Integrating Indigenous Knowledge in Modern Wind Prediction Models Along Coastal Areas: A Nigerian Perspective with Global Significance

Abstract The calls for conservation, exploitation and integration of Indigenous knowledge have gained much traction due to errors, shortfalls and the multifaceted nature of modern wind forecasting models. Optimizing the use of wind energy in the current energy transition further buttresses these calls. This paper explores the potential benefits of combining qualitative traditional wind prediction methods in local communities with advanced meteorological models to improve wind power generation. Coastal states were identified to have the potential to generate substantial wind energy, yet forecasting limitations hinder the effective harnessing of their resources. Through a comprehensive review of case studies, concepts and existing models, this study revealed that validation of indigenous knowledge is a challenge, there are scarcity of regional wind data infrastructure and a complementary approach to investigating wind dynamics may require an integration of indigenous knowledge with potentials of improving wind predictions, energy generation and community engagement. This engagement can also be a stirring factor to eschew resistance to renewable energy targets and bolster sustainable energy infrastructure. Furthermore, integrating these local insights into machine learning algorithms, numerical weather predictions and satellite data may close existing error gaps in addressing localized environmental and energy challenges.