Artificial Intelligence Driven Internet of Things Framework for Wind Energy Monitoring and Performance Enhancement in Smart Cities

IF 4.3 3区 工程技术 Q2 ENERGY & FUELS
Zakaria Mohamed Salem El-Barbary, Lola Safarova, Farruh Atamurotov, Ahmed Mohsin Alsayah, Bharosh Kumar Yadav
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引用次数: 0

Abstract

The integration of renewable energy sources in urban environments presents unique challenges due to complex wind patterns and infrastructure limitations. This study developed and implemented an advanced Internet of Things (IoT) framework incorporating deep learning algorithms for real-time wind energy monitoring and optimization in Abha, Saudi Arabia, addressing the limitations of conventional wind energy systems through intelligent sensor networks and predictive analytics. The study deployed 2300 IoT sensors across 75 urban wind turbines, collecting environmental and performance data over 24 months. The methodology implemented a custom long short-term memory (LSTM) neural network architecture with a dropout rate of 0.3, utilizing TensorFlow framework version 2.7 for model training. The system incorporated comprehensive sensor arrays including ultrasonic anemometers, digital wind vanes, temperature sensors, and tri-axial accelerometers, with data collection frequencies ranging from 0.1 Hz to 1 kHz. The implementation resulted in a 34.2% increase in energy harvesting efficiency, with turbine downtime reduced by 56%. The LSTM model achieved 91.7% accuracy in wind pattern prediction, enabling proactive adjustments that improved overall system reliability by 29%. Component-wise reliability analysis revealed the highest performance in sensor networks (MTBF = 94.3 days) and communication infrastructure (MTBF = 89.5 days). Statistical validation confirmed significant improvements across all metrics (p < 0.001) with the autoregressive integrated moving average (ARIMA) model demonstrating strong predictive capability (R2 = 0.934). The AI-driven framework achieved a 41% reduction in maintenance costs while increasing annual energy output by 23.8%, suggesting favorable techno-economic viability despite initial investment requirements. The developed framework demonstrates significant potential for optimizing urban wind energy systems through AI-driven monitoring and predictive maintenance. The results establish a scalable approach for smart city wind energy management, providing a comprehensive solution for urban renewable energy integration.

Abstract Image

智能城市风能监测与性能提升的人工智能驱动物联网框架
由于复杂的风力模式和基础设施的限制,可再生能源在城市环境中的整合面临着独特的挑战。本研究开发并实施了一个先进的物联网(IoT)框架,该框架结合了深度学习算法,用于沙特阿拉伯Abha的实时风能监测和优化,通过智能传感器网络和预测分析解决了传统风能系统的局限性。该研究在75个城市风力涡轮机上部署了2300个物联网传感器,在24个月内收集了环境和性能数据。该方法实现了一个自定义的LSTM神经网络架构,辍学率为0.3,利用TensorFlow框架2.7版本进行模型训练。该系统集成了包括超声波风速计、数字风标、温度传感器和三轴加速度计在内的综合传感器阵列,数据采集频率从0.1 Hz到1 kHz不等。实施后,能量收集效率提高了34.2%,涡轮机停机时间减少了56%。LSTM模型在风型预测方面的准确率达到91.7%,能够进行主动调整,将整个系统的可靠性提高29%。组件可靠性分析显示,传感器网络(MTBF = 94.3天)和通信基础设施(MTBF = 89.5天)的性能最高。统计验证证实了所有指标的显著改善(p <;自回归综合移动平均(ARIMA)模型具有较强的预测能力(R2 = 0.934)。人工智能驱动的框架降低了41%的维护成本,同时将年能源产量提高了23.8%,这表明尽管初始投资要求很高,但仍具有良好的技术经济可行性。开发的框架显示了通过人工智能驱动的监测和预测性维护来优化城市风能系统的巨大潜力。研究结果为智慧城市风能管理建立了可扩展的方法,为城市可再生能源整合提供了全面的解决方案。
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来源期刊
International Journal of Energy Research
International Journal of Energy Research 工程技术-核科学技术
CiteScore
9.80
自引率
8.70%
发文量
1170
审稿时长
3.1 months
期刊介绍: The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability. IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents: -Biofuels and alternatives -Carbon capturing and storage technologies -Clean coal technologies -Energy conversion, conservation and management -Energy storage -Energy systems -Hybrid/combined/integrated energy systems for multi-generation -Hydrogen energy and fuel cells -Hydrogen production technologies -Micro- and nano-energy systems and technologies -Nuclear energy -Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) -Smart energy system
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