Scholarlink

<p>Artificial Intelligence (AI) has become increasingly integrated into everyday life, with the general population progressively relying on it for even routine tasks. As AI models grow in complexity, precision, and computational power, their energy consumption rises exponentially, raising serious concerns about the sustainability of their widespread adoption. The field of green learning aims to mitigate these concerns by developing energy-efficient AI solutions. In this work, we propose a method to preserve the accuracy of photovoltaic (PV) power generation forecasting while reducing the environmental impact through dataset size reduction. Our approach employs a heuristic strategy that iteratively reduces the training dataset until a cutoff point is reached, balancing predictive accuracy and environmental efficiency. Experimental results using publicly available PV generation datasets demonstrate that the proposed data reduction method decreases training time by up to 17.13%, with only a 1.47% decline in prediction accuracy. These findings highlight the potential of the method to substantially reduce the carbon footprint of AI applications with minimal performance degradation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A heuristic dataset reduction for green computing of photovoltaic power generation prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

查看全文
  • Ana Paula Aravena-Cifuentes,
  • J. David Nuñez-Gonzalez,
  • Manuel Graña

摘要

Artificial Intelligence (AI) has become increasingly integrated into everyday life, with the general population progressively relying on it for even routine tasks. As AI models grow in complexity, precision, and computational power, their energy consumption rises exponentially, raising serious concerns about the sustainability of their widespread adoption. The field of green learning aims to mitigate these concerns by developing energy-efficient AI solutions. In this work, we propose a method to preserve the accuracy of photovoltaic (PV) power generation forecasting while reducing the environmental impact through dataset size reduction. Our approach employs a heuristic strategy that iteratively reduces the training dataset until a cutoff point is reached, balancing predictive accuracy and environmental efficiency. Experimental results using publicly available PV generation datasets demonstrate that the proposed data reduction method decreases training time by up to 17.13%, with only a 1.47% decline in prediction accuracy. These findings highlight the potential of the method to substantially reduce the carbon footprint of AI applications with minimal performance degradation.