Data-Driven Decision Support System for Sustainable Energy Management: An AI-IoT Fusion Approach

Sustainable energy management involves the strategic planning, implementation, and optimization of energy resources to meet present needs without compromising the ability of future generations to meet their own energy requirements. It encompasses various activities, including energy generation, distribution, consumption, and conservation, with the overarching goal of minimizing environmental impact while ensuring economic viability and social equity. The study presents a novel Data-Driven Decision Support System (DDDSS) designed for Sustainable Energy Management (SEM) through an innovative AI-IoT Fusion Approach. This approach integrates Artificial Intelligence (AI) techniques with Internet of Things (IoT) technologies to enhance the efficiency and sustainability of energy management systems. A key component of the proposed system is the SmartARIMA algorithm, which amalgamates AutoRegressive Integrated Moving Average (ARIMA) with Reinforcement Learning (RL) principles to predict energy demand and optimize resource allocation in real-time. To evaluate the effectiveness of the SmartARIMA algorithm, simulation analyses are conducted and compared with suitable existing algorithms using appropriate metrics. These metrics include mean absolute percentage error, forecast accuracy and scalability. The simulation results demonstrate that the SmartARIMA algorithm outperforms traditional algorithms in terms of accuracy and adaptability, thereby enabling more effective decision-making in sustainable energy management. The proposed DDDSS framework, leveraging the SmartARIMA algorithm within an AI-IoT fusion context, offers a promising solution for addressing the challenges of sustainable energy management. By harnessing the power of AI and IoT technologies, organizations can optimize energy consumption, reduce waste, and promote environmental sustainability. The findings of this research contribute to the advancement of data-driven approaches in sustainable energy management and provide valuable insights for practitioners and researchers alike.