Deep learning framework for wind speed prediction in Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory , through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods.