6

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