## 5 Conclusions  
In this study, future forecasting of the monthly measured wind speed parameter in the Bodo region was conducted using a series of machine learning algorithms, including LSTM, SVM, and ANN, across five different models. To enhance model performance, WT was also applied. The most significant findings from this study are as follows:  
* Based on statistical analysis and visual comparison results, the most successful algorithm was LSTM with WT, and the most successful model was M04. This model's input structure should be used for wind speed forecasting in this region. Additionally, the input variables should be kept at an optimal level. The input structure for this model was created using lagged data at t-1, t-2, t-11, and t-12. * In the analysis performed without WT, the most successful algorithm was SARIMA-MR1. Therefore, stochastic methods should be preferred in analyses that do not incorporate WT. * In the ANN analyses using WT, negative values were found in all models, indicating that WT had an adverse effect on ANN. Consequently, ANN should not be used with WT for wind speed prediction models in this region. * In SVM analysis incorporating WT, the M05 model yielded the most successful results. However, in terms of performance, this model still lagged behind LSTM models with WT. Nevertheless, WT led to performance improvements in all SVM models. * Among machine learning methods, the best results were obtained with LSTM in model M04. Compared to other models, the input structure of M04 is the most suitable for wind speed prediction in this region. * In LSTM analyses with WT, the most successful model was M04, while the least successful was M01. The input structure of M01 consisted of a single lagged t-12 wind data point. The results indicate that model performance varies depending on both the algorithm and the input structure. Therefore, selecting the appropriate algorithm and optimizing the input structure are critical for achieving accurate forecasts. * In SARIMA analyses, the most successful model parameters were found in MR1. If stochastic methods are used for wind speed forecasting in this region, the MR1 model parameters should be applied. These conclusions provide valuable insights, particularly for ongoing climate change adaptation efforts and wind-based energy production in Norway. These findings contribute to a better understanding of energy management, energy-related risks, and climate change impacts. Additionally, they offer guidance for institutions involved in environmental management and policymaking. Given Norway's vulnerability to climate change, this study offers several directions for future research. Its findings may support stakeholders involved in energy production, environmental planning, and decision-making processes related to these areas. In the future projects, risk assessments, or energy production planning, the findings of this study should be considered as a valuable reference as Norway continues to expand its renewable energy infrastructure. Conceptualization was conducted by [PERSON] and [PERSON]; methodology was conducted by [PERSON], [PERSON] and [PERSON]; writing--review and editing--was conducted by [PERSON], [PERSON] and [PERSON]; formal analyses were conducted by [PERSON]; visualization was conducted by [PERSON], [PERSON] and [PERSON]; and supervision was conducted by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. APC is supported by UiT the Arctic University of Norway. Not applicable. The original contributions presented in the study are included in the article. The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request. We would like to thank the Norwegian Centre for Climate Services (NCCS) for the open-access data they provide to support research.