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  • Open Access

    ARTICLE

    Enhancing Evaporative Cooler Efficiency through Magnetized Water and Heat Exchanger Optimization

    Mohammed J. Alshukri1,2, Amjed Al-Khateeb3, Ahmed Mohsin Alsayah2, Adel A. Eidan4, Hameed B. Mahood5,6,*

    Energy Engineering, Vol.122, No.4, pp. 1359-1372, 2025, DOI:10.32604/ee.2025.060613 - 31 March 2025

    Abstract This research presents a new method to boost the efficiency of evaporative coolers by integrating magnetized water and a heat exchanger. Magnetized water, known for its high evaporation rate and reduced surface tension, offers a promising way to enhance air cooler performance. Additionally, the advanced heat exchanger both improves air cooling capacity and controls humidity levels. Aloni 100 L, a locally manufactured evaporative cooling system, and tap water were used in experiments. Tap water was magnetized using recycled magnets extracted from computer hard drives. Twenty-six magnets meticulously arranged within rectangular grooves, each with a minimum… More >

  • Open Access

    ARTICLE

    MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

    Ruoxin Li1,*, Shaoxiong Wu1, Fengping Deng1, Zhongli Tian1, Hua Cai1, Xiang Li1, Xu Xu1, Qi Liu2,3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

    Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

  • Open Access

    ARTICLE

    A Dynamic Prediction Approach for Wire Icing Thickness under Extreme Weather Conditions Based on WGAN-GP-RTabNet

    Mingguan Zhao1,2,*, Xinsheng Dong1,2, Yang Yang1,2, Meng Li1,2, Hongxia Wang1,2, Shuyang Ma1,2, Rui Zhu3, Xiaojing Zhu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2091-2109, 2025, DOI:10.32604/cmes.2025.059169 - 27 January 2025

    Abstract Ice cover on transmission lines is a significant issue that affects the safe operation of the power system. Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents. However, under extreme weather conditions, strong instantaneous wind can cause tension sensors to fail, resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models. In this paper, we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments. The model expands… More >

  • Open Access

    ARTICLE

    IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

    Zhe Li, Yun Liang, Jinyu Wang, Yang Gao*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1171-1192, 2025, DOI:10.32604/cmc.2024.057225 - 03 January 2025

    Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >

  • Open Access

    ARTICLE

    Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

    Parthasarathi Manivannan1, Palaniyappan Sathyaprakash1, Vaithiyashankar Jayakumar2, Jayakumar Chandrasekaran3, Bragadeesh Srinivasan Ananthanarayanan4, Md Shohel Sayeed5,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4327-4347, 2024, DOI:10.32604/cmc.2024.055628 - 19 December 2024

    Abstract Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness. However, accurately classifying diverse and complex weather conditions remains a significant challenge. While advanced techniques such as Vision Transformers have been developed, they face key limitations, including high computational costs and limited generalization across varying weather conditions. These challenges present a critical research gap, particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’ intricate and dynamic nature in real-time. To address this gap, we propose a Multi-level Knowledge Distillation (MLKD) framework, which leverages… More >

  • Open Access

    ARTICLE

    Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

    Yuxuan Zhao1,2,*, Bo Wang1, Shu Wang1, Wenjun Xu2, Gang Ma2

    Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

    Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

  • Open Access

    ARTICLE

    Performance Analysis of Curved Track G2T-FSO (Ground-to-Train Free Space Optical) Model under Various Weather Conditions

    Mohammed A. Alhartomi1,*, Mohammad F. L. Abdullah2, Wafi A. B. Mabrouk2, Mohammed S. M. Gismalla3, Ahmed Alzahmi1, Saeed Alzahrani1, Mohammad R. Altimania1, Mohammed S. Alsawat4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2087-2105, 2024, DOI:10.32604/cmes.2024.055679 - 31 October 2024

    Abstract The demand for broadband data services on high-speed trains is rapidly growing as more people commute between their homes and workplaces. However, current radio frequency (RF) technology cannot adequately meet this demand. In order to address the bandwidth constraint, a technique known as free space optics (FSO) has been proposed. This paper presents a mathematical derivation and formulation of curve track G2T-FSO (Ground-to-train Free Space Optical) model, where the track radius characteristics is 2667 m, divergence angle track is 1.5° for train velocity at V = 250 km/h. Multiple transmitter configurations are proposed to maximize More >

  • Open Access

    ARTICLE

    Efficient and Cost-Effective Vehicle Detection in Foggy Weather for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems

    Naeem Raza1, Muhammad Asif Habib1, Mudassar Ahmad1, Qaisar Abbas2,*, Mutlaq B. Aldajani2, Muhammad Ahsan Latif3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049 - 15 October 2024

    Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >

  • Open Access

    ARTICLE

    Synergizing Wind, Solar, and Biomass Power: Ranking Analysis of Off-Grid System for Different Weather Conditions of Iran

    Razieh Keshavarzi, Mehdi Jahangiri*

    Energy Engineering, Vol.121, No.6, pp. 1381-1401, 2024, DOI:10.32604/ee.2024.050029 - 21 May 2024

    Abstract Nowadays, the use of renewable energies, especially wind, solar, and biomass, is essential as an effective solution to address global environmental and economic challenges. Therefore, the current study examines the energy-economic-environmental analysis of off-grid electricity generation systems using solar panels, wind turbines, and biomass generators in various weather conditions in Iran. Simulations over 25 years were conducted using HOMER v2.81 software, aiming to determine the potential of each region and find the lowest cost of electricity production per kWh. In the end, to identify the most suitable location, the Technique for Order Preference by Similarity… More > Graphic Abstract

    Synergizing Wind, Solar, and Biomass Power: Ranking Analysis of Off-Grid System for Different Weather Conditions of Iran

  • Open Access

    ARTICLE

    Weather-Driven Solar Power Forecasting Using D-Informer: Enhancing Predictions with Climate Variables

    Chenglian Ma1, Rui Han1, Zhao An2,*, Tianyu Hu2, Meizhu Jin2

    Energy Engineering, Vol.121, No.5, pp. 1245-1261, 2024, DOI:10.32604/ee.2024.046644 - 30 April 2024

    Abstract Precise forecasting of solar power is crucial for the development of sustainable energy systems. Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic (PV) power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data. To overcome these challenges, this research presents a cutting-edge, multi-stage forecasting method called D-Informer. This method skillfully merges the differential transformation algorithm with the Informer model, leveraging a detailed array of meteorological variables and historical PV power generation records. The D-Informer model exhibits remarkable superiority over competing… More > Graphic Abstract

    Weather-Driven Solar Power Forecasting Using D-Informer: Enhancing Predictions with Climate Variables

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