Search Results (511)

Wind energy is a critical renewable resource in the global effort toward sustainable development and climate change mitigation. This paper introduces a hybrid forecasting framework that integrates multistep gated recurrent unit (GRU) modeling with Weibull distribution analysis to assess wind energy potential and predict long-term wind speed dynamics. The approach combines deterministic and probabilistic components, improving robustness against seasonal variability and uncertainties. To demonstrate its effectiveness, the framework was applied to hourly wind data collected from multiple stations across diverse geographical regions in Turkey. Weibull parameters, wind power density, capacity factor, and annual energy production were estimated, while five machine learning models were compared for forecasting accuracy. The GRU model outperformed alternative methods, and the hybrid GRU–Weibull approach produced highly consistent forecasts aligned with historical patterns. Results highlight that the proposed framework offers a reliable and transferable methodology for evaluating wind energy resources, with applicability beyond the case study region. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps in this field? In accordance with the six-stage protocol that is aligned with PRISMA-ScR standards, 92 studies were selected for in-depth evaluation. The results of the study indicate the presence of three predominant patterns: (1) exponential growth in the applications of machine learning and artificial intelligence; (2) geographic concentration in Chinese, North American, and European watersheds; and (3) persistent operational barriers, particularly in data-scarce tropical regions with limited flood and streamflow forecasting validation. Hybrid statistical-AI modeling frameworks have been shown to enhance forecast accuracy and PU quantification; however, these frameworks are encumbered by constraints in computational demands and interpretability, with inadequate validation for extreme events highlighting critical gaps. The review emphasizes standardized metrics, broader validation, and adaptive postprocessing to enhance applicability, advocating robust frameworks integrating meteorological input to hydrological output postprocessing for minimizing uncertainty chains and supporting water management. This study provides an updated field mapping, identifies knowledge gaps, and prioritizes research for the operational integration of advanced PU quantification. Full article

Surface drifter trajectory prediction is essential for applications in environmental management, maritime safety, and climate studies. This survey paper reviews research from the past two decades, and systematically classifies the evolution of methodologies into six successive generations, including numerical models, data assimilation, statistical and probabilistic approaches, machine learning, deep learning, and hybrid or AI-based data assimilation (1st–5.5th Generation). To our knowledge, this is the first systematic generational classification of trajectory prediction methods. Each generation revealed distinct strengths and limitations. Numerical models ensured physical consistency but suffered from accumulated forecast errors in observation-sparse regions. Data assimilation improved short-term accuracy as observing networks expanded, while machine learning and deep learning enhanced short-range forecasts but faced challenges such as error accumulation and insufficient physical constraints in longer horizons. More recently, hybrid frameworks and AI-based data assimilation have emerged, combining physical models with deep learning and traditional statistical techniques, thereby opening new possibilities for accuracy improvements. By comparing methodologies across generations, this survey provides a roadmap that helps researchers and practitioners select appropriate approaches depending on observation density, forecast lead time, and application objectives. Finally, this paper highlights that future systems should shift focus from deterministic tracks toward credible uncertainty estimates, region-aware designs, and physically consistent prediction frameworks. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

Achieving decarbonization targets in the aviation sector requires transformative approaches to sustainable aviation fuel (SAF) production. In this pursuit, feedstock innovation has emerged as a critical challenge. This research uses the U.S. SAF Grand Challenge as a case study, focusing on its feedstock innovation workstream, to investigate how Industry 4.0 technologies can fulfill that workstream’s objectives. An integrative literature review, drawing on academic, industry, and policy sources, is used to evaluate the Technology Readiness Levels (TRLs) of Industry 4.0 technology applications across the SAF biomass supply chain. The analysis identifies several key technologies as essential for improving yield prediction, optimizing resource allocation, and linking stochastic models to techno-economic analyses (TEAs): IoT-enabled sensor networks, probabilistic/precision forecasting, and automated quality monitoring. Results reveal an uneven maturity landscape, with some applications demonstrating near-commercial readiness, while others remain in early research or pilot stages, particularly in areas such as logistics, interoperability, and forecasting. The study contributes a structured TRL-based assessment that not only maps maturity but also highlights critical gaps and corresponding policy implications, including data governance, standardization frameworks, and cross-sector collaboration. By aligning digital innovation pathways with SAF deployment priorities, the findings offer both theoretical insights and practical guidance for advancing sustainable aviation fuel adoption and accelerating progress toward net-zero aviation. Full article

Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and NYMEX datasets, the models are trained on long historical periods and tested under multi-step horizons. The results show that all hybrid models significantly outperform the traditional moving average benchmark, achieving R² values above 95% for one-step-ahead forecasts and maintaining an accuracy of over 87% at longer horizons. CNN-BiLSTM-Attention performs best in short-term prediction due to its ability to capture bidirectional dependencies, while TCN-based models demonstrate greater robustness over extended horizons due to their effective modeling of long-range temporal structures. These findings confirm the advantages of deep learning hybrids in energy forecasting and emphasize the importance of horizon-sensitive evaluation. This study contributes methodological innovation and provides practical insights for market participants, with future directions including the integration of macroeconomic and climatic factors, exploration of advanced architectures such as Transformers, and probabilistic forecasting for uncertainty quantification. Full article

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories. Full article

The uneven expansion of renewable energy generation in different regions highlights the necessity of accurately assessing the available transfer capability (ATC) in power systems. This paper proposes a distributed probabilistic inter-regional ATC assessment framework. First, a spatiotemporally correlated wind power output model is established using wind speed forecast data and correlation matrices, enhancing the accuracy of wind power forecasting. Second, a two-stage probabilistic ATC assessment optimization model is proposed. The first stage minimizes both generation cost and risk-related costs by incorporating conditional value-at-risk (CVaR), while the second stage maximizes the power transaction amount. Thirdly, a privacy-preserving two-level iterative alternating direction method of multipliers (I-ADMM) algorithm is designed to solve this mixed-integer optimization problem, requiring only the exchange of boundary voltage phase angles between regions. Case studies are performed on the 12-bus, the IEEE 39-bus and the IEEE 118-bus systems to validate the proposed framework. Hence, the proposed framework enables more reliable and risk-aware intraday ATC evaluation for inter-regional power transactions. Moreover, the impacts of risk parameters and wind farm output correlations on ATC and generation cost are further investigated. Full article

This study aims to highlight key figures and organizations in solar energy forecasting research, including the most prominent authors, journals, and countries. It also clarifies commonly used abbreviations in the field, with a focus on forecasting methods and techniques, the form and type of solar energy forecasting outputs, and the associated error metrics. Building on previous research that analyzed data up to 2017, the study updates findings to include information through 2023, incorporating metadata from 500 articles to identify key figures and organizations, along with 276 full-text articles analyzed for abbreviations. The application of text mining offers a concise yet comprehensive overview of the latest trends and insights in solar energy forecasting. The key findings of this study are threefold: First, China, followed by the United States of America and India, is the leading country in solar energy forecasting research, with shifts observed compared to the pre-2017 period. Second, numerous new abbreviations related to machine learning, particularly deep learning, have emerged in solar energy forecasting since before 2017, with Long Short-Term Memory, Convolutional Neural Networks, and Recurrent Neural Networks the most prominent. Finally, deterministic error metrics are mentioned nearly 11 times more frequently than probabilistic ones. Furthermore, perspectives on the practices and approaches of solar energy forecasting companies are also examined. Full article

The Gaussian Mixture Model (GMM) is commonly used to formulate the probabilistic model for quantifying uncertainties in renewable generation. However, traditional static probabilistic models may not efficiently adapt and learn from newly forecasted and measured data. In this paper, we propose a recursively updated probabilistic model that leverages a recursive estimation method to update the parameters of the GMM based on continuously arriving data of renewable generation. This recursive modeling approach effectively incorporates new observations while discarding outdated samples, enabling the tracking of time-varying uncertainties in renewable generation in an incremental manner. Furthermore, we introduce an extra calibration stage to enhance the long-term accuracy of the probabilistic model after a large number of incremental updates. The main contribution is to address the potential degradation of performance caused by suboptimal incremental updates accumulated over time. Numerical tests demonstrate that the proposed model achieves 5–10% higher log likelihood in characterizing renewable generation uncertainties compared to purely recursive models, while reducing computational time by three to four orders of magnitude (1000 to 10,000 times) relative to conventional EM. These results highlight the proposed model’s suitability for real-time probabilistic modeling of renewable generation, with potential applications in system operation. Full article

