Digital Twin (DT) technology is emerging as a powerful tool for optimizing energy efficiency and industrial sustainability. By creating virtual replicas of physical systems, DTs enable real-time monitoring, predictive maintenance, and resource optimization, offering new opportunities to meet growing energy demands. Despite its potential, the comprehension of DT technology’s applications, benefits, and challenges remains limited. This systematic review explores the role of Digital Twins in energy efficiency across various industries. A structured literature search was conducted in IEEE Xplore, Elsevier, Springer, MDPI, and Google Scholar, following PRISMA 2020 guidelines. After applying the predefined inclusion criteria, 50 studies were selected for in-depth analysis. The findings highlight that DT implementation can lead to energy savings of up to 30%, reduce operational costs, and improve predictive maintenance strategies. Their impact is particularly notable in smart buildings, manufacturing, and industrial processes, where real-time data analytics contribute to better energy management. However, significant barriers remain, including high implementation costs, data security risks, and the complexity of integrating DTs with existing infrastructures. By synthesizing the current research, this review underscores the transformative potential of Digital Twins while identifying key challenges that need to be addressed for their wider adoption. Future efforts should focus on developing standardized methodologies, reducing implementation costs, and enhancing cybersecurity measures to maximize their benefits in energy efficiency and sustainability.

An ordinary differential equation (ODE) for velocity distribution in open channel flows is presented based on an analysis of the Reynolds-averaged Navier–Stokes equations and a log-wake modified eddy viscosity distribution. This proposed equation allows to predict the velocity-dip-phenomenon, i.e. the maximum velocity below the free surface. Two different degrees of approximations are presented, a semi-analytical solution of the proposed ODE, i.e. the full dip-modified-log-wake law (fDMLW-law) and a simple dip-modified-log-wake law (sDMLW-law). Velocity profiles of the two laws and the numerical solution of the ODE are compared with experimental data. This study shows that the dip correction is not efficient for a small Coles' parameter, accurate predictions require larger values. The sDMLW-law shows reasonable agreement and seems to be an interesting tool of intermediate accuracy. The fDMLW-law, with a parameter for dip-correction obtained from an estimation of dip positions, provides accurate velocity profiles.

In Lake Ziway watershed in Ethiopia, the contribution of river inflow to the water level has not been quantified due to scarce data for rainfall-runoff modeling. However, satellite rainfall estimates may serve as an alternative data source for model inputs. In this study, we evaluated the performance and the bias correction of Climate Hazards Group InfraRed Precipitation (CHIRP) satellite estimate for rainfall-runoff simulation at Meki and Katar catchments using the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. A non-linear power bias correction method was applied to correct CHIRP bias using rain gauge data as a reference. Results show that CHIRP has biases at various spatial and temporal scales over the study area. The CHIRP bias with percentage relative bias (PBIAS) ranging from −16 to 20% translated into streamflow simulation through the HBV model. However, bias-corrected CHIRP rainfall estimate effectively reduced the bias and resulted in improved streamflow simulations. Results indicated that the use of different rainfall inputs impacts both the calibrated parameters and its performance in simulating daily streamflow of the two catchments. The calibrated model parameter values obtained using gauge and bias-corrected CHIRP rainfall inputs were comparable for both catchments. We obtained a change of up to 63% on the parameters controlling the water balance when uncorrected CHIRP satellite rainfall served as model inputs. The results of this study indicate that the potential of bias-corrected CHIRP rainfall estimate for water balance studies.