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A systematic review on predicting PV system parameters using machine learning.

Md Jobayer1, Md Al Hasan Shaikat1, Md Naimur Rashid1

  • 1BRAC University, Dhaka 1212, Bangladesh.

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Summary

Machine learning (ML) methods offer accurate and fast photovoltaic (PV) parameter estimation. This review of 2020-2022 studies shows neural networks are the most popular ML approach for PV system analysis.

Keywords:
Machine learningPhotovoltaicsSystem parameter estimationSystematic review

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Materials Science

Background:

  • Assessing photovoltaic (PV) system performance is crucial due to increasing energy demands.
  • Traditional PV parameter estimation methods (e.g., satellite data, IV characteristics) lack sufficient reliability.
  • Machine learning (ML) offers a faster and more accurate alternative for PV system parameter estimation.

Purpose of the Study:

  • To systematically review and analyze machine learning-based studies for photovoltaic (PV) parameter estimation published between 2020 and 2022.
  • To identify the most prevalent ML algorithms, data sources, and evaluation metrics in recent PV research.
  • To provide insights for researchers and stakeholders to optimize PV system performance and guide future research directions.

Main Methods:

  • Systematic literature review of ML-based PV parameter estimation studies from 2020-2022.
  • Analysis of selected studies based on ML algorithm, data source (hardware vs. simulation), sample size, and error metrics.
  • Quantitative assessment of the frequency of different ML algorithms and evaluation metrics used.

Main Results:

  • Neural networks were the most frequently used ML algorithm (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%).
  • Computer simulations were the primary data source (66%), with hardware tests (18%) and combined approaches (16%) also utilized.
  • Root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%) were the most common error metrics.

Conclusions:

  • Machine learning, particularly neural networks, is a dominant and effective approach for photovoltaic parameter estimation.
  • The prevalence of simulation data highlights a need for more hardware-based validation in ML for PV systems.
  • This review aids in understanding ML algorithm efficacy for PV systems, informing policy, investment, and optimization strategies.