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Zhou, Xinyu; Ao, Yanshuang; Wang, Xinlu; Guo, Xifeng; Dai, Wei

Learning with privileged information for short-term photovoltaic power forecasting using stochastic configuration network. (English) Zbl 07834438

Inf. Sci. 619, 834-848 (2023).
Summary: The optimal balance and dispatch of power plants in a smart grid require an accurate short-term forecast of photovoltaic (PV) power generation. The climatic condition may have an impact on the PV output, but it is difficult to be used in forecasting due to untimely sampling of meteorological data. To this end, this paper presents an incremental learning using privileged information (LUPI) paradigm for PV power forecasting by using stochastic configuration network. This novel algorithm can employ the meteorological data as privileged information for building PV power forecasting model in the training stage. Additionally, the model performance has been fully discussed in this paper. Finally, experimental results indicate that the proposed model indeed performs favorably in PV power forecasting.

Cited in 2 Documents

MSC:

68-XX Computer science
91-XX Game theory, economics, finance, and other social and behavioral sciences

Keywords:

learning using privileged information; stochastic configuration network; incremental learning; photovoltaic power

Software:

JStatCom
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Full Text: DOI

References:

[1] Shafi; Sharadga, H.; Hajimirza, S., Design of optimal power point tracking controller using forecasted photovoltaic power and demand, IEEE Trans. Sustain. Energy, 11, 3, 1820-1828 (2020)
[2] Rabiul Islam, M.; Mahfuz-Ur-Rahman, A. M.; Muttaqi, K. M.; Sutanto, D., State-of-the-art of the medium-voltage power converter technologies for grid integration of solar photovoltaic power plants, IEEE Trans. Energy Convers., 34, 1, 372-384 (2019)
[3] Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Monache, L. D., Short-term photovoltaic power forecasting using artificial neural networks and an analog ensemble, Renew. Energ., 108, 274-286 (2017)
[4] Niu, D. X.; Wang, K. K.; Sun, L. J.; Wu, J.; Xu, X. M., Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: a case study, Appl. Soft Comput., 93, Article 106389 pp. (2020)
[5] De Giorgi, M. G.; Congedo, P. M.; Malvoni, M., Photovoltaic power forecasting using statistical methods: impact of weather data, IET Sci. Meas. Technol., 8, 3, 90-97 (2014)
[6] Kroposki; Johnson, B.; Zhang, Y. C.; Gevorgian, V.; Denholm, P.; Hodge, B.-M.; Hannegan, B., Achieving a 100
[7] Wang, F.; Xuan, Z. M.; Zhen, Z.; Li, K. P.; Wang, T. Q.; Min, S., A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework, Energy Convers. Manag., 212, Article 112766 pp. (2020)
[8] Bracale; Carpinelli, G.; Falco, P. D., A probabilistic competitive ensemble method for short-term photovoltaic power forecasting, IEEE Trans. Sustain. Energy, 8, 2, 551-560 (2017)
[9] Guo, X. F.; Gao, Y.; Zheng, D.; Ning, Y.; Zhao, Q. N., Study on short-term photovoltaic power forecasting model based on the stacking ensemble learning, Energy Rep., 6, 9, 1424-1431 (2020)
[10] Li, P. T.; Zhou, K. L.; Lu, X. H.; Yang, S. L., A hybrid deep learning model for short-term PV power forecasting, Appl. Energy, 259, Article 114216 pp. (2020)
[11] Vapnik, V.; Vashist, A., A new learning paradigm: learning using privileged information, Neural Netw., 22, 5-6, 544-557 (2009) · Zbl 1335.68212
[12] Lapin, M.; Hein, M.; Schiele, B., Learning using privileged information: SVM+ and weighted SVM, Neural Netw., 53, 95-108 (2014) · Zbl 1308.68098
[13] Zhang, P. B.; Yang, Z. X., A new learning paradigm for random vector functional-link network: RVFL+, Neural Netw., 122, 94-105 (2020)
[14] Fouad, S.; Tino, P.; Raychaudhury, S.; Schneider, P., Incorporating privileged information through metric learning, IEEE Trans. Neural Netw. Learn. Syst., 24, 7, 1086-1098 (2013)
[15] Xu, X. X.; Li, W.; Xu, D., Distance metric learning using privileged information for face verification and person re-identification, IEEE Trans. Neural Netw. Learn. Syst., 26, 12, 3150-3162 (2015)
[16] Dai, W.; Ao, Y. S.; Zhou, L. N.; Zhou, P.; Wang, X. S., Incremental learning paradigm with privileged information for random vector functional-link networks: IRVLF+, Neural Comp. Appl., 34, 9, 6847-6859 (2022)
[17] V. Sharmanska, N. Quadrianto, C. H. Lampert, Learning to rank using privileged information, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 825-832.
[18] Wang, D. H.; Li, M., Stochastic configuration networks: fundamentals and algorithms, IEEE Trans. Cybern., 47, 10, 3466-3479 (2017)
[19] Wang, D. H.; Li, M., Robust stochastic configuration networks with kernel density estimation for uncertain data regression, Inf. Sci., 412-413, 210-222 (2017) · Zbl 1443.62315
[20] Huang, C. Q.; Huang, Q. H.; Wang, D. H., Stochastic configuration networks based adaptive storage replica management for power big data processing, IEEE Trans. Ind. Informat., 16, 1, 373-383 (2020)
[21] Lu, J.; Ding, J. L., Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks, Inf. Sci., 486, 119-132 (2019)
[22] Lu, J.; Ding, J. L.; Dai, X. W.; Chai, T. Y., Ensemble stochastic configuration networks for estimating prediction intervals: a simultaneous robust training algorithm and its application, IEEE Trans. Neural Netw. Learn. Syst., 31, 12, 5426-5440 (2020)
[23] Lu, J.; Ding, J. L., Mixed-distribution-based robust stochastic configuration networks for prediction interval construction, IEEE Trans. Ind. Inform., 16, 8, 5099-5109 (2020)
[24] Lu, J.; Ding, J. L.; Liu, C. X.; Chai, T. Y., Hierarchical-Bayesian-Based sparse stochastic configuration networks for construction of prediction intervals, IEEE Trans. Neural Netw. Learn. Syst. Early access (2021)
[25] Dai, W.; Li, D. P.; Zhou, P.; Chai, T. Y., Stochastic configuration networks with block increments for data modeling in process industries, Inf. Sci., 484, 367-386 (2019)
[26] Dai, W.; Zhou, X. Y.; Li, D. P.; Zhu, S.; Wang, X. S., Hybrid parallel stochastic configuration networks for industrial data analytics, IEEE Trans. Ind. Inform., 18, 4, 2331-2341 (2022)
[27] KEEL Data-Mining Software Tool: Data Set Repository, Integration of algorithms and experimental analysis framework. 2011. [Online], avail-able: http://www.keel.es/.
[28] Datafountain, [Online], available: https://www.datafountain.cn/competitions/303/datasets.
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