@misc{10481/106134,
year = {2025},
month = {6},
url = {https://hdl.handle.net/10481/106134},
abstract = {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 inno vative solution in the field of energy consumption prediction, which can be implemented in a distributed edge–computing solution with optimal performance.},
organization = {This research was funded by MICIU/AEI /10.13039/501100011033, grant PID2020-112495RB-C21},
publisher = {Springer Nature Switzerland AG 2025},
keywords = {GPU},
keywords = {Particle Swarm Optimization},
keywords = {Energy Consumption Optimization},
keywords = {Optimization Research},
keywords = {Predictive Accuracy},
title = {GPU-Accelerated PSO for Neural  Network-Based Energy Consumption  Prediction},
doi = {10.1007/978-3-031-90203-1_26},
author = {Capel Tuñón, Manuel Isidoro and Salguero Hidalgo, Alberto Gabriel and Holgado Terriza, Juan Antonio},
}