Search Results (173)

The reliable long-term prediction of photovoltaic (PV) system performance is essential for optimizing operation, maintenance, and energy management in smart cities. Digital Twin (DT) technology has emerged as a promising approach for real-time monitoring and predictive analytics by continuously integrating physical system measurements with virtual models. However, many existing DT-based studies rely on limited validation periods, lack comprehensive uncertainty quantification, and provide insufficient comparisons with conventional forecasting approaches. To address these limitations, this study proposes a data-driven Digital Twin framework for daily photovoltaic energy production prediction for three silicon photovoltaic technologies (amorphous silicon, polycrystalline silicon, and monocrystalline silicon) operating under semi-arid climatic conditions in Morocco. The framework integrates data quality assessment and statistical production trend analysis based on linear regression, bootstrap confidence intervals, the Mann–Kendall trend test, and Sen’s slope estimator. In addition, the proposed DT model was benchmarked against persistence, linear regression, Random Forest, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) models using a chronological training, validation, and independent testing framework. Model performance was evaluated over an independent test period of 602 consecutive days, representing approximately 1.65 years of continuous operation and more than one complete annual cycle. The proposed Digital Twin improved upon the standalone LightGBM model, achieving an RMSE of 1.3047 kWh/day, an MAE of 0.9255 kWh/day, a MAPE of 14.62%, and an R² of 0.7015, compared with an RMSE of 1.3349 kWh/day, an MAE of 0.9637 kWh/day, and an R² of 0.6875 for LightGBM. Over the 11-year monitoring period, the estimated long-term production trend rates were −0.566% yr⁻¹ for a-Si, −0.335% yr⁻¹ for pc-Si, and −0.260% yr⁻¹ for mc-Si. Bootstrap analysis yielded median long-term production trend rates of −0.573, −0.326, and −0.251% yr⁻¹, respectively, while Mann–Kendall tests indicated no statistically significant monotonic production trend for any technology (p = 0.1611, 0.2758, and 0.3502, respectively). The extended independent testing period, combined with statistical production trend analysis and comparative machine-learning evaluation, demonstrates the applicability of the proposed Digital Twin framework for photovoltaic performance monitoring and adaptive prediction under semi-arid climatic conditions. Full article

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts. Full article

The escalating impacts of climate change and anthropogenic pressures on vulnerable ecosystems demand digital tools that make advanced modeling more accessible to conservation practitioners. This study presents a TRL-4 prototype that integrates a configurable Digital Twin (DT) core with a generative AI conversational interface for conservation-oriented modeling in Doñana National Park, Spain, a UNESCO World Heritage site facing significant environmental challenges. The main contribution is not the training of specific ecological forecasting models, but the validation of an end-to-end workflow that allows users to configure, execute, inspect, and interpret a predictive system through natural language. The prototype supports the prediction of conservation-relevant ecological indicators, including Iberian lynx population dynamics and waterbird abundance, using heterogeneous environmental, climatic, hydrological, and socio-demographic datasets. The architecture connects a structured YAML configuration, heterogeneous environmental and biological datasets, automated machine learning training, database-backed traceability, dashboard visualization, and SHAP-based interpretability. Through representative executions, the prototype demonstrates that non-technical users can select target and explanatory variables, configure preprocessing options, launch model training, generate predictions, and review their outputs without directly editing configuration files or running code. Although the predictive metrics obtained in selected runs remain preliminary and should be interpreted as diagnostics rather than evidence of general forecasting skill, the results show that conversational DTs can substantially reduce technical barriers to ecological modeling. By combining generative AI, cloud infrastructure, reproducible machine learning workflows, and explainable AI, the proposed architecture provides a strong foundation for future conservation decision-support systems that augment expert judgment while preserving human oversight, transparency, and critical interpretation. Full article

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition. Full article

Against the backdrop of global climate change and energy structure transition, district energy systems have garnered significant attention for their efficiency and sustainability. Accurate load forecasting is crucial for enhancing the operational efficiency of district cooling systems. However, as typical dynamic time-varying systems, building complexes within district energy stations exhibit load characteristics influenced by multi-scale features. Furthermore, traditional load forecasting models employ single-scale analysis without specifically modeling these multi-scale characteristics, resulting in insufficient generalization capabilities of data-driven models under dynamic, time-varying scenarios. This paper proposes a multi-step forecasting model structure based on baseline-morphology decomposition to address the coupling of multi-scale characteristics. By decomposing load into baseline and morphological components, separate prediction models—a backpropagation neural network (BP) and a K-means clustering-decision tree (DT) classification prediction model—are constructed, overcoming the challenge of capturing multi-scale features in traditional methods. The results show that, during the four-month test period from August to November 2024, the proposed model achieves MAPE values ranging from 8.91% to 12.57% under the peak and transitional cooling conditions represented in the dataset. Compared to direct structure, recursive structure, and multi-input multi-output (MIMO) structure, it reduces errors by 1.49% to 19.62% while achieving remarkable advantages in training efficiency. Full article

