# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 1 Introduction  
Electricity generation and its costs in an isolated system off-grid are mostly related to fuel engines with diesel or natural gas consumption [1], mainly because these energy types are easily transported and they are non-intermittent energy sources in contrast to solar or wind power plants which can be integrated [2]. It is challenging to achieve accurate generation prediction of these cited renewable sources [3], while fossil fuel has significant disadvantages as it generates pollutant emissions and there is a need to refine the oil that is imported in part from foreign countries in the thermal plants of the Brazilian Amazon Region [4]. The interdependence between the nexus of greenhouse emissions, economic growth, financial development, and load demand-supply in the past with the future is important in managing policy recommendations and/or establishing strategies for the corporate sector to adopt business operations with better support technologies to improve profit with ecological efficiency [5]. Determining scenarios that could improve the reduction in carbon dioxide or coordinate carbon capture systems to reach targets considering uncertainties and promoting the best possible adaptability involves following the convergence results of these four parameters [6]. The extraction, conversion, and distribution of crude oil as diesel form part of a logistic value energy chain that is complex, and the implementation necessary for electricity generation in the power grid for a domestic load to supply isolated regions from this resource brings higher costs for the electrical system in a short time period [7]. In trying to avoid these disadvantages, the integration of renewable energy sources by applying an optimal dispatch algorithm can be evaluated and assist responsible companies in controlling greenhouse emissions from fossil fuels as well as reducing import dependence. To avoid these disadvantages, modeling the load capacity forecast is essential to plan the energy requirements of the analyzed region and the grid modifications needed [8]. Economic dispatch is the process of determining the optimal combination of power output from different generation units to meet the electrical load demand at the lowest cost while satisfying various system and operational constraints [9]. The key difference between the low-carbon form of economic dispatch and the common (or traditional) form lies in prioritizing environmental concerns, particularly carbon emissions, in the decision-making process [10]. While the traditional economic dispatch aims at cost minimization based on fuel and operational costs, the low-carbon form integrates environmental considerations, specifically targeting a reduction in carbon emissions, thus aligning the dispatch process with broader environmental and sustainability goals [11]. Low-carbon economic dispatch depends on energy conversion appliances which affect the exchange power and thermal load, storage, and carbon trading and require to be integrated with the demand response and scenarios of the predicted future load [12]. These parameters can help to determine losses and increase investment mechanisms to reduce costs and increase the profit-aggregating influence factors for carbon intensity, benefiting from the scale of technologies that could contribute to reaching lower emission targets in future scenarios [13]. A major concern about sustainability in the Amazon region is related to the use of diesel oil as the main source of energy. Figure 1 shows the used diesel oil for electricity generation in the legal Amazon by region states in 2021. According to this information, the further away from the industrialized center, the more fossil fuel consumption is observed. This is concerning since these regions are where there are the largest areas of preserved vegetation and the presence of large thermoelectric plants increases the level of pollution in the area, thus affecting the local ecosystem [14].