The rapid growth of wind energy has increased the need for advanced condition monitoring (CM), predictive maintenance, and remaining useful life (RUL) estimation strategies for wind turbines. In this context, digital twins (DTs) have emerged as a key tool for improving reliability, availability, and operational efficiency by integrating physical models, operational data, and artificial intelligence (AI). This paper presents a systematic literature review (SLR) aimed at analyzing the state of the art, classifying the main applications, and identifying research gaps. A rigorous search protocol was applied across scientific databases, considering inclusion and exclusion criteria and analysis categories aligned with four research questions. The results show a high concentration of studies on critical wind turbine components, a predominance of hybrid physics-based and data-driven approaches, and an increasing use of deep learning (DL) models. However, several research gaps remain, including the predominance of component-level digital twin implementations rather than system-level architectures, the lack of standardized datasets and benchmarking frameworks, and challenges related to SCADA data heterogeneity and real-time scalability. It is concluded that DTs are evolving toward more autonomous and prescriptive systems; however, they still require further maturation for widespread industrial adoption.

INTRODUCCIÓN: Se aborda la problemática de confirmar resultados experimentales a través de un modelo matemático que permita predecir el comportamiento de variables térmicas, en un módulo de aprovechamiento solar para la generación de aire caliente. OBJETIVO: Desarrollar un modelo matemático que describa los procesos internos de intercambio de calor en un colector solar de placas planas. MÉTODO: La investigación se realiza bajo el doble enfoque: teórico y experimental. Se procede a la medición de los principales parámetros de funcionamiento del colector solar de placas planas, la elaboración del modelo matemático y la realización del balance térmico. RESULTADOS: Utilizando el recurso Guide de MATLAB, se programó una aplicación para la ejecución del modelo matemático, estableciéndose las constantes que corresponde a datos geométricos del módulo solar y del ducto, características del aislante y condiciones térmicas de radiación de los materiales utilizados. Finalmente, definidos los datos de entrada, se realizan las iteraciones correspondientes y se obtienen los resultados de cálculo, los ajustes de temperaturas se realizaron con una divergencia inferior al 1%. DISCUSIÓN Y CONCLUSIONES: El error promedio que representaría el desface entre el modelo propuesto respecto a los parámetros reales de funcionamiento, fue de un 2,55%, aceptables desde el punto de vista estadístico. Se confirman resultados de investigaciones precedentes en cuanto a buenos rendimientos para un caudal másico de 0,0204 kg/s. Se logró la evaluación de un módulo del aprovechamiento solar para la generación de aire caliente que posee enormes potencialidades para la calefacción de viviendas de la región sierra ecuatoriana.

The performance of the thermal envelope in buildings plays a crucial role in the energy consumption used for heating and cooling, in the well-being of occupants, as well as in the costs associated with its installation and maintenance, especially in regions with extreme climates. However, selecting the optimal thickness represents a challenge for designers since, in certain situations, as the thickness of the thermal insulation increases, the heating demand decreases while the cooling demand tends to increase. This study focused on determining the optimal insulation thicknesses in a hypothetical 1200 m² building located in Salamanca, Spain, using the EnergyPlus software to perform an annual simulation, where technical and economic aspects were considered. For the development of this work, two scenarios were established. In the first scenario, the thicknesses of expanded polystyrene (EPS) insulation in the floor, roof, and walls were independently varied, with values ranging between 0.01 m and 0.08 m. In the second scenario, the thicknesses were adjusted uniformly in these areas. The results showed that the second option was the most suitable, with a payback period of 7.9 years. These analyses considered specific climatic parameters, detailed indoor conditions, and corresponding thermal loads, offering results that enable informed decision-making regarding the design and financing of building thermal envelopes.