## 6 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Ref.** & **RMSE** & **Latency (ms)** \\ \hline MTK (Modified Taylor Kriging) & [15] & 15.23\% & - \\ Random Forest Spatial Interpolation (RFSI) & [22] & 25\% & 305.2 \\ LightGBM & [35] & 24\% & - \\ XGBoost & [36] & 56.85\% & \\ Auto Arima, Prophet, and ProLaF & [37] & 12.81\% & 38 \\ BiGRU, BiLSTM, BiRNN, and unidirectional LSTM & [38] & 47.25\% & 12.3 \\ LightGBM and MLOps & The proposed study & 58.61\% & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of previous studies in the literature. test set, specifically chosen from the summer season, spanning June to August. The model trained with monthly data demonstrated slightly improved prediction accuracy, achieving an RMSE value of 4.11% in terms of Megawatts (MW) [39]. In a separate study, [PERSON] et al. developed a KNN and LightGBM model using data from wind farms and weather data. A comparison of these two models revealed that the LightGBM model provided superior performance with a 13.26% RMSE, showcasing its improved predictive accuracy. [PERSON] et al. conducted a comparative evaluation of five different regression models in wind energy forecasting, identifying XGBoost as the most effective model [40]. A review of similar studies indicates that model development has been undertaken on various datasets, incorporating different meteorological features. The study in [15] proposed the modified Taylor Kriging (MTK) method, which was found to be more accurate than the popular ARIMA method for predicting wind speed direction, with an average improvement of 15.23% in RMSE. In [22], the random forest spatial interpolation (RFSI) method was proposed for precipitation and temperature case studies, showing a prediction time of 6.83 s for 5000 points, compared to 312.12 s with the random forest for spatial prediction (RFsp), and a 25% improvement in RMSE with the RFSI method. Accurate forecasting of wind and weather conditions is crucial for energy production, with studies in [15; 22] significantly contributing to the precise forecasting of wind energy production. Moreover, the advancement of sustainable energy sources requires the development of robust forecasting tools for efficient energy management. Accurate predictions of solar energy production, similar to wind energy, are vital for optimizing output, minimizing costs, and ensuring grid stability. In [41], load forecasts generated using the ProLoaF forecasting tool and the auto-machine learning models and Facebook Prophet were proposed and compared. After designing the ML pipeline, an encoder-decoder RNN model was used to predict the net load under uncertainty, alongside these auto-machine learning models. The results demonstrated that choosing ProLoaF led to more acceptable RMSE values by 12.81% and a faster forecasting time by 38 ms. The role of TinyML and modern ML methods such as BiGRU, BiLSTM, BiRNN, and unidirectional LSTM in intelligent solar forecasting was also examined [38]. The BiLSTM method provided the best RMSE value against other ML methods, particularly being 47.25% better than LSTM and 12.3 ms faster. Studies across daily, monthly, and yearly intervals have underscored the importance of accurately predicting the power output of each wind turbine. Obtaining seasonal characteristics over extended periods is crucial for improving the performance of developed models, as they significantly influence wind energy production forecasting. Concerning latency, emphasis has been placed on the delay experienced in collecting data from wind turbines and farms for centralized processing, which is particularly crucial for achieving hourly power predictions. However, the latency anticipated due to the computational complexity of models developed with extensive training data and higher hyperparameters has not been addressed [42]. It is expected that high-performance ML models will deliver optimal responses without delay, even on low hardware infrastructure.