# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 1 Introduction
Traditional diesel engine power plants, compared to those based on new concepts, such as gasification with the Rankine cycle from biomass forest, are still cheaper to implement [15]. The new Rankine-gasification power plants have 1.2 times higher cost  
Figure 1: Diesel oil for electricity generation by Amazon legal region states (GWh). Source: Brazilian Energy Balance, Ministry of Mines and Energy of Brazil. for implementation than diesel thermoelectric plants currently used despite heat wasted through exhaust gases in the general diesel operation model [16]. However, the annual operation and maintenance costs using diesel have doubled in price compared with biomass use based on research performed in an isolated village aiming for decarbonization. When natural gas pipelines are implemented, this could reduce costs by forty percent and carbon dioxide emissions by twenty-five percent in Amazonia isolated systems [17]. Fossil fuel load forecasting is important for the adaptability and stability of regions that have high dependence as evaluated by [PERSON] et al. [18], who applied diversified ensemble learning methods to reduce delayed dispatch and provide strategically scheduled storage for natural gas. These methods can enhance hybrid generation using fossil fuels and renewable resources by integrating photovoltaic and diesel engines to reduce expenditure. The algorithms of [PERSON] et al. [19] utilize improved strategies for storage, decreasing the daily carbon emission costs of a power plant by 29%, and have the potential for application in isolated regions of the legal Amazon by avoiding battery degradation. Evaluating the consumption management and generation and adoption of technologies that relocate electrical load is a solution to increase energy sustainability [20]. Within this context, forecasting time series using ensemble learning approaches is an alternative to improve the reliability of the electrical system to achieve a better environmental-economic coefficient [21]. Ensemble learning is the fusion of several predictive models to enhance collective performance [22]. It starts by assembling a varied array of foundational forecasting models, like decision trees, support vector machines, support vector regressors, and others. Each of these base models offers distinct perspectives on the data, which the ensemble method consolidates [23]. Specifically, for generation forecasting, ensemble learning leverages diverse algorithmic strengths to attain heightened accuracy and resilience in predictions [24]. There is a trend towards using deep learning models, given their ability to comprehend non-linear patterns [25]. However, these models can require more computational effort and are not always the best alternative. Considering the promising results of using ensemble models, this paper aims to present a comparison between ensemble learning methods and models based on deep learning, such as the long short-term memory (LSTM) model. Considering that the use of thermal energy results in higher energy costs for consumers [26], especially when there is a shortage of raw materials, forecasting its use can help with energy planning. Based on this premise, the main theme of this paper is the forecasting of thermal systems dispatch. The analysis focuses on the legal Amazon since new energy sources are being explored and implementation of several insulated systems in this region and their more efficient use can improve energy supply capacity. Given the major importance of thermal systems generation in northern Brazil, this paper proposes a thorough evaluation of ensemble learning methods for dispatch forecast for thermal systems in this region. The main contributions of this paper are:  
* The time series forecasting of thermal dispatch is an indicator that can be used for evaluating energy prices, which is an important concern in insulated thermal systems since the prices are related.