Planificación, gestión, operación y evaluación de microrredes eléctricas aisladas es el resultado de un trabajo de investigación orientado a enfrentar los retos técnicos, económicos, ambientales y sociales de las microrredes. Estos sistemas eléctricos autónomos representan una alternativa estratégica para ampliar el acceso a la electricidad en zonas remotas y no interconectadas, contribuyendo a la sostenibilidad y a la transición energética. La obra ofrece un marco integral para abordar cuatro dimensiones esenciales. En primer lugar, la planificación, entendida como el proceso de dimensionamiento tecnoeconómico y socioambiental que permite definir el tamaño y la configuración más adecuados de una microrred. En segundo lugar, la gestión, que estudia las formas más eficientes de administrar los recursos energéticos distribuidos. En tercer lugar, la operación, que plantea metodologías orientadas a garantizar un funcionamiento confiable y estable. Finalmente, la evaluación, que aporta indicadores y herramientas para medir el desempeño y orientar mejoras continuas. La investigación presentada recurre a enfoques novedosos que integran técnicas de optimización multiobjetivo, multinivel y multicriterio, computación basada en reglas, programación lineal y estrategias de control distribuido. Cada capítulo desarrolla un aspecto clave: desde la planificación en zonas no interconectadas, pasando por la gestión energética, el papel de los sistemas de almacenamiento, hasta la construcción de modelos de evaluación. Esta obra constituye una referencia fundamental para investigadores, profesionales, estudiantes y tomadores de decisión interesados en el diseño, implementación y validación de microrredes, ofreciendo metodologías prácticas y adaptadas al contexto latinoamericano.

Accurately estimating wind speed and direction is essential for assessing wind energy potential, as precise estimates enable better identification of optimal generation periods and improved resource planning. While NWP models like ERA5 and JMA MSM provide valuable insights, they often entail substantial computational complexity and time dependence, limiting their practical applicability for local-scale assessments. This study proposes an efficient framework that combines IDW interpolation, logarithmic wind-profile theory, and Ekman spiral dynamics to estimate wind speed and direction from nearby weather stations. Unlike complex NWP models, our approach requires fewer computational resources, making it suitable for distributed, station-based applications. We evaluate the proposed method using five years of JMA AMeDAS data from 806 stations and compare performance against ERA5 and JMA MSM using multiple statistical metrics. Results show that the method achieves lower average and median RMSE, MAE, and MedAE, validating it as a viable alternative to conventional NWP models to estimate wind speed and direction at 10 m height. By combining simpler methods, this method offers an alternative tool to find wind speed and direction in unknown locations without the overhead of complex numerical models.

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia in cardiac pathology. The incidence of AF begins at a very early age and its initial state is paroxysmal atrial fibrillation (PAF). This type of heart disease can be detected and predicted by analyzing the spectrogram of a surface electrocardiogram (ECG) signal. In many studies, different ECG signal formats and convolutional neural network (CNN) architectures have been used. However, the lack of good signal preprocessing or signal adequacy may have affected the accuracy, especially on short-term ECG signals. In this study, we analyzed a preprocessed ECG signal, determined the optimal set to predict PAF, and evaluated the accuracy using ECG signals of different durations. The PAF Prediction Challenge–PhysioNet database was used to extract spectrograms in 30-sec and 5-sec windows for two classes (Normal, PAF) and 3 classes (Normal, Close-AF, Distant-AF). Then, the AlexNet architecture was used. The proposed method achieved a two-class accuracy of 99.92% with a 30-sec window and 99.42% with a 5-sec window, improving the PAF prediction performance compared with similar works. In addition, the three-class accuracies were 96.92% and 97.43% with windows of 30-sec, and 5-sec, respectively. These results prove the efficacy of the method for the early diagnosis of PAF, even based on short-term ECG signals.