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This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field.

Disputed supply chains, inappropriate weather and low investment, followed by the Russian invasion of Ukraine, has led to a phenomenal energy crisis, especially in the Horn of Africa. Accordingly, proposing eco-friendly and sustainable solutions to diversify the access of electricity in the Republic of Djibouti, which has no conventional energy resources and is completely energy-dependent on its neighboring countries, has become a must. Therefore, the implementation of sustainable renewable and energy storage systems is nationally prioritized. This paper deals, for the first time, with the exploitation of such an affordable and carbon-free resource to produce hydrogen from wind energy in the rural areas of Nagad and Bara Wein in Djibouti. The production of hydrogen and the relevant CO2 emission reduction using different De Wind D6, Vestas and Nordex wind turbines are displayed while using Alkaline and Proton Exchange Membrane (PEM) electrolyzers. The Bara Wein and Nagad sites had a monthly wind speed above 7 m/s. From the results, the Nordex turbine accompanied with the alkaline electrolyzer provides the most affordable electricity production, approximately 0.0032 $/kWh for both sites; this cost is about one per hundred the actual imported hydroelectric energy price. Through the ecological analysis, the Nordex turbine is the most suitable wind turbine, with a CO2 emission reduction of 363.58 tons for Bara Wein compared to 228.76 tons for Nagad. While integrating the initial cost of wind turbine implementation in the capital investment, the mass and the levelized cost of the produced green hydrogen are estimated as (29.68 tons and 11.48 $/kg) for Bara Wein with corresponding values of (18.68 tons and 18.25 $/kg) for Nagad.

Djibouti has an ambitious program to develop and expand its energy demand to satisfy local demand and increase the energy access in rural and urban areas. The renewable energy development is vital in Djibouti’s strategy including 2020’s vision. To increase investment in clean energy by reducing dependence on oil and derivative products, Djibouti want to be the first sub-Saharan African nation using 100% clean energy. This paper examines, for the first time, wind energy potential at Djibouti-city using 5-years (2014–2018) wind speed data collected at 10 m height of wind power using Weibull parameters. Such a study was not feasible before due to the lack of data in this urban area. The results showed the possibility to implement and develop urban wind energy sector in Djibouti-city for domestic applications. The statistical wind speed, the wind rose, and the power density were computed. This study is also part of the implementation project of five micro wind turbines of 1–25 kW in different height areas. The strong wind flows are Eastern and Western over the considered period.The Polaris P12-25 wind turbine provided the best capacity factor (CF = 9.629%) and energy output (AEP = 2.1 × 104 MWh) comparing to the others technologies because the study site having low monthly wind speeds. Finally, according to the analysis of wind power production, Djibouti-city needs to install the wind turbines with high hub height greater than 30.5 m for efficient harvesting. Keywords: Wind speed, Weibull distribution, Wind power density, Wind turbine, Capacity factor, Urban, Djibouti