Search Results (390)

China’s “dual-carbon” targets require more scientifically precise methods for carbon emission forecasting. Existing methods mainly rely on time series or regression models: the former captures temporal trends but lacks interpretability, while the latter provides explanatory power but struggles with nonlinear patterns. To overcome these limitations, this paper applies a multi-step LSTM with transfer learning to capture nonlinear temporal dynamics of carbon emissions, incorporates an SVR with added policy variables to improve accuracy, and finally employs a stacking model to integrate above advantages. Predictions are then aggregated via linear regression to leverage complementary strengths. The proposed model is trained on 1960–2004 data and tested on 2005–2019, 2023 and 2024 data. Results show that the optimized LSTM and SVR improve prediction accuracy, while the Stacking-based ensemble surpasses individual models in accuracy and robustness. Based on the integrated model, predictions for 2023–2050 indicate that if policies are strengthened in 2025, China’s carbon emissions will peak in 2024 and subsequently decline to about 8175 Mt CO₂ by 2050; if policies are not strengthened in 2025, emissions will peak in 2026 and subsequently decline to about 6983 Mt CO₂. Full article

Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R², RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power. Full article

The accurate forecasting of CO₂ emissions from power stations is critical for effective climate policy and the transition to sustainable energy systems. However, the complexity of power generation processes and the high dimensionality of operational data present significant challenges to traditional modeling approaches. This paper introduces a novel multi-stage framework that integrates advanced feature selection with explainable machine learning (XAI) to deliver high-accuracy forecasts of power station CO₂ emissions while maintaining full model transparency. The proposed methodology comprises a three-stage feature selection process—combining filter, wrapper, and embedded methods—to systematically identify the most influential emission drivers from a large set of potential variables. The selected features are then used to train a suite of machine learning models, including XGBoost, Random Forest, LSTM, and SVR. The best-performing model, XGBoost, achieved a Root Mean Square Error (RMSE) of 28.5, a Mean Absolute Error (MAE) of 19.8, and a coefficient of determination (R²) of 0.96 on a real-world dataset. To address the “black-box” nature of these models, we employ SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret the model’s predictions, providing granular insights into the key factors driving emissions. The results demonstrate that the proposed framework not only outperforms state-of-the-art forecasting models but also offers a clear, interpretable, and actionable tool for policymakers and plant operators to support CO₂ reduction strategies. The novelty of this work lies in its unique combination of a multi-stage feature selection pipeline and a comprehensive XAI-based analysis, providing a robust and transparent solution for a critical environmental challenge. Full article

Accurate forecasting of aggregated demand response (DR) potential is critical for load aggregators, yet remains challenging under severe data scarcity and domain shift conditions. This paper proposes a domain-adaptive transfer learning framework based on an ensemble of Random Vector Functional-Link (RVFL) neural networks for DR potential prediction without requiring any labeled target-domain data. By integrating domain adaptation layers and Maximum Mean Discrepancy (MMD) regularization, the proposed method explicitly reduces marginal feature distribution discrepancies between source and target domains, enabling effective knowledge transfer across heterogeneous operating scenarios. Compared with deep learning architectures, the RVFL-based framework offers favorable theoretical and practical properties for this application, including closed-form least-squares training, reduced risk of overfitting under limited data, and stable generalization under distribution shifts due to its direct-link structure and randomized hidden representations. These characteristics lead to significantly lower computational complexity and training cost than gradient-based deep models, while maintaining strong predictive capability. Case studies using real-world residential consumption data from the Pecan Street dataset demonstrate that the proposed approach consistently outperforms benchmark methods, including SVR, RF, and LSTM, across both intra-year and cross-year transfer scenarios. Reliable prediction accuracy is achieved even when only 10% of source-domain data are available, indicating strong data efficiency and scalability for practical aggregator deployment in day-ahead DR planning. Full article

