Hao Yue¹, Di Gao², Jin Gao¹, Chengmei Wei¹, Jiali Duan³, Shaocheng Mei^3,*

Energy Engineering, Vol.122, No.12, pp. 5091-5109, 2025, DOI:10.32604/ee.2025.069140 - 27 November 2025

Abstract Against the backdrop of regional coordinated development and China’s “dual carbon” strategic objectives, the Beijing-Tianjin-Hebei (BTH) region faces an urgent need to transition from its traditional economic growth model, which is heavily reliant on resource consumption. This study investigates the decoupling dynamics among economic growth, energy consumption, and carbon emissions in the BTH region, along with the underlying driving forces, aiming to provide valuable insights for achieving the “dual carbon” targets and fostering high-quality regional development. First, the Tapio decoupling model is employed to analyze the decoupling relationships between economic growth, energy consumption, and carbon… More >

Dong Liu¹, Runze Zhang¹, Anjie Hu¹, Na Liu¹, Liu Tang^2,3,*, Xiaozhou Wu⁴, Jun Wang^2,5

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2579-2612, 2025, DOI:10.32604/fdmp.2025.070407 - 30 October 2025

Abstract This study compares two end-cooling systems, convective–radiant combined cooling (FR+FC) and fan coil convection (FC), through continuous experimental investigations, focusing on the impact of window-to-wall ratio (WWR) on indoor thermal comfort, temperature distribution, humidity, and energy consumption. Results show that increasing WWR amplifies indoor temperature fluctuations. While the overall predicted mean vote (PMV) remains within the Level-II comfort range (−1.0 to +1.0), the FC system exhibits pronounced local PMV gradients near west-facing windows, especially at 80% WWR, where transient PMV reaches 1.26 close to the window, 0.89 higher than at the room center. In contrast, More >

Hongsheng Su¹, Wenyao Su¹, Yulong Che^1,*, Xiping Ma², Tian Zhao¹, Limiao Ren¹

Energy Engineering, Vol.122, No.11, pp. 4777-4797, 2025, DOI:10.32604/ee.2025.068134 - 27 October 2025

Abstract Aiming at the issues of insufficient carrying capacity, limited flexibility, and weak source-network-load-storage coordination capability in distribution networks under the background of high-proportion new energy integration. This study proposes a bi-level optimization model for ADN integrating hybrid wind-solar-hydrogen-storage systems. First, an electro-hydrogen coupling system framework is constructed, including models for electrolytic hydrogen production, hydrogen storage, and fuel cells. Meanwhile, typical scenarios of wind-solar joint output are developed using Copula functions to characterize the variability of renewable energy generation. Second, a bi-level optimization model for ADN with electrolytic hydrogen production and storage systems is established: the… More >

Sheng Bi^1,2,*, Jiayan Wang¹, Dong Su¹, Hui Lu¹, Yu Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4135-4151, 2025, DOI:10.32604/ee.2025.066735 - 30 September 2025

Abstract By comparing price plans offered by several retail energy firms, end users with smart meters and controllers may optimize their energy use cost portfolios, due to the growth of deregulated retail power markets. To help smart grid end-users decrease power payment and usage unhappiness, this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection. An enhanced state-based Markov decision process (MDP) without transition probabilities simulates the decision issue. A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue. Several adjustments to the sampling and data… More >

Alya Penta Agharid¹, Indra Permana², Linlan Chang¹, Yi-Han Luo², Fujen Wang^2,*

Energy Engineering, Vol.122, No.9, pp. 3839-3866, 2025, DOI:10.32604/ee.2025.068586 - 26 August 2025

Abstract Air conditioning (AC) is essential for maintaining indoor comfort during Taiwan’s hot and humid summers but significantly contributes to increased energy consumption. This study evaluates the effects of AC duty-cycling strategies on energy performance, thermal comfort, and operational costs in office environments. Duty-cycling was implemented using a building energy management system (BEMS), which remotely controlled the ON/OFF cycles of AC units. Five duty-cycling modes were tested, with some modes incorporating air circulation during OFF periods. Field measurements of energy consumption, temperature, humidity, and air velocity were conducted and integrated with thermal comfort analysis tools to… More >

Amir Sina Darabi^*, Mehdi Ravanshadnia

Energy Engineering, Vol.122, No.9, pp. 3753-3792, 2025, DOI:10.32604/ee.2025.065241 - 26 August 2025

Abstract Although currently, a large part of the existing buildings is considered inefficient in terms of energy, the ability to save energy consumption up to 80% has been proven in residential and commercial buildings. Also, carbon dioxide is one of the most important greenhouse gases contributing to climate change and is responsible for 60% of global warming. The facade of the building, as the main intermediary between the interior and exterior spaces, plays a significant role in adjusting the weather conditions and providing thermal comfort to the residents. In this research, 715 different scenarios were defined… More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >

Jafar Aminu^1,2,*, Rohaya Latip^1,*, Zurina Mohd Hanafi¹, Shafinah Kamarudin¹, Danlami Gabi²

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2989-3011, 2025, DOI:10.32604/cmc.2025.062451 - 03 July 2025

Abstract As mobile edge computing continues to develop, the demand for resource-intensive applications is steadily increasing, placing a significant strain on edge nodes. These nodes are normally subject to various constraints, for instance, limited processing capability, a few energy sources, and erratic availability being some of the common ones. Correspondingly, these problems require an effective task allocation algorithm to optimize the resources through continued high system performance and dependability in dynamic environments. This paper proposes an improved Particle Swarm Optimization technique, known as IPSO, for multi-objective optimization in edge computing to overcome these issues. To this… More >

Jiaqing Wang¹, Yuming Shen^2,*, Xuli Wang², Jiayin Xu²

Energy Engineering, Vol.122, No.7, pp. 2751-2766, 2025, DOI:10.32604/ee.2025.064317 - 27 June 2025

Abstract To adapt to the uncertainty of new energy, increase new energy consumption, and reduce carbon emissions, a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed. Firstly, the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model, and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios. Finally, considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system More >

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >