Search Results (1,757)

Sub-Saharan Africa is rich in agricultural biomass, yet its biogas utilization is far below its potential. Most earlier studies failed to identify the nonlinear, multi-factor relationships that shape real national biogas yields and fully clarify this imbalance. This study constructs a 2007–2023 panel dataset for ten sub-Saharan African countries, merging agricultural output, socioeconomic, and infrastructure metrics. Gradient Boosting model and SHapley Additive exPlanations (SHAP) analysis are applied for empirical evaluation. SHAP analysis confirms that charcoal consumption yields the largest contribution to biogas production, with a mean absolute SHAP value of 1.018. The correlation between the two variables is negative under the threshold and becomes positive beyond this critical level. Urbanization has an inverted U-shaped correlation with biogas output, and the marginal contributions of predictors vary substantially across sampled countries. Instead, fragile supply chains, rural labor loss, and fierce competition in clean energy markets curb local biogas production. Forecasts show that regional biogas output will continue to fall until 2030. Targeted national policies matching each country’s core influencing factors are therefore urgently required. Full article

The output of photovoltaic power generation exhibits significant randomness, volatility, and intermittency, and its uncertainty will have an impact on the planning assessment, dispatch decision-making, and real-time operation of the power system. To systematically understand the development status of modeling methods for the uncertainty of photovoltaic power generation, this paper conducts a review around the generation of annual scenarios and multi-timescale power prediction of photovoltaic power, and analyzes the correlations between photovoltaic output, influencing factors, and system applications from the perspective of statistical relationships and artificial intelligence. For the annual scale, the focus is on the generation methods of meteorological-driven scenarios for long-term sequences, including probability statistical methods, deep generation methods, constraint relations and engineering application evaluation issues; for the day-ahead scale, the historical power, meteorological variables and numerical weather forecasts are used to explore feature extraction, probability prediction and robust modeling methods; for the intraday scale, the signal decomposition, deep learning, regional collaborative modeling and multi-source perception methods for short-term power fluctuations perception are summarized. On this basis, further analysis is conducted on subsequent research directions such as multi-timescale collaborative modeling, multi-source heterogeneous information fusion, controllable generative modeling, extreme scenario characterization, and engineering closed-loop verification. This paper can serve as a reference for scenario generation, power prediction, and power system operation analysis under the condition of a high proportion of photovoltaic power integration. Full article

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this paper proposes a Tensor Network Extended Kalman Filter (TNEKF) framework for short-term metro OD demand forecasting. First, metro OD demand is formulated as a nonlinear dynamic state-space prediction problem, where a multi-input multi-output Volterra model is adopted to characterize the nonlinear relationship between historical passenger demand and future OD flows. An Extended Kalman Filter (EKF) is then developed to recursively estimate the latent model parameters and continuously refine demand prediction using newly available observations. To improve computational efficiency for high-dimensional OD systems, both the latent state vector and covariance matrix are represented using low-rank tensor network structures, and all recursive filtering operations are implemented through tensor-network contractions without explicitly constructing large-scale matrices. Experiments on real-world smart-card data from the Hangzhou metro system demonstrate that the proposed method consistently outperforms ARIMA, conventional EKF, and several state-of-the-art spatiotemporal prediction models in terms of MAE, RMSE, and MAPE. Compared with the best-performing baseline of the whole-day scenario, the proposed method reduces MAE, RMSE, and MAPE by 30.2%, 9.8%, and 6.3%, respectively. Furthermore, the proposed framework exhibits strong robustness under disruption scenarios, demonstrating its effectiveness and scalability for large-scale metro OD demand forecasting. Full article