The planned inclusion of the steel and electrolytic aluminum sectors into China’s Carbon Emission Allowance (CEA) market—initially limited to thermal power since 2021—will expand its coverage to approximately 70% of national carbon emissions, significantly influencing carbon pricing. This study employs a Variational Mode Decomposition–Support Vector Regression (VMD-SVR) model to forecast carbon price fluctuations under three post-expansion scenarios. The results indicate that, in addition to quota allocations, factors such as sectoral emission scales, the CSI 300 Power Index, and the Shanghai Energy Price Index substantially affect price trends. While market expansion induces a short-term price increase, it also stabilizes prices by reducing volatility. Furthermore, different quota allocation methods yield distinct outcomes: equal allocation facilitates a smoother market transition, whereas benchmarking provides stronger incentives for emissions reductions. Full article

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R² of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article

Accurate forecasting of crop maturity supports efficient harvest planning and accelerates selection decisions in breeding programs. In spring wheat, maturity is typically assessed through manual scoring late in the season, which limits its usefulness for timely harvest management and early selection decisions in breeding programs. This study evaluated uncrewed aerial vehicle (UAV)–based multispectral imagery for forecasting maturity in spring wheat grown at Lethbridge, Alberta (AB), Canada, during the 2024 and 2025 growing seasons. Thirty cultivars were monitored using seven-band UAV multispectral imagery during grain filling, enabling derivation of core vegetation and senescence-related indices from radiometrically calibrated orthomosaics. Strong correlations ($\left|r\right|>0.85$) were observed between vegetation indices and days remaining to maturity (DRTM), motivating baseline regression models and subsequent evaluation of eleven machine-learning and deep-learning approaches. Among these, support vector regression (SVR) and multi-layer perceptron (MLP) achieved the highest predictive accuracy ($R^2=0.95$–$0.96$; mean absolute error (MAE) $\approx 1.25$ days). Deep learning models achieved performance comparable to machine-learning approaches; however, incorporating spatial information through convolutional neural networks did not improve prediction accuracy. Feature-attribution analysis identified the red, red-edge (RE), and near-infrared (NIR) spectral bands as key predictors, enabling non-destructive, early, and scalable UAV-based maturity forecasting. Full article

The Baltic Dry Index (BDI) measures the cost of transporting dry bulk commodities such as coal, iron ore, and grain. As a key indicator of global trade, supply chain dynamics, and overall economic activity, accurate short-term forecasting of the BDI is crucial. This paper compares six univariate methods to obtain a more precise short-term BDI prediction model, providing valuable insights for decision-makers. The six forecasting techniques include Grey Forecast, ARIMA, Support Vector Regression, LSTM, GRU and EMD-SVR-GWO. Model performance is evaluated using three common metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Our findings reveal that the novel EMD-SVR-GWO model outperforms the other univariate methods, demonstrating superior accuracy in forecasting monthly BDI trends. This study contributes to improved BDI prediction, aiding managers in strategic planning and decision-making. Full article

The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R², MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R² values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R²) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article

In recent years, the green financial market has been exhibiting heightened volatility daily, largely due to policy changes and economic shifts. To explore the broader potential of predictive modeling in the context of short-term volatility time series, this study analyzes how fine-tuning hyperparameters in predictive models is essential for improving short-term forecasts of market volatility, particularly within the rapidly evolving domain of green financial markets. While traditional econometric models have long been employed to model market volatility, their application to green markets remains limited, especially when contrasted with the emerging potential of machine-learning and deep-learning approaches for capturing complex dynamics in this context. This study evaluates the performance of several data-driven forecasting models starting with machine-learning models: regression tree (RT) and support vector regression (SVR), and with deep-learning ones: long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU) applied to over a decade of daily estimated volatility data coming from three distinct green markets. Predictive accuracy is compared both with and without hyperparameter optimization methods. In addition, this study introduces the quantile loss metric to better capture the skewness and heavy tails inherent in these financial series, alongside two widely used evaluation metrics. This comparative analysis yields significant numerical and graphical insights, enhancing the understanding of short-term volatility predictability in green markets and advancing a relatively underexplored research domain. The study demonstrates that deep-learning predictors outperform machine-learning ones, and that including a hyperparameter tuning algorithm shows consistent improvements across all deep-learning models and for all volatility time series. Full article

