Elman-based forecaster integrated by Adaboost algorithm in 15 min and 24 h ahead power output prediction using PM 2.5 values, PV module temperature, hours of sunshine, and meteorological data

• Autorzy: Zhou J. , Li W. , Yu X. , Xu X. , Yuan X. , Wang J.
• Języki publikacji: EN

Abstrakty

EN

Nowadays, with the depletion of fossil energy and deterioration of environmental quality, solar energy is perceived to be a renewable and clean energy. While developing rapidly all over the world, solar energy is also faced with many challenges resulting from its inherent properties. In order to reduce the impact on the grid and facilitate scheduling, it is a growing problem to build a feasible model to forecast PV power with high precision. Therefore, this paper proposes an Elman-based forecaster integrated by Adaboost algorithm, namely Adaboost + Elman. Before forecasting, input variables containing PM 2.5 values, temperature of the PV module, sunshine hours, and meteorological data are made using correlation, clustering, and discriminate analysis to avoid information redundancy and improve the generalization ability of the model. To verify the developed model’s application to short-term PV forecasting in two different time scales, data of Huangsi in 2016 are used for model construction and verification. An additional 7 models are introduced to make comparison. Experimental results prove that the proposed model is effective and practicable for two different scales of short-term PV power prediction.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2019
• Tom: 28
• Numer: 3
• Opis fizyczny: p.1999-2008,fig.,ref.

Twórcy

autor

Zhou J.

• Department of Economics and Management, North China Electric Power University, Baoding, China

autor

Li W.

• Department of Economics and Management, North China Electric Power University, Baoding, China

autor

Yu X.

• Department of Economics and Management, North China Electric Power University, Baoding, China

autor

Xu X.

• Department of Economics and Management, North China Electric Power University, Baoding, China

autor

Yuan X.

• Dezhou Power Supply Company, Dezhou City, Shandong Province, China

autor

Wang J.

• Spic Ningjin Thermoelectricity Co., Ning Jin County, China

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Identyfikatory

DOI

10.15244/pjoes/89539