As efforts to reduce the environmental impact of energy production expand, renewable energy sources are becoming increasingly significant in the global energy balance. Over the anticipated 20-year life of a wind turbine, operation and maintenance (O&M) expenditures are predicted to account for 65%–90% of the overall investment cost, including inflation and crane charges. The higher estimate is based on 600–7500 kW machines in North America, while the lower estimate derives from the Danish fleet of 600 kW turbines. Reliability studies indicate that O&M costs contribute roughly 20%–25% of the levelized cost per kWh. These expenses strongly influence the profitability of wind farms and the competitiveness of wind turbines compared to other renewable energy options, highlighting significant potential for technological improvement. The profitability of a wind farm and the competitiveness of wind turbines compared to other green energy options are strongly influenced by O&M costs. Accurate wind speed and power generation prediction is therefore essential to improve efficiency and reduce investment costs. To address this, machine learning algorithms such as long-short term memory (LSTM), gated recurrent unit (GRU), artificial neural network (ANN), XGBoost, random forest (RF), and support vector machine (SVM) have been applied for forecasting wind speed and predicting power generation. Specifically, LSTM, GRU, and ANN are employed for wind speed forecasting, while XGBoost, RF, and SVM are used for electricity generation prediction. Results show that LSTM and GRU achieve lower root mean squared error than ANN in wind speed forecasting, while RF provides higher accuracy for power generation prediction compared to XGBoost and SVM. Overall, LSTM, GRU, and RF demonstrate strong performance in wind forecasting and power generation prediction.
Rajkamal et al. (Fri,) studied this question.