In the fight against global climate change, the transportation sector is of critical importance because it is one of the major causes of total greenhouse gas emissions worldwide. Although urban rail transit systems offer a lower carbon footprint compared to road transportation, accurately forecasting the energy consumption of these systems is vital for sustainable urban planning, energy supply management, and the development of carbon balancing strategies. In this study, forecasting models are designed using five different machine learning (ML) algorithms, and their performances in predicting the energy consumption and carbon footprint of urban rail transit systems are comprehensively compared. For five distribution-center substations, 10 years of monthly energy consumption data and the total carbon footprint data of these substations are used. Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Neural Network (NAR-NN) models are developed to forecast these data. Model hyperparameters are optimized using a 20-iteration Random Search algorithm, and the stochastic models are run 10 times with the optimized parameters. Results reveal that the SVR model consistently exhibits the highest forecasting performance across all datasets. For carbon footprint forecasting, the SVR model yields the best results, with an $R^2$ of 0.942 and a MAPE of 3.51%. The ensemble method XGBoost also demonstrates the second-best performance ($R^2=0.648$). Accordingly, while deterministic traditional ML models exhibit superior performance, the neural network-based stochastic models, such as LSTM, ANFIS, and NAR-NN, show insufficient generalization capability under limited data conditions. These findings indicate that, in small- and medium-scale time-series forecasting problems, traditional machine learning methods are more effective than neural network-based methods that require large datasets. Full article

This work implements deep learning models to capture non-linear and complex data behavior in aluminum price data. Deep learning models include the long short-term memory (LSTM) and deep feedforward neural networks (FFNN). The support vector regression (SVR) is employed as a base model for comparison. Each predictive model is tuned by using two different optimization methods: Bayesian optimization (BO) and random search (RS). All models are tested on daily, weekly, and monthly data. Three performance metrics are used to evaluate each forecasting model: the root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The experimental results show that the LSTM-BO is the best-performing model across the time horizons (daily, weekly, and monthly). By consistently achieving the lowest RMSE, MAE, and highest R², the LSTM-BO outperformed all the other models, including SVR-BO, FFNN-BO, LSTM-RS, SVR-RS, and FFNN-RS. In addition, predictive models utilizing BO regularly outperformed those using RS. In summary, LSTM-BO is highly beneficial for aluminum spot price forecasting. Full article

Short-term load forecasting (STLF) underpins the efficient and secure operation of power systems. This study develops and evaluates a hybrid architecture that couples deep neural networks (DNNs) with support vector regression (SVR) for national-scale day-ahead STLF using Croatian load data from 2006 to 2022. The approach employs an hour-specific framework of 24 hybrid models: each DNN learns a compact nonlinear representation for a given hour, while an SVR trained on the penultimate layer activations performs the final regression. Gradient-boosting-based feature selection yields compact, informative inputs shared across all model variants. To overcome limitations of historical local measurements, the framework integrates global numerical weather prediction data from the TIGGE archive with load and local meteorological observations in an operationally realistic setup. In the held-out test year 2022, the proposed hybrid consistently reduced forecasting error relative to standalone DNN-, LSTM- and Transformer-based baselines, while preserving a reproducible pipeline. Beyond using SVR as an alternative output layer, the contributions are as follows: addressing a 17-year STLF task, proposing an hour-specific hybrid DNN–SVR framework, providing a systematic comparison with deep learning baselines under a unified protocol, and integrating global weather forecasts into a practical day-ahead STLF solution for a real power system. Full article

Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article

Road safety and maintenance strategy optimization depend on accurate pavement surface friction prediction. In order to predict the Friction Number for Continuously Reinforced Concrete Pavement (CRCP) sections using data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a hybrid machine learning framework that combines Gradient Boosting Machines (GBMs) with Genetic Algorithm (GA) optimization. Twenty input variables from the structural, climatic, traffic, and performance categories were used in the analysis of 395 data points from 33 CRCP sections. With a mean Root Mean Squared Error (RMSE) of 3.644 and a mean R-squared (R²) value of 0.830, the GA-optimized GBM model outperformed baseline models such as non-optimized GBM, Linear Regression, Random Forest, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The most significant predictors, according to sensitivity analysis, were AADT, Total Thickness, Freeze Index, and Pavement Age. The marginal effects of these variables on the expected friction levels were illustrated using partial dependence plots (PDPs). The results show that the suggested GA-GBM model offers a strong and comprehensible instrument for forecasting pavement friction, with substantial potential for improving safety evaluations and maintenance scheduling in networks of rigid pavement. Full article