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Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree approach

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  • Kumar Ganti, Praful
  • Naik, Hrushikesh
  • Kanungo Barada, Mohanty

Abstract

This paper proposes a hybrid technique to recover the efficiency of solar photovoltaic (PV) energy system from the environmental impacts. The proposed technique is the combination of both sparrow search algorithm and gradient boosting decision tree; thus it is named SSA-GBDT method. The purpose of the proposed technique is to improve the efficiency of solar PV energy system and maximization of power removal from PV arrays. The PV module voltage, current and power is measured by SSA and it creates possible database offline. Database with electric parameters is utilized to develop the model online using GBDT. The data set contains some parameters like particle size and dust weight input and maximal power value output variables. Then, the proposed technique is implemented on the MATLAB/Simulink platform and the performance is compared with existing techniques. The performance of PV under normal condition, dust accumulation condition, water drops condition and partial shading conditions are the considered cases. In the cases, photovoltaic reference irradiance and temperature, PV current, voltage and generated power, active and reactive power, grid current and voltage, inverter power is also evaluated. The efficiency comparison of PV power for solution processes like ANN, GBDT, SSA and proposed system are also analyzed.

Suggested Citation

  • Kumar Ganti, Praful & Naik, Hrushikesh & Kanungo Barada, Mohanty, 2022. "Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree appr," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028103
    DOI: 10.1016/j.energy.2021.122561
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    1. Tuba Tanyıldızı Ağır, 2024. "Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    2. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    3. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).

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