In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Janice Anta Zebaze is using AI to address energy supply challenges and she told us more about the research she's carried our so far, her plans for further investigations, and what inspired her to pursue a PhD in the field. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am currently pursuing my PhD in Physics at the University of Yaounde I in Cameroon, with a focus on renewable energy systems, tribology, and artificial intelligence. The aim of my research is to address energy supply challenges in developing countries by leveraging AI to evaluate resource availability and optimize energy systems.

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future.

As the world increasingly turns to renewable energy sources to power our homes and businesses, the need for high-performance computing (HPC) and artificial intelligence (AI) is also increasing. HPC and AI are used to model and predict complex phenomena, like weather patterns and climate change, as well as to optimize the design of renewable energy systems. The demand for HPC and AI is therefore increasing in many industries that are critical to the transition to a low-carbon economy. In addition, a great deal of research and development (R&D) has been put into play using these technologies, which are leading to breakthroughs that promise to change the way people live and work. With supercomputing technology in the limelight and companies focusing on enhancing their data centers' performance, it's easy to get caught up in the hype surrounding the new computer systems that boast high computing power. But a lot of people aren't sure where all of this is going or why it's such a big deal.

The energy grid is a complex network of hard and soft infrastructure that delivers electricity from producers to consumers. Producing the electricity that powers our homes and businesses involves dozens of steps, including generation, transmission, distribution, and consumption. Luckily, most people in the United States don't have to think about this process. They simply pay the electricity bill each month and the lights come on. The electricity grid in the United States has remained relatively stagnant for decades.

Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on the renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being designed and developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort towards the provision of uninterrupted power to the end-users is strongly dependent on an effective and fault resilient Operation and Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust Prognostic Maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Machine Learning (ML) techniques are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges including data-related issues, such as the availability and quality of the data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies key future research directions. We believe such detailed analysis will provide a baseline for future research in the domain.

An increasing number of countries, companies and regions are embracing sustainable energy generation and the landscape is rapidly evolving. Here are 6 renewable energy trends to watch in the coming year. Renewable energy is booming in China.Getty Energy storage plays an important role in balancing power supply and demand, and is key to tackling the intermittency issues of renewable energy. Pairing a storage system with a renewable energy source ensures a smooth and steady power supply, even when weather conditions are not optimal for energy generation. Batteries are the most common storage devices used in renewable energy systems and their use is increasing on both the residential and grid-wide scale.