The rapid adoption of Artificial Intelligence (AI) in energy management is reshaping the way eco-friendly institutions optimize their energy systems, driving sustainability and operational efficiency. This chapter explores the integration of AI-driven energy optimization and automated power management systems in institutional settings, with a particular focus on hybrid energy systems, energy storage solutions, and real-time demand prediction. By leveraging AI’s predictive capabilities, these systems enhance the management of renewable energy sources such as solar and wind, ensuring stable, reliable, and cost-effective energy supply. The chapter highlights how AI optimizes energy storage, balancing grid demand with renewable energy production, while mitigating the intermittency issues of renewable sources. Furthermore, the integration of weather forecasting data with AI is shown to improve energy demand prediction accuracy, leading to more effective power distribution. Despite the significant potential, challenges related to data integration, system scalability, and user engagement in these advanced energy systems remain. This chapter presents AI as a pivotal technology for eco-friendly campuses striving to achieve sustainability goals, offering a comprehensive framework for energy optimization and smart grid integration.
As global sustainability concerns intensify, eco-friendly institutions are seeking innovative solutions to optimize their energy consumption while minimizing their environmental impact [1]. The traditional methods of managing energy use in such institutions, although effective to some extent, often fail to fully address the growing need for energy efficiency, flexibility, and scalability [2]. Artificial Intelligence (AI) has emerged as a powerful tool for overcoming these limitations [3]. By harnessing AI, eco-friendly campuses can transform their energy management systems, making them smarter, more responsive, and more adaptive to dynamic conditions [4]. AI’s ability to process vast amounts of data, predict energy demand patterns, and optimize energy usage in real-time offers significant advantages in terms of sustainability and operational efficiency [5].
The key advantage of AI-driven energy optimization lies in its predictive capabilities [6]. Machine learning algorithms enable systems to analyze historical and real-time energy usage data, learning from trends and making accurate predictions about future demand [7]. These systems can anticipate fluctuations in energy consumption based on factors such as time of day, weather patterns, occupancy levels, and even external events [8]. This predictive power allows institutions to optimize energy distribution, ensuring that energy resources are used efficiently and waste is minimized [9]. The ability to adjust energy usage proactively based on anticipated demand is a key element in reducing operational costs and enhancing the sustainability of institutions [10].
Another critical application of AI in energy management is its integration with renewable energy systems [11]. As eco-friendly institutions strive to increase their use of renewable energy sources like solar, wind, and geothermal, AI helps address the inherent variability of these energy sources [12]. AI-driven systems are capable of forecasting renewable energy generation based on weather conditions and historical data, allowing institutions to effectively balance supply and demand [13]. This capability ensures that renewable energy is used at its full potential, reducing reliance on non-renewable grid power and optimizing the use of energy storage systems when generation exceeds demand [14]. AI’s role in managing renewable energy contributes directly to the overarching goal of reducing an institution's carbon footprint [15].
