This study investigates the integration of artificial intelligence (AI) and machine learning (ML)... more This study investigates the integration of artificial intelligence (AI) and machine learning (ML) technologies in solarbiomass hybrid energy systems for sustainable energy generation. Through a comprehensive empirical analysis of 250 hybrid energy installations across five regions, this research evaluates the performance optimization potential of AI/ML algorithms in renewable energy management. The study employed a mixed-methods approach with quantitative data collection from operational systems over 24 months (2023)(2024). Results demonstrate that AI-enhanced solar-biomass systems achieved 34.7% higher energy efficiency compared to conventional systems, with ML-based predictive maintenance reducing operational costs by 28.3%. The study found significant correlations between AI implementation and system reliability (r=0.847, p<0.001), while deep learning models improved energy forecasting accuracy to 92.4%. Integration challenges were identified in 18.2% of installations, primarily related to data synchronization and algorithm compatibility. These findings contribute to advancing sustainable energy technologies through intelligent optimization frameworks, providing empirical evidence for the transformative potential of AI/ML in hybrid renewable energy systems.
This study investigates the integration of artificial intelligence (AI) and machine learning (ML)... more This study investigates the integration of artificial intelligence (AI) and machine learning (ML) technologies in solar-biomass hybrid energy systems for sustainable energy generation. Through a comprehensive empirical analysis of 250 hybrid energy installations across five regions, this research evaluates the performance optimization potential of AI/ML algorithms in renewable energy management. The study employed a mixed-methods approach with quantitative data collection from operational systems over 24 months (2023-2024). Results demonstrate that AI-enhanced solar-biomass systems achieved 34.7% higher energy efficiency compared to conventional systems, with ML-based predictive maintenance reducing operational costs by 28.3%. The study found significant correlations between AI implementation and system reliability (r=0.847, p<0.001), while deep learning models improved energy forecasting accuracy to 92.4%. Integration challenges were identified in 18.2% of installations, primarily related to data synchronization and algorithm compatibility. These findings contribute to advancing sustainable energy technologies through intelligent optimization frameworks, providing empirical evidence for the transformative potential of AI/ML in hybrid renewable energy systems.
Precision agriculture utilizes information technologies to efficiently manage water and soil reso... more Precision agriculture utilizes information technologies to efficiently manage water and soil resources in agriculture. This technological revolution has been sparked by the big data analytics, machine learning and Internet of Things (IoT)in numerous industries. This game-changing technology has influence on houses, grids, smart cities, and health. Machine learning and data-intensive decisionmaking have been made possible, which has created new possibilities for efficiency and innovation in various fields. In several fields, the convergence of IoT, big data analytics, and machine learning has opened the way for cutting-edge solutions and enhanced results.The application of machine learning in the agricultural sector can increase production quantity and quality to meet increasing food demand. These advancements are transforming current agrarian approaches and generating new opportunities, despite some limitations. This paper presents a systematic appraisal of the role of IoT, big data analytics, and machine learning in precision agriculture, highlighting the potential benefits and challenges in this rapidly evolving field.
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Papers by Sunday Okuyade