China’s power sector is accelerating its transition to spot-market clearing with increasing offshore wind penetration. This transition poses compounded operational and economic challenges, as the interaction between generation variability and price volatility affects both producer revenues and real-time system balancing costs. This study utilizes full-year hourly generation and spot price data from an offshore wind farm in eastern Guangdong, which represents the largest offshore wind industry cluster and a premier high-wind-resource area along China’s near-sea coasts. This empirical dataset provides significant value for characterizing real-world market behaviors under Guangdong’s dual-settlement framework. By employing a settlement-consistent Monte Carlo framework to quantify the joint effects of forecast errors, our results reveal that while downside risk is primarily driven by generation volume errors under normal conditions, the negative correlation between wind output and prices intensifies revenue volatility. Furthermore, under high-stress scenarios characterized by extreme market volatility and large deviations, price uncertainty emerges as the dominant driver of tail risk. Ultimately, these findings demonstrate that probabilistic forecasting for both prices and generation is essential not only for producer risk management but also for supporting dispatchable decision-making and reliable operation of power systems with high shares of renewable energy. Full article

The rapid growth of electric vehicle (EV) adoption has increased the importance of understanding charging behavior and improving the operational sustainability of charging infrastructure. Although existing studies have extensively investigated charging demand forecasting, charging load prediction, and charging behavior analysis, limited attention has been given to evaluating the sustainability of individual charging sessions. To address this gap, this study proposes a Sustainable Charging Session Index (SCSI) framework for assessing and classifying real-world EV charging behaviors based on operational charging characteristics. The proposed framework integrates the Entropy Weight Method (EWM), K-Means clustering, Principal Component Analysis (PCA), Random Forest feature importance analysis, and statistical validation techniques. A real-world dataset comprising 1929 EV charging sessions was analyzed, from which 1795 valid charging records were retained after preprocessing. Charging energy usage, average output power, and charging duration were selected as complementary indicators representing energy delivery effectiveness, charging efficiency, and temporal efficiency, respectively. The EWM assigned the highest weights to charging energy usage (0.5119) and average output power (0.4340), reflecting their greater discriminatory capability within the analyzed dataset. Clustering analysis identified three charging behavior archetypes, namely High-Sustainability Charging Sessions, Low-Sustainability Charging Sessions, and Efficient Charging Sessions. PCA demonstrated clear cluster separation, with the first two principal components explaining 97.9% of the total variance. Statistical analyses confirmed significant differences among the identified charging behavior groups (p < 0.001), while one-way ANOVA demonstrated strong internal consistency between the charging behavior clusters and SCSI scores (η² = 0.730). Furthermore, Random Forest analysis identified charging power as the most influential factor in differentiating charging behaviors. The proposed SCSI framework provides an objective and data-driven approach for charging session sustainability assessment, charging behavior characterization, and sustainable charging infrastructure management. Full article

This study proposes a hybrid deep learning model named LSTM-CBAM-Time2Vec-KAN based on symmetry awareness and optimized multi-module collaborative features, aiming to improve the accuracy and stability of stock price prediction. To address common shortcomings in traditional forecasting models such as insufficient feature extraction, difficulties in parameter optimization, and inadequate utilization of temporal characteristics, the research innovatively exploits the symmetry inherent in financial time series, particularly their temporal periodicity and cross-dimensional feature consistency, to construct an intelligent prediction framework that integrates multiple modules. First, wavelet transform is applied to perform multi-scale decomposition and signal reconstruction on the raw stock price sequence, effectively extracting high signal-to-noise ratio features. Second, the Northern Goshawk Optimization (NGO) algorithm is employed to jointly optimize key hyperparameters of the model, including the LSTM hidden layer dimension and CBAM compression ratio, thereby resolving the challenge of parameter coupling across modules. Third, the CBAM attention mechanism enhances the importance of temporal features extracted by LSTM through a dual mechanism of channel and spatial attention, enabling the model to focus on critical price movement points. Meanwhile, Time2Vec encoding transforms temporal information into embedding representations with periodic properties, effectively capturing cyclical patterns at daily, weekly, and monthly trading intervals. Finally, the Kolmogorov–Arnold network (KAN) fuses multimodal features and produces precise predictive outputs. Experimental results show that the proposed model significantly outperforms all baseline models in four evaluation metrics, namely mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), which verifies its superior prediction accuracy and robustness. Furthermore, analyses of stock price forecasting under different time spans and simulated trading performance under various trading strategies further demonstrate that this study provides a feasible and effective technical solution for financial time-series forecasting, with important theoretical research value and practical application value. Full article

