# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 5 Conclusions  
By employing time series forecasting techniques, this paper provides insights into the patterns, trends, and dependencies associated with diesel-powered thermal systems. Understanding these dynamics is pivotal for energy planners, policymakers, and industry \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Evaluated**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE (\%)**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**Time (s)**} \\  & & & & & & & & \\ \hline \multirow{8}{*}{LSTM} & \multirow{4}{*}{RMSprop} & 10 & \(1.65\times 10^{5}\) & \(1.81\times 10^{2}\) & 7.31 & 0.3576 & 42.21 \\  & & 50 & \(6.06\times 10^{4}\) & \(8.31\times 10^{1}\) & 2.89 & 0.7634 & 27.81 \\  & & 100 & \(5.11\times 10^{4}\) & \(8.21\times 10^{1}\) & 3.11 & 0.8006 & 29.40 \\  & & 500 & \(2.88\times 10^{4}\) & \(6.33\times 10^{1}\) & 2.87 & 0.8876 & 55.55 \\ \cline{2-8}  & \multirow{4}{*}{Adam} & 10 & \(1.22\times 10^{5}\) & \(1.55\times 10^{2}\) & 6.33 & 0.5246 & 27.65 \\  & & 50 & \(3.31\times 10^{4}\) & \(5.29\times 10^{1}\) & 2.07 & 0.8710 & 29.76 \\  & & 100 & \(1.06\times 10^{4}\) & \(2.00\times 10^{1}\) & 0.71 & 0.9586 & 28.72 \\  & & 500 & \(1.75\times 10^{4}\) & \(5.38\times 10^{1}\) & 2.19 & 0.9317 & 56.04 \\ \cline{2-8}  & \multirow{4}{*}{SGDM} & 10 & \(1.25\times 10^{5}\) & \(1.58\times 10^{2}\) & 6.50 & 0.5133 & 25.97 \\  & & 50 & \(5.88\times 10^{4}\) & \(8.03\times 10^{1}\) & 3.06 & 0.7705 & 26.16 \\  & & 100 & \(4.79\times 10^{4}\) & \(6.65\times 10^{1}\) & 2.39 & 0.8131 & 26.72 \\  & & 500 & \(1.26\times 10^{4}\) & \(1.14\times 10^{1}\) & 0.29 & 0.9510 & 53.84 \\ \hline \multirow{8}{*}{ANFIS} & \multirow{4}{*}{FCM} & 10 & \(1.71\times 10^{4}\) & \(6.00\times 10^{1}\) & 2.66 & 0.9334 & **6.11** \\  & & 20 & \(2.59\times 10^{4}\) & \(7.87\times 10^{1}\) & 3.49 & 0.8991 & 11.16 \\ \cline{1-1}  & & 30 & \(1.20\times 10^{7}\) & \(1.53\times 10^{3}\) & \(6.48\times 10^{1}\) & - & 17.15 \\ \cline{1-1}  & & 40 & \(2.85\times 10^{9}\) & \(6.35\times 10^{2}\) & \(2.60\times 10^{1}\) & - & 25.99 \\ \cline{1-1}  & & 50 & \(2.16\times 10^{10}\) & \(5.82\times 10^{3}\) & \(2.51\times 10^{2}\) & - & 37.936 \\ \hline \multicolumn{8}{l}{Bagged} \\ \multicolumn{8}{l}{Model} & - & \multicolumn{1}{c}{-} & **6.36 \(\times\) 10\({}^{3}\)** & **4.04** & **8.95 \(\times\) 10\({}^{-2}\)** & **0.9752** & 15.59 \\ \hline \hline \end{tabular} Best results are in bold.  
\end{table} 
Table 5: Comparison of ensemble bagged method to LSTM model. stakeholders to make informed decisions regarding energy resource allocation, infrastructure development, and environmental sustainability. Considering a MAPE of 0.089% and a coefficient of determination of 97.52% achieved by the bagged ensemble learning model (average considering 50 experiments), besides its robustness proved by statistical evaluation, this model is shown to be promising for short-term time series forecasting. By having more precise predictions about the use of thermal systems, it is possible to improve the management of the feedstock to be used for burning and generating electricity. This can prevent a shortage of the necessary inputs, resulting in a price spike in this type of generation. This research lays the groundwork for informed decision-making, emphasizing the importance of balancing energy needs, environmental considerations, and long-term sustainability in Brazil's energy landscape. Stakeholders can steer the nation towards a more resilient, diversified, and environmentally conscious energy future. Further research can be undertaken, including an evaluation of the relations between different regions since when there is a lack of rain and the hydropower dams have lower levels, generation using thermal systems is used in all regions of the country. For time series with higher noise, filters with higher intensity may be a solution that can be applied to related tasks.