Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.

A constant and consistent supply in electrical energy in a location is a reflection of a good economy. Developing countries nevertheless don't have access to this quality of energy, which slows down their economy and consequently development. Wind is a clean, sustainable and renewable resource which can be used to meet the energy needs in such countries. However, the intermittent nature of wind yields fluctuations on the amount of energy produced by a wind turbine. Coupled with frictional power losses in the wind turbine gearbox bearings, one can't be sure on the exact amount of energy that will be produced. This leads to the distribution and management issues. To tackle this issue, we propose here the use of Large Language models. These are tools which have been proving their potential in various domains till date and whose potential are still to be seen in the field to our knowledge. Taking advantage of their flexibility and adaptability to any model and dataset, we intend to explore its abilities in the fields of wind energy and tribology. Making use of available data, predictions on the wind energy potential and power losses will be carried out using Large Language models such as BERT. The results of this work intends to promote the use of wind energy by lifting barriers in thee management and knowledge of the resource.