Existing airport delay prediction models often focus on isolated flight-level or airport-level forecasts and do not explicitly represent the spatial propagation of disruptions across a national air transportation network. This study develops and evaluates a spatio-temporal graph learning framework for multi-horizon airport-level delay forecasting in the U.S. domestic network. Using 2,878,854 flight records from January 2019 to August 2023, flights are aggregated into hourly node-time tensors over 370 airports and 5334 directed connections. The proposed framework combines graph-based airport connectivity, temporal delay persistence, rare-disruption detection, and conformal uncertainty estimation over a 1–3 h forecasting horizon. The results show that LSTM attains the lowest aligned point-forecast errors, while ST-GCN provides competitive multi-horizon performance with substantially lower parameter complexity and explicit graph-aware outputs; the direct XGBoost baselines provide nonlinear tabular benchmarks but do not dominate the final multi-horizon evaluation. These findings indicate that graph-based models are most useful not as universal point-forecast winners but as interpretable network-aware tools for monitoring delay propagation, disruption risk, and operational uncertainty. Full article

Marine chlorophyll-a (Chl-a) concentration is a key proxy for phytoplankton biomass and an important indicator for eutrophication assessment, ecological monitoring, and harmful algal bloom early warning. Accurate forecasting of Chl-a concentration therefore has substantial scientific and practical value for marine environmental management. Nevertheless, existing deep models still have difficulty coupling relatively stable large-scale spatial structure with locally time-varying interactions, and many of them do not provide reliable uncertainty estimates, which limits their use in harmful algal bloom early warning and high-chlorophyll event assessment. To address these issues, this study proposes an uncertainty-quantified dynamic graph ordinary differential equation network, termed UQDGODE, for marine Chl-a concentration forecasting. The model combines a multivariate diffusion graph convolutional branch for stable spatial diffusion modeling with a dynamic graph ordinary differential equation branch for continuously evolving local interactions. A gating mechanism fuses the two branches, and a multivariate probabilistic prediction module outputs predictive means and covariance information for uncertainty quantification. Experiments on the Bohai Sea and South China Sea datasets show that the UQDGODE performs better in both point forecasting accuracy and probabilistic forecasting quality and produces more informative predictive distributions and more adaptive prediction intervals for marine ecological alerting. Full article

The exploitation of deep coalbed methane is of great significance for easing China’s energy supply pressure and realizing the “Dual Carbon” goals. However, local grid refinement simulation methods for coalbed methane cannot well capture the characteristics of deep coalbed methane reservoirs, including strong stress sensitivity and high brittleness. To tackle this issue, this paper develops a novel numerical simulation approach dedicated to deep coalbed methane development. Integrated with the fluid–solid coupling effect in rock mechanics, this approach considers the interporosity flow between matrix pores and cleat fractures as well as that between cleat fractures and hydraulic fractures, and establishes a multi-dimensional coupled simulation framework on the basis of the dual-porosity single-permeability model and embedded discrete fracture model. Simulation results show that compared with the local grid refinement model, the daily gas production curve simulated by the proposed method is more consistent with the actual field curve. The local grid refinement method fails to accurately characterize the specific morphology of hydraulic fractures. The average relative error of the local grid refinement model reaches 25.61%, while that of the model in this paper is only 7.54%, representing an accuracy improvement of 18.07%. Sensitivity analysis draws the following conclusions: reservoir gas content is the dominant geological factor governing deep coalbed methane output, and raising reservoir gas content can boost cumulative gas production by 45.77%; hydraulic fracture length mainly affects gas production performance in the middle and late production stages, while fracture conductivity dominates early-stage productivity. This method can fully characterize the coupled flow behaviors of three types of media (matrix pores, cleat fractures and hydraulic fractures), and offers solid technical support for productivity forecasting and development scheme optimization of deep coalbed methane reservoirs. Full article

