Embracing Artificial Intelligence for Forecast-Based Energy Optimization: Advancing Sustainable Power Systems Towards SDG Achievement

Abstract

The increasing complexity of balancing grid power with renewable energy sources, amid rising global energy demands, underscores the need for intelligent and cost-effective energy management solutions. This research proposes an Artificial Intelligence (AI)-driven approach that leverages advanced forecasting and optimization techniques to enhance energy utilization, operational efficiency, and cost reduction. A key component of the system is an Autoregressive Integrated Moving Average (ARIMA) model, trained on historical consumption data to accurately predict energy demand and support real-time decision-making. The framework dynamically toggles between renewable and grid energy sources based on forecasted demand and real-time availability. When conditions are favourable, the system prioritizes renewable energy to reduce reliance on the grid and lower operational costs. In less favourable conditions, it strategically draws on grid power to preserve renewable reserves and maintain uninterrupted supply, or intelligently combines both sources when needed. To address the limitations of static, grid-dependent energy systems, particularly in emerging economies, the proposed model integrates ARIMA with a Genetic Algorithm (GA). This hybrid approach enhances the system’s ability to navigate complex, non-linear energy demand patterns by conducting global searches across vast solution spaces, resulting in improved adaptability and optimization accuracy. Aligned within Uganda’s Vision 2040 and the United Nations Sustainable Development Goals (SDGs), both of which place real weight on making energy and infrastructure more affordable and accessible, this work positions itself as a practical, scalable approach to energy management. It speaks most directly to SDG 7 Affordable and Clean Energy and SDG 13 Climate Action, though the alignment is not merely symbolic. The evaluation results, while still open to further validation in real-world settings, suggest noticeable gains in cost-efficiency, energy optimization, and overall system scalability. Taken together, these outcomes point to a framework that could realistically support more resilient and adaptive power systems, particularly in rapidly developing economies where such improvements are not just desirable but, in many cases, overdue.