The conventional approaches of electricity production have a massive side effect on the global climate. With the advantages of minimum carbon pollution, renewable energy is a feasible solution to make the world safer and energy proficient. Over the past few years, many types of renewable energy resources – such as wind, solar, geothermal, biomass, and hydropower – have been exploited. As innovation brings down costs, renewable power is booming. Renewable energy contributions are currently meeting approximately 20% of global energy consumption. They are also fulfilling approximately 25% of global electricity generation.

The success of a renewable energy application usually depends on whether the load can extract sufficiently high power from the renewable sources to keep high energy conversion efficiency. However, there are a good number of nonlinear interactions among multiple parameters controlling the integration of renewable energy into the grid. Artificial Intelligence (AI) technologies develop smart entities that will produce more accurate predictions for complicated problems. AI algorithms (e.g. neural networks, fuzzy logic, intelligent optimization algorithms) have become more and more popular as alternative approaches to conventional techniques in solving problems such as modeling, identification, optimization, availability prediction, forecasting, and control of renewable energy systems.

The implementation of novel AI-based approaches will add additional performance improvement of renewable energy systems. Opportunities have been created for collaboration between electrical researchers and AI researchers to design and develop technologies to improve:

- modeling and parameters estimation;

- net load forecasting;

- line loss predictions;

- maintaining system reliability;

- energy efficiency;

- renewable energy operations;

- integrating hybrid solar and battery storage systems;

- predicting equipment failure;

- decision process for grid operators.

This Research Topic seeks to contribute to advancing the generation capacity and integration of renewable energy into the grid with AI technologies. We are interested in theoretically, empirically, and/or methodologically oriented contributions focusing on innovative AI methodologies/applications to renewable energy forecasting and integration, including reviews and case studies.