Reliable identity preservation is essential in retail multi-object tracking because an identity switch may assign dwell time, shelf interactions, or demographic statistics to the wrong consumer trajectory. This challenge is amplified in crowded indoor scenes, where occlusions and appearance ambiguity weaken conventional association cues. This work presents a demographic-aware multi-object tracking framework for retail environments that exploits spatial, motion-based, appearance-based, and demographic cues within a modular tracking-by-detection pipeline. The proposed approach integrates IoU-based spatial association, LSTM-based motion forecasting, ReID appearance embeddings, and apparent demographic information derived from age-group and gender predictions into a common association cost. The core assumption is that demographic attributes should remain consistent across neighboring frames for the same tracked individual; therefore, demographic agreement can serve as a weak semantic cue during detection-to-tracklet association. Unlike conventional pipelines that treat multi-object tracking and demographic estimation as independent stages, the proposed method reuses the outputs of an existing Inception-based apparent demographic classifier to support identity preservation, without introducing a new demographic estimation model or requiring end-to-end retraining. The framework is evaluated on RGB retail tracking sequences from the Consumers dataset under both raw and privacy-aware anonymized settings. The proposed method achieves 84.8% MOTA and 87.8% IDF1 on raw data and 80.0% MOTA and 85.3% IDF1 under anonymization, improving the strongest evaluated baseline by 0.4 and 0.6 percentage points and by 1.0 and 1.3 percentage points, respectively, while preserving the same low ID-switch counts. Although incremental in absolute terms, these gains are consistent across both evaluation settings and are obtained over a strong baseline in challenging indoor retail sequences. The results indicate that even a lightweight demographic-consistency cue can provide measurable complementary information for improving consumer trajectory stability. Full article

With the advancement of global energy conservation and emission reduction, hydrogen-powered ships (HPSs) have received great attention. However, the current drainage volume of HPSs is generally small, and its operating load fluctuates greatly due to the influence of hydrological and meteorological conditions in the waterway. Therefore, a reasonable energy management strategy (EMS) is needed to allocate the output of hydrogen fuel cells (HFCs) and lithium batteries (LBs). This article proposes a two-stage EMS framework for HPSs based on dynamic empirical modeling and model predictive control (DEM-MPC) to achieve optimal operational energy efficiency of the HFC-LB energy supply system. Firstly, a DEM probabilistic load forecasting (PLF) model was established by combining the operational status data of an HPS system with the meteorological data of waterway water level. The DEM model was constructed using delay coordinate embedding (DCE) and nearest neighbor prediction (NNP) methods to obtain future multi-step PLF sequences as important reference information for the EMS. Subsequently, the PLF sequence is used as input for MPC to optimize the output allocation of the EMS. In the first stage of MPC, the efficiency of HFCs and LBs is optimized, and in the second stage, the comprehensive cost is optimized. Finally, the method was validated using actual data from an HPS in the Yangtze River waterway. The results indicate that the proposed DEM-MPC framework significantly improves the overall operational energy efficiency of HPSs. Full article

Accurate significant wave height prediction is essential for fuel-efficient ship operation and weather routing, as wave-induced resistance directly affects propulsion demand and fuel consumption. This study proposes a Residual U-Net-based deep-learning correction model to improve long-range SWH forecasts from WAVEWATCH III (WW3). WW3 global forecast fields were corrected using the proposed model, with CMEMS reanalysis data used as the ground-truth reference. The corrected outputs, denoted as WW3_UNET, were evaluated against 10 min resolution main engine fuel oil consumption (ME1_FOC) records and onboard wave observations from a commercial vessel traversing the South Atlantic in 2025. WW3_UNET showed markedly improved agreement with ship observations compared with the raw WW3 forecast across all lead times from 0 to 288 h. When a 24 h moving average was applied, WW3_UNET achieved a correlation of 0.720 with ME1_FOC at the 168–180 h lead time, closely approaching the 0.736 obtained from onboard wave measurements. These results indicate that AI-corrected forecasts can provide observation-consistent wave information up to 7–8 days in advance. The proposed approach can support fuel-aware weather routing and voyage planning, thereby contributing to improved maritime energy efficiency and decarbonization. Full article

