A review of artificial intelligence applied for the solution of issues in the extensive adaption of solar and wind energy

Abstract

Electricity requirement is booming worldwide, especially in developing and underdeveloped nations due to their economic growth. The world is looking for sustainable and reliable energy sources apart from conventional ones. Renewable energy, such as solar and wind, has an enormous potential to suffice the electricity need. Various challenges faced by the wider adoption of solar and wind resources are discussed in this paper. Case studies from global best practices are included. Policy lessons drawn from the German renewable sector are discussed. A project of a hybrid solar-wind power plant in Libya is discussed with its implications in the Indian scenario. Japan’s floating solar islands are studied, which can be the solution to the issue of availability of land. Various machine learning algorithms that are employed in the renewable energy field to overcome enduring challenges in the sector are surveyed. A brief discussion on the domains in which these algorithms are employed is added to allude to the possible solutions to the conundrum of a high renewable energy ratio in the energy mix. Artificial intelligence and machine learning have proven potential to be a part of the solution for challenges faced in the wider adaption of solar and wind energy in the power sector.

Highlights

• Overcoming the obstacles is imperative in a wider adaptation of renewable energies.

• Global best practices provide good templates to learn the lessons from them.

• Hybrid renewable systems can decrease the temporal mismatch of demand and supply.

• AI/ML needs to be employed widely to better solve the issues in the renewable field.

KEYWORDS:

• Solar energy
• wind energy
• hybrid renewable
• artificial intelligence (AI)
• machine learning (ML)

Disclosure statement

No potential conflict of interest was reported by the author(s).