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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models
(MDPI AG, 2025-04-04)
Pedro Torres-Bermeo
;
Kevin López-Eugenio
;
;
Guillermo Palacios-Navarro
;
José Varela-Aldás
The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.
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Correction: Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial
(2024)
Jorge Buele
;
Fátima Avilés-Castillo
;
;
José Varela-Aldás
;
Guillermo Palacios-Navarro
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Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial
(2024)
Jorge Buele
;
Fátima Avilés-Castillo
;
;
José Varela-Aldás
;
Guillermo Palacios-Navarro
<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant.</jats:p> </jats:sec><jats:sec> <jats:title>Objective</jats:title> <jats:p>To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>Thirty-four older adults (men, women) with MCI were randomized to the experimental group (<jats:italic>n</jats:italic> = 17; 75.41 ± 5.76) or control (<jats:italic>n</jats:italic> = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions).</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (<jats:italic>p</jats:italic> < 0.001), with large effect sizes. There was no statistically significant improvement in any of the groups when evaluating their performance in ADLs (control, <jats:italic>p</jats:italic> = 0.28; experimental, <jats:italic>p</jats:italic> = 0.46) as expected. The completion rate in the experimental group was higher (82.35%) compared to the control group (70.59%). Likewise, participants in the experimental group reached a higher level of difficulty in the application and needed less time to complete the task at each level.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The application of a dual intervention, through motor training prior to a cognitive task based on Immersive VR was shown to be a beneficial non-pharmacological strategy to improve cognitive functions and reduce depression in patients with MCI. Similarly, the control group benefited from such dual intervention with statistically significant improvements.</jats:p> </jats:sec><jats:sec> <jats:title>Trial registration</jats:title> <jats:p>ClinicalTrials.gov NCT06313931; <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://clinicaltrials.gov/study/NCT06313931">https://clinicaltrials.gov/study/NCT06313931</jats:ext-link>.</jats:p> </jats:sec>
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