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 >

Hongbo Sun¹, Xingyu Jiang^1,*, Wenyao Sun¹, Yi Zhao¹, Jifeng Cheng², Xiaoyi Qian¹, Guo Wang³

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.073012 - 27 March 2026

Abstract The accuracy of photovoltaic (PV) power prediction is significantly influenced by meteorological and environmental factors. To enhance ultra-short-term forecasting precision, this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory (TCN-BiLSTM) architecture incorporating a spatiotemporal attention mechanism. Firstly, during data preprocessing, the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients. Subsequently, a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term… More >

Yibin Guo¹, Lingxiao Ye^1,*, Xiang Wang¹, Di Wu¹, Zirong Wang¹, Hao Wang²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072163 - 27 March 2026

Abstract Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies. However, the strong nonlinearity and multi-scale temporal characteristics of crude oil prices pose significant challenges to traditional forecasting methods. To address these issues, this study proposes a hybrid CEEMDAN–HOA–Transformer–GRU model that integrates decomposition, complexity analysis, adaptive modeling, and intelligent optimization. Specifically, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original series into multi-scale components, after which entropy-based complexity analysis quantitatively evaluates each component. A differentiated modeling strategy… More > Graphic Abstract

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026

Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

Qi Wang, Kelvin Amos Nicodemas^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074305 - 12 March 2026

Abstract Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks, where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends. However, existing Transformer-based methods often process data at a single resolution or handle multiple scales independently, overlooking critical cross-scale interactions that influence prediction accuracy. To address this gap, we introduce the Hierarchical Attention Transformer (HAT), which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism. HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks, fuses them via temperature-controlled mechanisms, and optimizes More >

Cuihong Wen¹, Jingjing Wen¹, Qinyue Zhang¹, Yeting Wen², Fanyong Cheng^3,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071911 - 27 February 2026

Abstract Air conditioning is a major energy-consuming component in buildings, and accurate air conditioning load forecasting is of great significance for maximizing energy utilization efficiency. However, the deep learning models currently used in the field of air conditioning load forecasting often suffer from issues such as distribution bias in load data and insufficient expression ability of nonlinear features in the model, which affect the accuracy of load forecasting. To address this, this paper proposes a novel load forecasting model. Firstly, the model employs the Dish-TS (DS) module to standardize the input window data through self-learning standardized… More >

Yung-Yao Chen¹, Desri Kristina Silalahi^1,2, Atinkut Atinafu Yilma³, Chao-Lung Yang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075040 - 26 February 2026

Abstract Accurate photovoltaic (PV) power generation forecasting is essential for the efficient integration of renewable energy into power grids. However, the nonlinear and non-stationary characteristics of PV power signals, driven by fluctuating weather conditions, pose significant challenges for reliable prediction. This study proposes a DOEP (Decomposition–Optimization–Error Correction–Prediction) framework, a hybrid forecasting approach that integrates adaptive signal decomposition, machine learning, metaheuristic optimization, and error correction. The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features. Subsequently, the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy. The main… More >

Chih-Chieh Chang^*, Khairul Izyan Bin Anuar, Yu-Hwa Liu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073177 - 10 February 2026

Abstract Small datasets are often challenging due to their limited sample size. This research introduces a novel solution to these problems: average linkage virtual sample generation (ALVSG). ALVSG leverages the underlying data structure to create virtual samples, which can be used to augment the original dataset. The ALVSG process consists of two steps. First, an average-linkage clustering technique is applied to the dataset to create a dendrogram. The dendrogram represents the hierarchical structure of the dataset, with each merging operation regarded as a linkage. Next, the linkages are combined into an average-based dataset, which serves as… More >

Guebin Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073985 - 29 January 2026

Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >