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A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data

  • Research Article - Electrical Engineering
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Abstract

The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg–Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.

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Source: NASA Earth Observations (Terra/MODIS)

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Authors and Affiliations

  1. Department of Electrical Engineering, Delhi Technological University, Delhi, 110042, India

    Astitva Kumar, M. Rizwan & Uma Nangia

Authors
  1. Astitva Kumar
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  2. M. Rizwan
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  3. Uma Nangia
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Corresponding author

Correspondence to Astitva Kumar.

Appendix

Appendix

See Figs. 17, 18 and Table 6.

Fig. 17
figure 17

Experimental setup for solar PV power forecasting

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Fig. 18
figure 18

Linear regression plot of the inputs and the solar photovoltaic power, a PV power versus irradiance, b PV power versus temperature, c PV power versus Linke turbidity, d PV power versus wind speed

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Table 6 Daily PV output and its correlation with the inputs considered
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Kumar, A., Rizwan, M. & Nangia, U. A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data. Arab J Sci Eng 45, 1715–1732 (2020). https://doi.org/10.1007/s13369-019-04183-0

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