Residential building retrofit requires reliable building energy simulation and efficient performance prediction. This research presents an adaptable workflow for establishing two residential base model templates, namely corner space (CS) and intermediate space (IS). The models were assessed through National Construction Code of Australia-based verification, comparison with a documented Melbourne residential reference model, and Morris index convergence analysis. These models were coupled with an AI-assisted three-stage prediction framework and evaluated across seven additional Australian climates, with Melbourne as the primary case, to examine cross-climate applicability. Stage 1 developed a two-variable analytical model to predict annual energy consumption. Stage 2 extended this to a three-variable relation and compared its energy predictions with support vector machine (SVM). Stage 3 examined multivariable cases using four machine learning (ML) models—artificial neural network (ANN), SVM, random forest (RF), and decision tree (DT)—to predict performance metrics and identify the best model. For the primary Melbourne case, the two-variable relation reproduced the simulated response with mean absolute percentage errors (MAPEs) of 0.27% and 0.62% for CS and IS, respectively. In Stage 2, the analytical model achieved a MAPE of 8.38%, while SVM reduced the error to 0.75%. Cross-climate results confirmed that the staged workflow remained robust across different climates. In Stage 3, SVM was robust with limited data, while the ANN and RF became more competitive with larger samples. DT was prone to overfitting. The proposed framework supports early-stage residential retrofit assessment by enabling retrofit hotspot screening and suitable surrogate model selection before detailed optimization. Full article

The spatial relationships between lightning-induced fire ignition and topography, vegetation, climate, and weather were analyzed in the region of East Macedonia and Thrace, northeastern Greece. The study was based on reported lightning-induced ignitions during the 2009 fire period. Lightning data for the same period was provided by the ZEUS lightning detection network operated by the National Observatory of Athens, while fire statistics were obtained from the official records of the Greek Fire Service. A total of 198 lightning strike events (66 fire ignitions and 132 non-fire events) were used for model development. Statistical models based on Logistic Regression (LR) and random forests (RF) were developed to estimate the probability of lightning-induced fire using topography, climate, weather, and vegetation indices as predictor variables. According to the analysis results, the probability of an area being affected by lightning-induced fire is primarily determined by the Normalized Difference Vegetation Index (NDVI) and the accumulated precipitation in 24 h equal to or less than 2.5 mm expressed by Dry Thunderstorm (DT) day occurrence in this dataset. The logistic regression model achieved an area under the ROC curve of 0.94 and an overall classification accuracy of 91.9%, while the random forest model produced an Out-Of-Bag (OOB) error rate of 3.0%. Although the models have not been subjected to independent validation and include a single year’s data, the results demonstrate high internal classification performance and provide valuable insights into the primary drivers of fire ignition following lightning strikes in the study region. The outcomes of the present study will be useful in assessing spatially explicit fire risk, the planning and coordination of efforts to identify high-fire-risk areas, and designing long-term fire management and climate change adaptation strategies. Full article

Reference evapotranspiration (ET₀) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET₀ in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET₀, R² = 0.802), followed by Cubist (RMSE = 7.3%, R² = 0.787) and DT (RMSE = 7.5%, R² = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET₀ maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET₀ target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology. Full article

This systematic review builds upon 51 published empirical studies out of 354 studies that were published between 2020 and 2025 to assess the effectiveness of building-scale digital twins (DTs) in providing thermal comfort and energy efficiency, and improving the indoor environment and system reliability. The results show that there is a rapidly developing field focused on five thematic clusters: system architecture, artificial intelligence and machine learning (AI/ML)-driven control, human-centric engagement, predictive maintenance, and blockchain-enabled cybersecurity. Existing DT frameworks not only achieve real-time building information modeling (BIM)–Internet of Things (IoT) integration with prediction errors under 10%, but reinforcement learning controllers are also able to achieve 25–40% heating, ventilation, and air conditioning (HVAC) energy savings, and human-centric interfaces increase thermal satisfaction from 0.64 up to 1.2 Likert points. Predictive maintenance models have diagnostic accuracies of 91–97%, and new blockchain applications enhance data integrity, but largely at the prototype level. The cross-cluster convergence signifies the transition towards adaptive, socio-technical systems with an equilibrium of efficiency, comfort, reliability, and trust. The major weaknesses identified in this paper were a lack of longitudinal validation, climatic bias and ethical governance. A framework of a modular six-layer architecture is proposed after the review of 51 studies, which facilitates scalable, interoperable, and ethically robust DT deployments. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article