In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment.

To evaluate the effects of water velocity and artificial substratum characteristics on the growth rate and biomass accumulation of periphyton, an artificial stream mesocosm experiment was conducted using alternative water sources collected from the Mangwall Stream (MW), the Han River (HR), and bank filtration water (BFW) from the Han River in the Republic of Korea. The measured concentrations of organic matter and inorganic nutrients in the MW were higher than in the HR and BFW. The surface of tile is relatively smooth and nonporous, whereas the surfaces of concrete and pebble are rough with numerous isolated pores in which filamentous periphyton become immobilized against hydrodynamic shear stress and mat tensile strength. Compared with the periphyton biomass of the HR and BFW, the peak biomass in the MW was significantly higher due to higher nutrient concentrations in the MW. Reasonable linear relationships (R² ≥ 0.69) between water velocity and total periphyton biomass/growth rate were obtained, indicating that water velocities above critical values can cause a reduction in biomass accrual. In addition, reasonable relationships (R² ≥ 0.58) between specific surface area and total periphyton biomass were obtained for the HR and BFW, indicating that an increase in the specific surface area of the substratum can lead to an increase in periphyton biomass in a nutrient-poor water body. Principal components analysis (PCA) results indicate that nutrient concentrations were the first dominant limiting factor for the growth and accumulation of periphyton, and water velocity and the specific surface area of the substratum were determined to be potential limiting factors. Consequently, the growth rate and biomass accumulation of periphyton were considered to be a complex function of nutrient concentrations, water velocities, and substratum characteristics.

Both status and progress in smart water metering (SWM) implementations in five selected countries (i.e., United States of America, United Kingdom, Australia, Israel, and South Korea) are investigated in this study. Despite the countless benefits of SWM implementation, the diffusion of the SWM technologies has been slow due to various challenges, including the absence of compulsory water policies, the lack of support from customers and expertise, and weak cost–benefit analysis. Over the past 30 years, the aforementioned countries have transitioned from a fixed charging to a volumetric charging regime composed of traditional water meters and SWM. Both the status and progress of SWM implementation are quite different among countries, although governments across the world have been applying water policies responding to water scarcity, population growth, and water demand management. However, the absence of strong water policies and political support for SWM implementation resulted in the slow and retarded spread of SWM implementation. Although several changes in water policies have occurred since 1990, there is no compulsory law for SWM implementation. Between 1995 and 2010, pilot/trial cases for SWM were dominant. After 2010, the number of SWM implementation kept increasing and all countries experienced more concentrated SWM implementation, despite the variances in both endpoints and completion of SWM implementation depending on water policies (i.e., acts and regulations) encouraging SWM implementations. The global market for SWM has consistently grown to USD 5.92 billion in 2020. Finally, the application of favourable water policies to optimize the use of water resources and to promote sustainable development is expected to drive the SWM market further.

The sustainable management of global groundwater resources is increasingly challenged by climatic uncertainty and escalating anthropogenic stress. Thus, there is a need for simulation tools that are more robust and flexible. This systematic review addresses the integration of two dominant modeling paradigms: the physically grounded Modular Finite-Difference Flow (MODFLOW) model and the data-agile Artificial Neural Network (ANN). While the MODFLOW model provides deep process-based understanding, it is often limited by extensive data requirements and computational intensity. In contrast, an ANN offers remarkable predictive accuracy and computational efficiency, particularly in complex, non-linear systems, but traditionally lacks physical interpretability. This review synthesizes existing research to present a functional classification framework for MODFLOW–ANN integration, providing a systematic analysis of the literature within this structure. Our analysis of the literature, sourced from Scopus, Web of Science, and Google Scholar reveals a clear trend of the strategic integration of these models, representing a new trend in hydrogeological simulation. The literature reveals a classification framework that categorizes the primary integration strategies into three distinct approaches: (1) training an ANN on MODFLOW model outputs to create computationally efficient surrogate models; (2) using an ANN to estimate physical parameters for improved MODFLOW model calibration; and (3) applying ANNs as post-processors to correct systematic errors in MODFLOW model simulations. Our analysis reveals that these hybrid methods consistently outperform standalone approaches by leveraging ANNs for computational acceleration through surrogate modeling, for enhanced model calibration via intelligent parameter estimation, and for improved accuracy through systematic error correction.

Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

Urban flooding has become increasingly frequent due to the rising intensity of rainfall driven by urban development and climate change. Effective prevention measures are crucial to mitigate the significant human and material damages caused by such events. Rapid and accurate pre-detection techniques can help to reduce the impacts of urban flooding. With the advancement of deep learning, deep neural networks (DNNs) have been successfully applied across various domains, including computer vision and speech recognition. In particular, DNNs for computer vision demonstrate high performance with relatively low computational costs. In this paper, we propose a flooding region segmentation model for urban underpasses based on the U-Net architecture. To train and evaluate the model, we collected datasets from the Mannyeon, Oryang, and Daedong underpasses in Daejeon. The proposed method achieved Dice coefficients of 98.8%, 94.03%, and 93.85%, respectively. This model demonstrates high segmentation performance in detecting flooded regions and can be integrated into continuous flood monitoring systems.