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This study incorporates advanced parallelism methods using GPU computing to accelerate the process of convergence to an objective, providing faster results for Particle Swarm Optimization (PSO), a bio-inspired stochastic optimization algorithm used to make predictions in various fields. The two proposed distributed implementations of PSO with Apache Spark further enable comprehensive optimisation of both the algorithm structure and its parameters, leading to improved predictive accuracy. Therefore, this approach provides a new and innovative solution in the field of energy consumption prediction, which can be implemented in a distributed edge–computing solution with optimal performance.

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GPU-Accelerated PSO for Neural Network-Based Energy Consumption Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Manuel I. Capel,
  • Alberto Salguero–Hidalgo,
  • Juan A. Holgado-Terriza

摘要

This study incorporates advanced parallelism methods using GPU computing to accelerate the process of convergence to an objective, providing faster results for Particle Swarm Optimization (PSO), a bio-inspired stochastic optimization algorithm used to make predictions in various fields. The two proposed distributed implementations of PSO with Apache Spark further enable comprehensive optimisation of both the algorithm structure and its parameters, leading to improved predictive accuracy. Therefore, this approach provides a new and innovative solution in the field of energy consumption prediction, which can be implemented in a distributed edge–computing solution with optimal performance.