Session: 11-01: Alternative Energy Converstion Technology (including Wind, Geothermal, Hydro, and Ocean)

Paper Number: 157497

157497 - Smart, Sustainable Energy Systems for Remote Communities: Leveraging Ai, Optimization, and Renewable Integration 

Abstract: 

Smart, Sustainable Energy Systems for Remote Communities: Leveraging AI, Optimization, and Renewable Integration

Nwaiwu Uchechukwu

Department of Mechanical Engineering

Abia State University Uturu, Nigeria

philip.uche2009@yahoo.com

Abstract

The shift of remote communities to renewable energy aligns with global climate action goals aimed at reducing carbon emissions and combating climate change. This study seeks to advance the development of scalable and adaptive energy systems that improve energy access while minimizing environmental impacts through the integration of multiple renewable energy sources, including geothermal, solar, and wind power. The central research question driving this study is how advanced optimization techniques, artificial intelligence (AI), and machine learning (ML) can be seamlessly integrated to create energy systems that are both efficient and adaptable to the diverse and changing needs of remote communities. To address this, the study employed mixed-integer linear programming (MILP) to model and optimize earth-integrated thermal energy systems. MILP was utilized as it provides a robust framework for managing complex variables and constraints within energy system design, ensuring that the integration of different energy sources and their operational dynamics are optimized for peak efficiency. This optimization process was complemented using smart grid technologies, which play a critical role in enhancing overall system reliability and enabling efficient energy distribution, even in the face of varying consumption patterns and external conditions. Moreover, the application of AI and ML algorithms was a key component of the study, aimed at developing adaptive models capable of real-time responsiveness. These models are designed to monitor and react to fluctuations in both energy demand and environmental factors, enhancing the resilience and performance of the energy systems. By incorporating machine learning, the systems learn and adapt over time, offering improvements in energy distribution efficiency, predictive maintenance, and proactive management of energy resources. A unique aspect of this study was its collaboration with local and indigenous communities. Engaging these stakeholders ensured that the solutions developed were not only technically sound but also culturally relevant and sensitive to the needs of the populations they aim to serve. This approach acknowledges that energy technology must be both socially appropriate and technically feasible to achieve long-term success and acceptance. The results of the study underscore the necessity of an interdisciplinary approach that merges engineering, mathematical modeling, AI, and environmental science. By considering social and environmental dimensions alongside technical innovations, the study contributes meaningfully to advancing energy equity and sustainability. Ultimately, this work paves the way for creating renewable energy systems that are resilient, adaptable, and capable of supporting the diverse needs of remote and underserved communities.

Presenting Author: Uchechukwu Nwaiwu Abia State University Uturu

Presenting Author Biography: Nwaiwu Uchechukwu is a doctoral student at the University of Surrey, United kingdom. He holds Masters in Thermofluid and Energy Engineering and Bachelors degree in mechanical engineering. He has several peer-reviewed articles.