https://hdl.handle.net/20.500.11984/14090 Mon, 24 Aug 2026 22:59:51 GMT 2026-08-24T22:59:51Z https://hdl.handle.net/20.500.11984/14655 Ander Manterola Aldekoa: zeanuriztar etnografoari elkarrizketa Mentxakatorre Odriozola, Jon Sun, 30 Nov 2025 00:00:00 GMT https://hdl.handle.net/20.500.11984/14655 2025-11-30T00:00:00Z https://hdl.handle.net/20.500.11984/14654 Battery aging-aware adaptive model predictive control based on coupled semi-empirical electro-thermal and aging models Dorronsoro, Xabier; de Castro, Ricardo; Barreras, Jorge Varela; GARAYALDE, ERIK; IRAOLA, UNAI This paper presents an aging-rate aware nonlinear model predictive control (MPC) strategy for battery energy storage systems, integrating a semi-empirical, experimentally validated electro-thermal and degradation model to account for both calendar and cycle aging factors, often neglected in conventional energy management approaches. A key contribution is the introduction of a adaptive weighting method that dynamically adjusts the weights of the MPC cost function according to the battery’s aging state, primarily driven by time-dependent degradation factors. This adaptive mechanism improves control decisions across varying prediction horizons, leading to reductions in both battery degradation and total operating costs by up to 262.7 % and 44.51 %, respectively, when compared to a standard MPC. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.11984/14654 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14653 Power Transformer Modelling for the Evaluation of a Reinforcement Learning-Based Inrush Current Minimization Strategy Ugarte Valdivielso, Jone; Barrenetxea, Manex; Torres, Asier; Stewart, Brian G.; Aizpurua Unanue, Jose Ignacio Inrush currents in power transformers can affect the transformer lifetime and the reliability of electrical grids. Various inrush current minimization techniques have been proposed to mitigate these effects. To validate any minimization strategy, an accurate transformer model is required. This study focuses on developing a transformer model for inrush current minimization. The transformer is modelled through duality-based transformation, recognized for its accurate core representation. In addition, the laboratory environment and its constituent components are also modelled. The developed model is validated against real inrush current measurements. Finally, the model is employed to test a Reinforcement Learning (RL) based inrush current minimization strategy. The performance of the proposed method is validated by contrasting its outcomes with inrush current data obtained from traditional uncontrolled Circuit Breaker (CB) operations. The findings indicate that the proposed inrush current minimization approach reduces the peak inrush current by 77% compared to traditional uncontrolled CB switching. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.11984/14653 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14652 Brains Without Brawn: Evaluating CPU Performance for Code Generation with Large Language Models Illarramendi, Miren; Agirre, Joseba Andoni; Picatoste, Aitor; Igartua, Juan Ignacio This research presents a comparative analysis of the performance of various Large Language Models (LLMs) for code generation tasks executed on Central Processing Units (CPUs) without the use of dedicated Graphics Processing Units (GPUs). The study evaluates key metrics including inference time, code generation accuracy, CPU and memory usage, and energy consumption. By conducting repeated experiments, we assess the impact of model size and optimization on efficiency in environments lacking GPU resources. Energy consumption is measured using tools like CodeCarbon, focusing on the environmental impact of running these models on CPU-based systems. The findings offer insights into the trade-offs between model precision, resource usage, and energy efficiency, providing valuable guidance for developers and researchers aiming to balance performance and sustainability in low-resource computing environments. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.11984/14652 2025-01-01T00:00:00Z