# Enhancing Weather Forecasting Integrating LSTM and GA
## 5 Conclusions and Future Work  
Accurate weather forecasts are crucial for decision making and planning in various sectors. Understanding weather patterns is essential for making informed decisions, optimizing operations, and mitigating risks in industries such as agriculture and renewable energy.  
Solar radiation and temperature are more stable compared to wind speed, which can be unpredictable. Our numerical results show that regression models are better at estimating average temperature than wind-related variables, highlighting the reliability of these weather elements.  
When it comes to LSTM predictions, the results are generally smoother and less variable compared to the input data. This is due to LSTM's ability to capture and learn long-term dependencies within a sequence of data using memory cells and gates to store past information and selectively forget or update it as new data are introduced. Finally, using a GA method to optimize the LSTM hyperparameters minimized the evaluation metrics, especially the MSE value, improving the accuracy of the LSTM in forecasting weather variables.  
In LSTM predictions, the results are usually smoother and have less variation compared to the input data. This is because LSTM can capture and learn long-term dependencies in a sequence of data by using memory cells and gates to store past information while selectively forgetting or updating it as new data are introduced. Additionally, using a GA approach to fine-tune LSTM hyperparameters led to a reduction in evaluation metrics, especially the MSE value, thereby improving the accuracy of the LSTM in forecasting weather variables.  
The evaluation of the metrics revealed promising results. The LSTM-based models consistently provided accurate and precise weather forecasts, regardless of the time horizon considered. This underscores the effectiveness and reliability of LSTM technology in weather forecasting.  
The quality and quantity of available data posed a significant challenge in this work. Techniques for data augmentation and improvement may be explored in future studies.  
On the other hand, the computational cost of training the models emerged as a critical consideration. Investigating more efficient training algorithms, transfer learning, or utilizing distributed computing resources could be valuable work for future research.  
Conceptualization, R.T., A.C., E.J.S.P. and J.B.; methodology, R.T., A.C., E.J.S.P. and J.B.; software, R.T., A.C., E.J.S.P. and J.B.; validation, R.T., A.C., E.J.S.P. and J.B.; formal analysis, R.T., A.C., E.J.S.P. and J.B.; investigation, R.T., A.C., E.J.S.P. and J.B.; resources, R.T., A.C., E.J.S.P. and J.B.; data curation, R.T., A.C., E.J.S.P. and J.B.; writing--original draft preparation, R.T., A.C., E.J.S.P. and J.B.; visualizing, R.T., A.C., E.J.S.P. and J.B.; supervision, R.T., A.C., E.J.S.P. and J.B.; project administration, R.T., A.C., E.J.S.P. and J.B.; funding acquisition, R.T., A.C., E.J.S.P. and J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. Data are contained within the article.  
This research was developed in line with the Vine and Wine Portugal--Driving Sustainable Growth Through Smart Innovation Mobilizing Agenda (ID: C644866286-011).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DL & Deep learning \\ GA & Genetic algorithm \\ IPMA & Portuguese Institute for Sea and Atmosphere \\ LASSO & Least absolute shrinkage and selection operator \\ LSTM & Long short-term memory \\ ML & Machine learning \\ MAE & Mean absolute error \\ MSE & Mean square error \\ MLR & Multiple linear regression \\ RMSE & Root mean square error \\ ReLU & Rectified linear unit \\ XGBoost & Extreme gradient boosting \\ \end{tabular}