## 2 Literature Review  
The significance of devising a sustainable energy plan and the emphasis on forecasting energy demand highlights the importance of managing energy demand--the necessity to align with the long-term objectives of ecological, societal, economic, and industrial sustainability goals [22]. By combining energy demand forecasting with sustainable energy planning, societies can progress toward a future that is both sustainable and equitable. Therefore, energy demand forecasting is critical to energy planning and management, enabling stakeholders to make informed decisions regarding resource allocation, infrastructure development, and policy formulation. Energy demand forecasting methods can be broadly categorized into qualitative and quantitative approaches. As documented by [23], within quantitative methods, time-series models are widely used for energy de mand forecasting because they capture the temporal patterns and trends inherent in energy consumption data. Although this study focuses on energy demand, forecasting models have also been used for energy production, such as from wind power [24], including the use of state-of-the-art approaches, such as TFT [25; 26]. Several studies have compared the performance of time-series models in different contexts and for various applications. Ref. [27] conducted a comprehensive review of forecasting methods, including classical and modern approaches. Similarly, ref. [28] used a Nonlinear Autoregressive (NAR) neural network to forecast the next decade of energy demand based on a publicly available data set for global energy consumption. Apart from energy, SARIMA, for example, has also been used for predicting traffic flow [29], and network traffic generated from an agricultural environment [30]. Energy demand forecasting has been a significant research area, with various methods proposed for accurate predictions. Neural networks, such as Nonlinear Autoregressive neural networks, can effectively handle statistical, empirical, and theoretical problems, as presented by [31]. Convolution Neural Networks (CNNs) and Conditional Random Fields (CRFs) have been used to forecast power consumption, achieving high accuracy, according to [32]. Electricity price forecasting is also relevant in this context [33]. Developing countries face challenges in achieving low-carbon and sustainable economic growth. Still, for [23], artificial neural networks (ANNs) hybridized with meta-heuristic techniques are superior in load forecasting. Probabilistic forecasting models, such as DeepAR and Deep State Space (DeepState), perform better for longer prediction horizons, as conducted by [27]. Machine learning (ML) techniques are widely used, particularly for short-term electricity forecasting, while engineering-based models cover long temporal horizons and appliance consumption, according to [34]. In energy generation, forecasting models have also been used for predicting parameters of electrical energy production, such as wind speed [35; 36]. Existing studies on energy demand forecasting methods in Brazil have explored various models and approaches. The Holt-Winters method, Seasonal ARIMA model, dynamic linear model, and autoregressive neural network model have been compared for forecasting electric energy consumption in the Brazilian industrial sector [37]. Regression models and recurrent neural networks have been evaluated for predicting electricity consumption in the northern region of Brazil, with the linear regression model using moving average and the recurrent neural network model performing well [38]. Time-series forecasting models such as Holt-Winters, SARIMA, dynamic linear model, TBATS, neural network autoregression, and multilayer perceptron have been applied to forecast industrial electricity consumption in Brazil, with the multilayer perceptron model showing the best performance [10].