Optimal Placement of Wind Power System Using Machine Learning - Abdul Karim; Muhammad Amir Raza; Darakhshan Ara; Muhammad Shahid; Shakir Ali Soomro (Sir Syed University Research Journal of Engineering and Technology)
The switch from fossil fuels to sustainable energy sources is crucial for power generation sustainability. Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbines using the Pythons algorithms namely, Long Short-Term Memory (LSTM), Prophet (PT), Support Vector Regression (SVR), Linear Regression (LR), Seasonal Autoregressive Integrated Moving Average with External Factors (SARIMAX), and K-Nearest Neighbors (KNN). These models have taken raw data from 2000 to 2019 and tested from 2020 to 2023 and finally predict the future from 2023 to 2030. The results show that for renewable energy variables such as temperature and wind speed, statistical models such as SARIMAX perform better than traditional models such as LSTM, PT, SVR, LR, and KNN. Because SARIMAX captures long-term dependencies, it is particularly suitable for time series data where past events have a large impact on future values. It is recommended that wind power has enormous potential and offers a low-carbon future for Doha Qatar.