Xiaolan Li^1,2,*, Jinyu Shen^1,2, Jinhuang Liang^1,2, Yanting Wang^1,2

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076521 - 18 June 2026

Abstract The intrinsic volatility and stochasticity of large-scale wind power generation pose significant challenges to grid stability. To address the limitations of conventional models in capturing strong non-stationarity, this study proposes a novel Multi-Stage Adaptive Forecasting Network (MSAF-Net). The framework features a hierarchical signal refinement strategy coupled with an intelligently optimized hybrid predictor. Initially, input redundancy is minimized via Pearson Correlation Coefficient (PCC) analysis to isolate significant meteorological variables. A two-phase decomposition-reconstruction mechanism is then implemented: the wind power series is first decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). To optimize the… More >

Xiaolan Li^1,2, Yanting Wang^1,2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076417 - 18 June 2026

Abstract To address the operational challenges of power systems with high renewable penetration, this research targets the non-stationarity and stochasticity of wind power. A novel hybrid framework for probabilistic forecasting and risk assessment is proposed. Initially, Empirical Mode Decomposition (EMD) adaptively decomposes the raw power signal into multi-scale Intrinsic Mode Functions (IMFs) and a residual trend, effectively segregating temporal features and reducing complexity. These components are then fused with historical data to form a comprehensive input. The core predictor is a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with a Temporal Attention (TA) mechanism. The BiGRU… More >

Amal H. Alharbi¹, Marwa M. Eid^2,*, Nima Khodadadi³, Ebrahim A. Mattar⁴, Sayed Elkenawy^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080044 - 27 May 2026

Abstract Seismic time series forecasting remains challenging due to the nonlinearity, non-stationarity, and noise of earthquake data, and because deep learning models are sensitive to preprocessing and hyperparameter settings. Although recent studies have improved neural architectures and optimization techniques, preprocessing is often treated as a fixed or manually designed stage, with limited integration into model optimization. To address this, this paper proposes an integrated, data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction, specifically forecasting earthquake magnitude from seismic catalog time-series data, with experiments conducted on Canadian seismic records. The method… More >

Said Benkachcha^1,*, Mustapha Adar¹, Mohamed Maniana², Youssef Najih¹, Mourad Kaddiri¹, Mutapha Mabrouki¹

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.075406 - 27 May 2026

Abstract Accurate forecasting of solar photovoltaic (PV) power generation is essential for enabling reliable integration of renewable energy into modern power systems. Variability in solar production, driven by meteorological fluctuations and inherent nonlinear dynamics, presents significant challenges for grid stability, operational planning, and energy management. This study investigates and compares the performance of classical statistical forecasting techniques and advanced deep learning approaches using real PV production data from a Moroccan solar plant. The analysis focuses on accuracy, robustness, computational efficiency, and suitability for short-term operational applications. Among statistical approaches, the Holt–Winters model demonstrated strong capability in… More > Graphic Abstract

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*

Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026

Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >

Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079991 - 08 May 2026

Abstract Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover More >

Jinsung Park^1,#, Jaehyuk Lee^1,2,#, Eunchan Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079734 - 08 May 2026

Abstract Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was employed to identify the dominant error-contributing features, which guided the construction of targeted month-specific interaction terms for retraining. Experimental results More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >

Qinglei Zhang, Zhenzhen Wang^*, Jianguo Duan, Jiyun Qin, Ying Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076562 - 09 April 2026

Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >

Hatim Madkhali^1,2,*, Abdullah Sheneamer², Linh Nguyen³, Gnana Bharathy¹, Ritu Chauhan⁴, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074444 - 09 April 2026

Abstract Consumer electronics, with 62 million tons of electronic waste (e-waste) generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030, pose critical challenges when it comes to national infrastructure and circular economy policies. This paper compares forecasting approaches using sparse panel data for 32 European countries (2005–2018, Eurostat/Waste Electrical and Electronic Equipment (WEEE) Directive), focusing on leakage-safe prospective validation to guarantee true predictive performance. We make one-step-ahead predictions with conservative features (primarily lagged values) to account for temporal autocorrelation but with reduced multicollinearity (Variance Inflation Factor (VIF) ≈ 1.0). Cross-paradigm comparisons… More >