Hui Zhang¹, Mangang Xie^1,*, Baozhen An², Jing Wei¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026

Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >

Perihan Hazel Kaya^1,*, Coşkun Kuş², Mustafa Çoklu³, Mustafa Göktuğ Kaya⁴

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084804 - 30 August 2026

Abstract This study examines the relationship between energy consumption, renewable energy supply, and economic growth in BRICS countries over the period 2000–2023 within the framework of panel data analysis. In the analysis, economic growth is represented by gross domestic product, while total energy consumption and renewable energy supply are used as the main energy-related variables. The study contributes to the literature by evaluating the energy-growth nexus together with both total energy use and renewable energy dynamics in BRICS economies. Since BRICS countries may be affected by common global shocks, cross-sectional dependence is considered in the empirical… More >

Mohammed Almajed¹, Mahbub Hassan², Turjoy Das Turjo³, Md Ashequl Islam^4,*, Md Ehtesamul Haque¹, M. M. Hafizur Rahman⁵

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084024 - 30 August 2026

Abstract Accurate per-kilometer energy consumption prediction is essential for effective electric bus (EB) fleet management in urban transit networks. Existing studies report mean absolute percentage errors of 5%–8% and rarely cross-validate explainability attributions or quantify prediction uncertainty. This study presents a physics-informed, interpretable machine learning framework for EB energy consumption prediction under Bangkok-like tropical urban conditions. Due to the institutional inaccessibility of operational telemetry, a parametric dataset of 4000 trip-level scenarios was constructed across 29 input features anchored to published primary sources. Four algorithms were benchmarked under Bayesian hyperparameter optimization: Extreme Gradient Boosting (XGBoost), Light Gradient… More >

Haotian Yang¹, Lin Lu^1,2,*

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.083071 - 30 August 2026

Abstract Building-integrated photovoltaics (BIPV) has attracted growing attention due to its promising applications in modern buildings. By serving as components of the building envelope, BIPV avoid competing with land use and facilitates net-zero energy buildings in the face of the current energy crisis. In addition to power generation, BIPV alters the energy-related properties of buildings. Appropriate daylighting and effective management of heat gains can reduce lighting, cooling, and heating loads, helping to balance energy production and consumption. This review examines the optical, thermal, and electrical behaviors of BIPV systems and their impacts on building energy efficiency. More >

Amjad Rehman¹, Tariq Mahmood^1,2, Faten S. Alamri^3,*, Muhammad I. Khan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.076683 - 27 May 2026

Abstract The rapid deployment of Wireless Sensor Networks (WSNs) faces critical challenges due to sensor nodes’ limited energy and communication capabilities, which restrict network lifetime and data transmission efficiency. Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution, whereas intelligent optimization approaches are hindered by high computational costs and slow convergence. This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints, aiming to improve stability, energy efficiency, and throughput. This research proposed three quantum optimization-based solutions to address complex issues. First,… More >

Maryem Lachgar^1,*, Mansour Lmkaiti¹, Ibtissam Larhlimi¹, Imad Aattouri², Hicham Ouchitachen¹, Hicham Mouncif¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075234 - 08 May 2026

Abstract This study introduces a hybrid routing protocol, Low Energy Adaptive Clustering Hierarchy—Ant Colony Optimization—Genetic Algorithm (LEACH-ACO-GA), for wireless sensor networks. It combines regional ant colony optimization for cluster head selection with inter-cluster routing based on a genetic algorithm. The proposed method reduces energy consumption from 6.9 J (LEACH Classic) to 5.6 J (LEACH-ACO-GA) and decreases latency from 460 to 390 ms, while maintaining a packet delivery ratio of 0.97. These values are averaged over 70 rounds based on 30 independent simulation runs conducted on networks with 50 and 200 nodes. The hybrid method extends network More >

Junwei Zhang¹, Pei Liu¹, Huihang Li¹, Guokang Huang¹, Bozheng Yuan¹, Wenjing Wei¹, Xiaoshun Zhang^2,*

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.072462 - 27 April 2026

Abstract Addressing global climate challenges necessitates urgent low carbon transitions in high energy consuming enterprises (HECEs). This study proposes a comprehensive framework to assess their carbon reduction potential (CRP) by integrating electricity usage behavior analysis and dynamic carbon emission factor (DCEF) prediction. HECEs are classified into “electricity reduction” and “electricity transfer” categories based on load characteristics, enabling tailored optimization strategies. The framework employs machine learning to predict DCEFs, capturing real time variations in grid carbon intensity. A low carbon optimization model is then formulated to minimize emissions while adhering to production requirements and grid constraints, solved… More > Graphic Abstract

Thatsamaphon Boonchuntuk¹, Thanyapisit Buaprakhong¹, Varintorn Sithisint¹, Awirut Phusaensaart¹, Sinthon Wilke¹, Thittaporn Ganokratanaa^1,*, Mahasak Ketcham²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.069005 - 27 April 2026

Abstract Urban traffic congestion directly contributes to excessive energy consumption and urban air pollution, requiring adaptive traffic signal control strategies that incorporate sustainability objectives alongside mobility performance. This study proposes a Large Language Model (LLM) driven traffic signal optimization framework that transforms detailed intersection-level traffic states into structured natural-language prompts, enabling the LLM to reason over congestion patterns, queue asymmetry, phase history, and estimated energy emission impacts. Unlike reinforcement learning (RL) based controllers, the LLM requires no task-specific training and operates in a zero-shot manner through carefully designed structured prompts that encode traffic states, phase history,… More >

Aditya Mairal^*, Todd Rossi, Michael Muller

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.075576 - 27 March 2026

Abstract An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting. EPA categorizes facility carbon footprints in three scopes. Scope-2 emissions include electricity, heat or steam purchased from a utility provider. This paper evaluates the existing calculation methods for scope-2 CO₂ emissions for purchased electricity. The electricity grid in US is complex and is divided spatially into states, eGRID regions, balancing authorities (BAs), and utilities. Up to hourly temporal granularity can be obtained from available datasets. A matrix is developed that categorizes different datasets based on the complexity to calculate the carbon… More >

Yang Li¹, Xianfu Gong¹, Sifan Chen¹, Yi Lei^2,*, Donghui Zhang², Yue Xing²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072189 - 27 March 2026

Abstract This paper develops an innovative computational model for assessing the Carbon Emission Factor (CEF) of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios combining conventional and renewable sources. A key contribution lies in evaluating how deep regulation of thermal power plants influence the carbon intensity of coal-fired generation and coal-fired generation together with high penetration renewables. Furthermore, the study quantitatively analyzes the role of renewable energy consumption and the prospective application of Carbon Capture and Storage (CCS) in reducing system-wide CEF. Based on this framework, the paper proposes phased carbon emission… More > Graphic Abstract