AI-Driven Energy Management: Optimizing Supply and Demand to Reduce Imbalance and Enhance Consumer Engagement for a Sustainable Future - Dharmarajan, Aakash (Digital Repository of Theses - SSBM Geneva, 2025)
Background This research explores the implementation of AI-driven energy management strategies aimed at optimizing supply and demand, reducing energy imbalances, and enhancing consumer engagement within small and medium-sized enterprises (SMEs) for a sustainable future. As the energy sector evolves, there is an increasing need for innovative solutions that effectively address energy inefficiencies while promoting sustainability. Methods Utilizing a mixed-methods approach, the research integrates quantitative and qualitative analyses. Specifically, Long Short-Term Memory (LSTM) networks are employed for energy consumption forecasting, while Particle Swarm Optimization (PSO) is used to optimize energy usage set points. Additionally, qualitative assessments are conducted through AI-powered chatbots to gauge consumer experiences and engagement in energy-saving initiatives. Results Key findings indicate that AI technologies significantly improve the accuracy of energy consumption forecasting, resulting in better resource allocation and substantial reductions in operational costs for SMEs. The integration of PSO aids in determining optimal energy set points, further minimizing energy expenses while meeting operational needs. The study also demonstrates that AI-enhanced communication tools effectively increase consumer participation in energy-saving initiatives and foster positive attitudes toward energy efficiency. Discussion and Conclusion The implications of this research emphasize the potential for SMEs to leverage advanced AI-driven solutions to achieve operational efficiencies and sustainability goals. Moreover, the findings advocate for the establishment of supportive policies and training programs that facilitate the adoption of these innovative technologies. This study contributes valuable insights to the fields of energy management and sustainability, paving the way for future research into AI applications within the energy sector.Results Key findings indicate that AI technologies significantly improve the accuracy of energy consumption forecasting, resulting in better resource allocation and substantial reductions in operational costs for SMEs. The integration of PSO aids in determining optimal energy set points, further minimizing energy expenses while meeting operational needs. The study also demonstrates that AI-enhanced communication tools effectively increase consumer participation in energy-saving initiatives and foster positive attitudes toward energy efficiency. Discussion and Conclusion The implications of this research emphasize the potential for SMEs to leverage advanced AI-driven solutions to achieve operational efficiencies and sustainability goals. Moreover, the findings advocate for the establishment of supportive policies and training programs that facilitate the adoption of these innovative technologies. This study contributes valuable insights to the fields of energy management and sustainability, paving the way for future research into AI applications within the energy sector.