Artificial Intelligence Applications in Improving Electric Load Management: Case Studies

Abstract

As the globe faces serious climate and energy concerns, Artificial Intelligence has shown to be a transformative force in the advancement of renewable energy technologies. This paper examines the existing and prospective applications of artificial intelligence in renewable energy. This work focuses on the transformative role of Artificial Intelligence (AI) in making renewable energy systems more efficient, reliable, and scalable. AI technology - from machine learning and deep learning to learning to learning - optimization of energy production, forecasts demand, future maintenance and management of decentralized energy networks.

Looking ahead, emerging fields like quantum machine learning and AI-powered augmented reality offer exciting possibilities, with the potential to fundamentally reshape energy infrastructures.

The survey highlights major innovations across wind and solar power, energy storage, and smart grids, emphasizing how AI helps address persistent challenges such as intermittency and variability. Equally important are the supporting technologies—big data, the Internet of Things (IoT), and real-time analytics—that drive the development of more advanced AI models. We also explore how AI is shaping energy policy and market modeling, paving the way for broader renewable energy adoption.

Real-world applications bring these ideas to life. For instance, Google's collaboration with DeepMind has enhanced wind power generation using wiser forecasting, while Australia's National Electricity Market has looked to AI in order to enhance grid stability. These cases demonstrate that AI’s role in renewable energy is not just theoretical—it is already delivering measurable results and redefining what’s possible in the global energy landscape.

. In order to optimize AI's potential for advancing sustainable energy and combating climate change, this study identifies the obstacles preventing its implementation in renewable energy systems and makes suggestions for enhancing current technology.