Accurate load forecasting for community-level heating, ventilation, and air conditioning (HVAC) plays an important role in determining an efficient strategy for demand response (DR) and the operation of the power grid. However, community-level HVAC includes various building-level HVACs, whose usage patterns and standard parameters vary, causing the challenge of load forecasting. To this end, a novel deep learning model, multiscale and cross-variable transformer (MSCVFormer), is proposed to achieve accurate community-level HVAC probabilistic load forecasting by capturing the various influences of multivariables on the load pattern, providing effective information for the grid operators to develop DR and operation strategies. This approach is combined with the multiscale attention (MSA) and cross-variable attention (CVA) mechanism, capturing the complex temporal patterns of the aggregated load. Specifically, by embedding the time series decomposition into the self-attention mechanism, MSA enables the model to capture the critical features of time series while considering the correlation between multiscale time series. Then, CVA calculates the correlations between the exogenous variable and aggregated load, explicitly utilizing the exogenous variables to enhance the model’s understanding of the temporal pattern. This differs from the usual methods, which do not fully consider the relationship between the exogenous variable and aggregated load. To test the effectiveness of the proposed method, two datasets from Germany and China are used to conduct the experiment. Compared to the benchmarks, the proposed method achieves outperforming probabilistic load forecasting results, where the prediction interval coverage probability (PICP) deviation with the nominal coverage and prediction interval normalized averaged width (PINAW) are reduced by 46.7% and 5.25%, respectively. Full article

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities. Full article

Accurately predicting annual renewable power generation is critical for advancing energy structure transformation, ensuring energy security, and fostering sustainable development. In this study, a probabilistic non-homogeneous grey model (PNGM) is proposed to address this forecasting challenge. Firstly, the proposed model is constructed by integrating a Probabilistic Accumulation Generation Operator with the classical non-homogeneous grey model. Secondly, the Whale Optimization Algorithm is utilized to tune the parameters of the operator, thereby enhancing the extraction of valid information required for modeling. Furthermore, the superiority of the new model in information extraction and predictive performance is validated using synthetic datasets. Finally, it is applied to forecast renewable power generation in the United States, Russia, and India. The result exhibits significantly superior performance compared to the comparative models. Additionally, this study provides projections of renewable power generation for the United States, Russia, and India from 2025 to 2030, and the uncertainty intervals of the predicted values are estimated using the Bootstrap method. These results can provide reliable decision support for energy sectors and policymakers. Full article

Background: Liquidity crises pose significant risks to financial stability, and missing data in predictive models increase the uncertainty in decision-making. This study aims to develop a robust Bayesian Model Averaging (BMA) framework using decision trees (DTs) to enhance liquidity crisis prediction under missing data conditions, offering reliable probabilistic estimates and insights into uncertainty. Methods: We propose a BMA framework over DTs, employing Reversible Jump Markov Chain Monte Carlo (RJ MCMC) sampling with a sweeping strategy to mitigate overfitting. Three preprocessing techniques for missing data were evaluated: Cont (treating variables as continuous with missing values labeled by a constant), ContCat (converting variables with missing values to categorical), and Ext (extending features with binary missing-value indicators). Results: The Ext method achieved 100% accuracy on a synthetic dataset and 92.2% on a real-world dataset of 20,000 companies (11% in crisis), outperforming baselines (AUC PRC 0.817 vs. 0.803, p < 0.05). The framework provided interpretable uncertainty estimates and identified key financial indicators driving crisis predictions. Conclusions: The BMA-DT framework with the Ext technique offers a scalable, interpretable solution for handling missing data, improving prediction accuracy and uncertainty estimation in liquidity crisis forecasting, with potential applications in finance, healthcare, and environmental modeling. Full article