With the continuous depletion of fossil energy and the continuous destruction of the ecological environment, wind energy(WE) and photovoltaic PG(PPG) are more and more emerging power generation(PG) technologies, and WE and PPG systems will become the mainstream of PG in my country's power grid in the future industry. PPG will be affected by the weather, resulting in great uncertainty in the PG output of the photovoltaic system(PS). If it is integrated into the large power grid, it will also bring immeasurable impact to the power grid. Therefore, in order to ensure the safe and stable operation of the power system and the coordinated development of power supply and distribution, it is crucial to use big data technology to establish a PG prediction model(PM). For wind PG, this paper establishes a short-term(ST) PG PM based on BP-NN, and proposes several correction models; for PPG, this paper establishes LS-SVM combined with Markov's PM, which can predict both sunny and rainy days. Predict the PPG in different weather. Through the research on wind PG forecasting methods and PPG forecasting methods, the accurate forecast of ST PG can be achieved, which provides certain theoretical and technical support for the PG industry of the power grid to better supply power to users in the future.

Chadie Altrjmane. Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis. Academic Journal of Energy (2020), Vol. 1, Issue 3: 9-16. https://doi.org/10.38007/RE.2020.010302.

[1] Ando R , Ishii H , Hayashi Y , et al. A Planned PG for Battery-Assisted PS Using ST Forecast. IEEE Access, 2020, PP(99):1-1.

[2] Kim J , Tianshu X U , Tsuneoka Y , et al. Empirical study on effective utilization of PPG by precooling and preheating operation in house. Journal of Environmental Engineering (Transactions of AIJ), 2019, 84(755):73-81. https://doi.org/10.3130/aije.84.73

[3] Das U K , Tey K S , Seyedmahmoudian M , et al. Forecasting of PPG and model optimization: A review. Renewable and Sustainable Energy Reviews, 2018, 81(pt.1):912-928. https://doi.org/10.1016/j.rser.2017.08.017

[4] Akhter, Muhammad, Naveed, et al. Review on forecasting of PPG based on machine learning and metaheuristic techniques. IET Renewable PG, 2019, 13(7):1009-1023. https://doi.org/10.1049/iet-rpg.2018.5649

[5] Lopes F M , Silva H G , Rui S , et al. ST forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal. Solar Energy, 2018, 170(AUG.):14-30.

[6] Harrison G P , Sun W . Wind-solar complementarity and effective use of distribution network capacity. Applied Energy, 2019, 247(AUG.1):89-101.

[7] Bugala A , Zaborowicz M , Boniecki P , et al. ST forecast of generation of electric energy in PSs. Renewable & Sustainable Energy Reviews, 2018, 81(pt.1):306-312. https://doi.org/10.1016/j.rser.2017.07.032

[8] Ahmed A , Khalid M . An intelligent framework for ST multi-step wind speed forecasting based on Functional Networks. Applied Energy, 2018, 225(SEP.1):902-911.

[9] Lenzi A , Steinsland I , Pinson P . Benefits of spatio-temporal modelling for short term wind power forecasting at both individual and aggregated levels. Environmetrics, 2018, 29(3):e2493.1-e2493.17. https://doi.org/10.1002/env.2493

[10] Umizaki M , Uno F , Oozeki T . Estimation and forecast accuracy of regional PPG with upscaling method using the large monitoring data in Kyushu, Japan. IFAC-PapersOnLine, 2018, 51( 28):582-585. https://doi.org/10.1016/j.ifacol.2018.11.766

[11] Eseye A T , Lehtonen M ,  Tukia T , et al. Adaptive Predictor Subset Selection Strategy for Enhanced Forecasting of Distributed PV PG. IEEE Access, 2019, PP(99):1-1.

[12] Hafiz F , Awal M A , Queiroz A , et al. Real-time Stochastic Optimization of Energy Storage Management using Deep Learning based Forecasts for Residential PV Applications. IEEE Transactions on Industry Applications, 2020, PP(99):1-1. https://doi.org/10.1109/IAS.2019.8912315

[13] Agoua X G , Girard R , Kariniotakis G . Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting. IEEE Transactions on Sustainable Energy, 2019, 10(2):780-789.

[14] Saleh M , Meek L , Masoum M , et al. Battery-Less ST Smoothing of Photovoltaic Generation Using Sky Camera. IEEE Transactions on Industrial Informatics, 2018, 14(2):403-414. https://doi.org/10.1109/TII.2017.2767038

[15] Carriere T , Kariniotakis G . An Integrated Approach for Value-Oriented Energy Forecasting and Data-Driven Decision-Making Application to Renewable Energy Trading. IEEE Transactions on Smart Grid, 2019, 10(6):6933-6944. https://doi.org/10.1109/TSG.2019.2914379

[16] Lingohr D , Mueller G . Stochastic modeling of intraday PPG. Energy Economics, 2019, 81(JUN.):175-186. https://doi.org/10.1016/j.eneco.2019.03.007

[17] Sobri S , Koohi-Kamali S ,  Abd Rahim N . Solar photovoltaic generation forecasting methods: A review. Energy Conversion & Management, 2018, 156(JAN.):459-497.

[18] Aladin S , Tran A , Allogba S , et al. Quality of Transmission Estimation and ST Performance Forecast of Lightpaths. Journal of Lightwave Technology, 2020, PP(99):1-1.

[19] Kushwaha V , Pindoriya N M . A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very ST solar PV PG forecast. Renewable Energy, 2019, 140(SEP.):124-139. https://doi.org/10.1016/j.renene.2019.03.020