Survey of Wind Power Output Power Forecasting Technology
DOI:
https://doi.org/10.54097/hset.v50i.8490Keywords:
Wind power, non-linearity, power system, artificial intelligence modelAbstract
Because of the randomness of wind energy and the non-linearity of power system, there are many dubious variables that should be noticed when forecasting the output power of the wind power. Physical method is often used in the medium-term forecasting, as its model does not require the historical data of the wind farm. The statistical method is simple and requires a small amount of data. It can be applied in those situations where data acquisition is difficult. The artificial intelligence model is suitable in the random or non — linear system as it does not rely on the accurate mode of the objective. The combined forecasting model maximizes favorable factors and minimizes unfavorable ones as contained in above-mentioned methods. This article gives out a brief summary and proposes some improvement measures against the main existing problems in prediction field.
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References
Zhao, X., Wang, S., Li, T. (2011). Review of Evaluation Criteria and Main Methods of Wind Power Forecasting. Energy Procedia, 12: 761–769.
Feng, S.L., Wang, W.S., Liu, C., Dai, H.Z. (2010). Study on the Physical Approach to Wind Power Prediction. Proceedings of the CSEE, (02):1–6.
Croonenbroeck, C., Dahl, C. M. (2014). Accurate medium-term wind power forecasting in a censored classification framework. Energy, 73: 221–232.
Bossanyi, E.A. (1985). Short-Term Wind Prediction Using Kalman Filters. Wind Engineering, 9 (1): 1–8.
Han, J. (2013). Review on Prediction Method of Wind Power. China Electric Power (Technology Edition), (01): 43–46.
Huang, J.H., Peng, H. (2009). Study of Wind Power Short-Term Prediction of Wind Farm Based on Neural Network. Electrotechnics Electric, (09): 57–60.
Qi, S.B., Wang, W.Q., Zhang, X.Y. (2009). Wind Speed and Wind Power Prediction Based on SVM. East China Electric Power, 37(09): 1600–1603.
Tascikaraoglu, A., Uzunoglu, M. (2014). A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34: 243–254.
Chang, G.W., Lu, H.J., Wang, P.K., Chang, Y.R., Lee, Y.D. (2017). Gaussian mixture model-based neural network for short-term wind power forecast. International Transactions on Electrical Energy Systems, 27(6), e2320.
Zhou, B., Liu, C., Li, J., Sun, B., Yang, J., Arpino, F. (2020). A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information. Mathematical Problems in Engineering, 1–12.
Yuan, X., Chen, C., Yuan, Y., Huang, Y., Tan, Q. (2015). Short-term wind power prediction based on LSSVM–GSA model. Energy Conversion and Management, 101: 393–401.
Li, L., Cen, Z.Y., Tseng, M.L., Shen, Q., Ali, M.H. (2020). Improving short-term wind power prediction using hybrid improved cuckoo search arithmetic - support vector regression machine. Journal of Cleaner Production, 123739.
Zhang, Y., Wang, J., Wang, X. (2014). Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, 32: 255–270.
Shi, K.F. (2020). Ultra-Short-Term Wind Power Prediction Based on Combined Model. Yanshan University.
Mao, Y., Wang, S.S. (2016). A review of wind power forecasting & prediction. In: 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Beijing. pp. 1–7.
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