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Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm

Author

Listed:
  • Yuhao Zhang

    (School of Mechanic and Electronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China)

  • Ting Li

    (School of Mechanic and Electronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China)

  • Tianyi Ma

    (School of Mechanic and Electronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China)

  • Dongsheng Yang

    (School of Information Science and Engineering, Northeastern University, Shenyang 110006, China)

  • Xiaolong Sun

    (School of Mechanic and Electronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China)

Abstract

Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction.

Suggested Citation

  • Yuhao Zhang & Ting Li & Tianyi Ma & Dongsheng Yang & Xiaolong Sun, 2024. "Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm," Energies, MDPI, vol. 17(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:960-:d:1341341
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    References listed on IDEAS

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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & Andres Muñoz-Jimenez & Pedro M. Lara-Santillan, 2013. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
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