## 1 Introduction  
Energy demand forecasting plays a key role in ensuring the efficient allocation and management of resources within the energy sector. It also helps achieve sustainable development goals, particularly Sustainable Development Goal 7 (SDG 7) and its targets [1]. The SDG7 incorporates a number of targets, including the following: ensuring universal access to affordable, reliable, and modern energy services (SDG 7.1), increasing the share of renewable energy in the global energy mix (SDG 7.2), doubling the global rate of improvement in energy efficiency (SDG 7.3), encouraging international cooperation for clean energy research and technology (SDG 7.4), and improving the infrastructure for sustainable energy services in developing countries (SDG 7.5). To this extent, Table 1 demonstrates the actions taken by Brazil to reach these targets. Accurate predictions about the international energy outlook are essential to enable utilities, policymakers, and stakeholders to make informed decisions that support these targets. Long-term trends, such as socioeconomic factors and technological advances, influence energy consumption patterns over extended periods and are aligned with SDG7's goal of increasing access to modern energy services (SDG 7.1) and enhancing energy efficiency (SDG 7.3). Short-term seasonal factors, including variations in weather conditions and industrial production cycles, also affect energy demand fluctuations. These are fundamental considerations when seeking to achieve the targets of SDG7, particularly with regard to the integration of renewable energy sources (SDG 7.2) and infrastructural development (SDG 7.5). Furthermore, unexpected events such as economic fluctuations and policy changes highlight the importance of robust forecasting methods to support international cooperation in clean energy research and technology (SDG 7.4). By selecting appropriate forecasting techniques, utility companies and researchers can contribute to global efforts to enhance access to clean energy and create a sustainable infrastructure for renewable energy (SDG 7.A, 7.B). Accurate knowledge of energy consumption enables cities to formulate effective strategies for energy management, advancing the development of smart cities [2]. By analyzing consumption patterns, authorities can identify peak demand periods, allocate resources efficiently, implement demand response programs to alleviate strain on the energy grid, and develop sustainable urban planning initiatives. This approach may also be used in data centers and cloud environments, leading to improved energy efficiency and reduced operational costs [3]. As the global population grows, urbanizes, and industrializes, the demand for electricity rises, introducing uncertainty regarding future consumption patterns [4]. Remarkably, this has been a global concern with the recent growth in the use of electric vehicles [5]. This brings challenges to energy planners and utility providers to accurately predict and provide future energy needs. This is especially true for developing countries [6], such as Brazil. Due to the relevance of this field, researchers have focused on time series models for forecasting electricity consumption in Brazil [7; 8; 9; 10; 11; 12]. None of them, however, forecast the general energy consumption in Brazil to the time frame proposed in this work, for up to 2033. They are either sector-specific, such as industrial or residential demand, or their forecasting does not cover the temporal scope of this work. Hence, this study addresses these limitations by long-term forecasting Brazil's energy consumption, thereby contributing to Brazilian policymakers and global energy research. The primary objective of this research is to compare the performance of various forecasting models in predicting energy demand in Brazil while seeking to achieve the targets of SDG7 by finding innovative ways of improving energy demand forecasting methods and forecasting energy consumption in Brazil for up to 2033.