Accurate ultra-short-term wind power forecasting is of great significance for grid integration scheduling and the secure operation of power systems. However, due to meteorological disturbances and turbine operating states, wind power series generally exhibit non-stationary, multi-scale fluctuations and strong nonlinearity. To improve forecasting accuracy, this paper proposes an ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN. First, a cascaded decomposition module is constructed using the wavelet transform (WT) and ICEEMDAN to attenuate the non-stationarity of the original power series and to extract multi-scale features. An iTransformer branch is then employed to model global dependencies among multiple variables, while an LSTM branch captures temporal dynamics in the historical power series. Subsequently, a cross-attention mechanism is introduced to achieve cross-branch feature fusion, and a KAN output layer is adopted to enhance the model’s representation of the wind speed–power nonlinear mapping. A particle swarm optimization (PSO) algorithm, combined with a cosine annealing strategy, is used to optimize key hyperparameters and improve training stability. Experimental results using SCADA data from a 150 MW wind farm in southern Hunan Province show that the proposed model achieves an MAE of 9.8327 MW, an RMSE of 13.1872 MW, an SMAPE of 18.8474%, and an $R^2$ of 0.7798. These values correspond to the fixed main comparison protocol used for baseline evaluation, while the ablation study reports multi-seed mean and standard deviation results to assess module-level robustness. Compared with LSTM and WT-ICEEMDAN-CNN-LSTM, the proposed model achieves clear improvements in forecasting accuracy and fitting capability. Additional cross-wind-farm validation on a second wind farm shows that WT-ICEEMDAN-iTransformer-LSTM-KAN-PSO (hereafter referred to as ILKP) maintains the best overall performance, achieving an MAE of 27.2193 MW, an RMSE of 36.1862 MW, an SMAPE of 27.8429%, and an $R^2$ of 0.5189, demonstrating transferability and robustness under different operating conditions. Full article

Accurate photovoltaic (PV) power forecasting is essential for reliable microgrid operation, efficient energy dispatch, and improved utilization of renewable energy resources. Existing forecasting methods often have limited capacity to represent the nonlinear relationships between meteorological conditions and PV power output. They also tend to underrepresent the temporal dynamics of PV generation and the physical principles governing photovoltaic energy conversion. To address these limitations, this study proposes a hybrid forecasting framework, CNN-LSTM-PINNs, that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). In the proposed framework, CNNs extract spatial dependencies among multivariate meteorological variables, LSTM networks capture temporal dependencies in PV generation, and PINNs incorporate soft physical constraints derived from photovoltaic energy conversion mechanisms. The proposed model is evaluated using publicly available datasets from three large-scale PV power stations in China, with observations recorded at 15-min intervals. The empirical results show that CNN-LSTM-PINNs outperform the conventional CNN-LSTM benchmark across the primary station-level datasets. Relative to the benchmark model, the proposed framework reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and improves the coefficient of determination (R²). These results indicate that embedding physical constraints into data-driven forecasting models can improve PV power prediction accuracy. The model also shows stronger robustness and generalization performance under heterogeneous operating conditions, although its effectiveness is contingent on relatively stable data distributions. Feature-importance analysis further indicates that global horizontal irradiance (GHI) and irradiance-derived variables are the most informative predictors of PV power output. Overall, this study provides a physics-informed hybrid modeling approach for high-resolution PV power forecasting in microgrid applications. Full article

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources. Full article