Yali Cao¹, Weijian Hu^1,2, Lingfang Li^1,*, Minchao Li¹, Meng Xu², Ke Han²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069373 - 09 December 2025

Abstract Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for… More >

Zitong Zhao¹, Zixuan Zhang², Zhenxing Niu^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069752 - 10 November 2025

Abstract Reliable traffic flow prediction is crucial for mitigating urban congestion. This paper proposes Attention-based spatiotemporal Interactive Dynamic Graph Convolutional Network (AIDGCN), a novel architecture integrating Interactive Dynamic Graph Convolution Network (IDGCN) with Temporal Multi-Head Trend-Aware Attention. Its core innovation lies in IDGCN, which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs, and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data. For 15- and 60-min forecasting on METR-LA, AIDGCN achieves MAEs of 0.75% and 0.39%, and RMSEs More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Mintao Xu^1,#, Zichao Jin^1,#, Yangyang Tian¹, Jingcheng Zhang^1,*, Huiqin Ma¹, Yujin Jing¹, Jiangxing Wu², Jing Zhai²

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4059-4078, 2025, DOI:10.32604/phyton.2025.074422 - 29 December 2025

Abstract The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security. Data-driven forecasting models have emerged as an effective approach to support early warning and management, yet the lack of user-friendly tools for model development remains a major bottleneck. This study presents the Multi-Scenario Crop Disease Forecasting Modeling System (MSDFS), an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training, evaluation, and deployment-across four representative scenarios: static point-based, static grid-based, dynamic point-based, and dynamic grid-based. Unlike conventional frameworks, MSDFS emphasizes modeling flexibility, allowing… More >

Chen Pan, ChangGyoon Lim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025

Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

Tariq Ali^1,2,*, Muhammad Ayaz^1,2, Mohammad Hijji², Imran Baig³, MI Mohamed Ershath⁴, Saleh Albelwi²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3839-3858, 2025, DOI:10.32604/cmes.2025.073169 - 23 December 2025

Abstract The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind, solar, and other renewables. Accurate forecasting is crucial for ensuring grid stability, optimizing market operations, and minimizing economic risks. This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models, fractal-based feature engineering, and deep learning architectures to improve renewable energy forecasting accuracy. Fractional autoregressive integrated moving average (FARIMA) and fractional exponential smoothing (FETS) models are explored for capturing long-memory dependencies in energy time-series data. Additionally, multifractal detrended fluctuation analysis (MFDFA) More >

Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3783-3811, 2025, DOI:10.32604/cmes.2025.071964 - 23 December 2025

Abstract The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address… More >

Carlos Torres-Aguilar^1,*, Pedro Moreno^2,*, Diego Rossit³, Sergio Nesmachnow⁴, Karla M. Aguilar-Castro¹, Edgar V. Macias-Melo¹, Luis Hernández-Callejo⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996 - 23 December 2025

Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More > Graphic Abstract

Zheng Yin, Zhao Zhang^*

Journal on Artificial Intelligence, Vol.7, pp. 633-645, 2025, DOI:10.32604/jai.2025.074450 - 24 December 2025

Abstract Power load forecasting load forecasting is a core task in power system scheduling, operation, and planning. To enhance forecasting performance, this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks, Gated Recurrent Units, and a self-attention mechanism. Based on standardized data cleaning and normalization, the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately. The self-attention mechanism is then applied to assign weights to key time steps, after which the two feature streams are flattened and concatenated. Finally, a fully connected layer is used to generate More >

Chirag Devendrakumar Parikh^*

Journal on Artificial Intelligence, Vol.7, pp. 571-584, 2025, DOI:10.32604/jai.2025.073765 - 01 December 2025

Abstract With the increasing complexity and scale of hyperscale data centers, the requirement for intelligent, real-time power delivery has never been more critical to ensure uptime, energy efficiency, and sustainability. Those techniques are typically static, reactive (since CPU and workload scaling is applied to performance events that occur after a request has been submitted, and is thus can be classified as a reactive response.), and require manual operation, and cannot cope with the dynamic nature of the workloads, the distributed architectures as well as the non-uniform energy sources in today’s data centers. In this paper, we… More >