# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 2 Related Work  
The use of artificial intelligence to analyze electrical power systems is increasing due to the extent to which these models have to deal with non-linearities [27]. Nowadays, deep learning has been widely applied in several fields [28; 29; 30; 31]. Regarding power systems [32], its application can focus on the identification of faults in the power grid based on classification [33] or the prediction of its condition based on time series analysis [34]. Given the advantages of having earlier knowledge about a system's condition, time series forecasting of energy is a way to improve decision-making in electrical power management [35]. In the work by [PERSON] et al. [36], a review is presented about solar irradiance measurement instrumentation and power generation forecasting over the past five years, specifically focusing on the integration of artificial neural networks (ANNs). They discuss the pivotal role of ANNs in enhancing the accuracy of solar power forecasts, addressing their capabilities, limitations, and areas for further improvement. The synthesis of five years of research provides insights into the progress, emerging trends, and future directions in harnessing solar energy through instrumentation and predictive modeling, contributing to a broader understanding and development of renewable energy systems. [PERSON] and [PERSON]'s research [37] focuses on forecasting models applied to various renewable energy sources, including solar, wind, hydroelectric, and other emerging technologies. It involves a targeted analysis of ANN-based models and their practical implications in optimizing the integration and management of renewable energy systems. Their work proposes new advances in power system forecasting for greater efficiency and stability in energy systems. Focused on improving the supply of electricity, [PERSON] et al. [38] presented a study on the application of convolutional neural network (CNN)-based models for classifying faults in electrical insulators, an approach that was also applied in [39] through a hybrid approach that combines several models. In [40], CNNs are combined with LSTM for fault detection in electrical machines. The CNN-LSTM can also be used for time series forecasting [41]. In [42], an interpretable method is proposed to improve fault classification. In [43], an object detection method based on deep learning is combined with the Quasi-ProtoPNet to have an improved architecture to classify faults in the power grid, enhancing the reliability of the electrical power system. Deep learning strategies have been successfully applied to time series forecasting [44]. In [45], ANNs are applied for forecasting electricity generation, and in the work of [PERSON] et al. [46], the landscape of forecasting photovoltaic power generation through machine learning and metaheuristic techniques is analyzed. They highlight the effectiveness, adaptability, and areas for improvement in machine learning and metaheuristic techniques, thus providing practical applications in optimizing the efficiency of photovoltaic energy systems. [PERSON] et al. [47] presented a review of five-year wind generation forecasting methodologies, focusing on ANN applications. In their paper, they explore the evolution and limitations of ANNs for energy forecasting, providing ideas on possible implementations of these models, and highlighting their potential for renewable energy forecasting. Regarding hybrid methods, many authors are applying filters combined with forecasting models to enhance their capacities and generalization. For instance, in [48], the Hodrick-Prescott filter, and in [49; 50; 51], the wavelet transform, are applied for denoising. All these studies proved that the use of filters in pre-processing the time series is a promising strategy when trend forecasting is the major goal of the analysis. [PERSON] et al. [52] explore the application of optimized hybrid ensemble learning approaches in the context of very short-term load forecasting.