The concept of sustainability, with a focus on energy, has emerged as a central tenet in addressing the mounting global challenges of environmental degradation and resource depletion. Indicators of sustainability focusing on energy are crucial tools used to assess and monitor progress toward achieving a more sustainable energy system. These indicators provide valuable insights into the environmental, social, and economic dimensions of energy practices and their long-term impacts. By analyzing and understanding these indicators, policymakers, businesses, and communities can make informed decisions, formulate effective policies, and steer their efforts toward a more sustainable energy future. These indicators serve as navigational guides, steering the world toward energy practices that support both present needs and the well-being of future generations. In this paper, the concept of sustainability and measurement indexes used are reviewed, focusing on energy factors. The focus of the discussion presented here is related to an assessment of the possibilities for improving energy efficiency and evaluating the indicators that are used to measure whether the desired levels of sustainability are being achieved.

Electricity load forecasting is important to planning the decision-making regarding the use of energy resources, in which the power system must be operated to guarantee the supply of electricity in the future at the lowest possible price. With the rise of the ability of forecasting based on deep learning, these approaches are promising in this context. Considering the attention mechanism promising to capture long-range dependencies, it is highly recommended for sequential data processing, where time series-related tasks stand out. Considering a sequence-to-sequence (Seq2Seq) time series data of the electricity load in Brazil, this paper proposes the use of the long short-term memory (LSTM) with the attention mechanism to perform the time series forecasting. The proposed Seq2Seq-LSTM with attention mechanism outperforms other well-established models. Having a mean absolute error equal to 0.3027 the proposed method is shown to be promising for field applications. The proposed method contributes to time series forecasting by implementing an attention mechanism to Seq2Seq data, therefore, more than one correlated signal can be used to perform the prediction enhancing its capacity when more data is avaliable.

The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

The scheduling of the operation of the electricity system in Brazil is based on multi‐criteria optimization that takes into account the forecast of the level of the dams of the hydroelectric plants, this variation is evaluated by the soil moisture active passive model. Considering the advances in using deep learning to forecast time series variations, this paper proposes a hybrid method for forecasting dam level variations. In particular, the temporal fusion transformer (TFT) is used for prediction with the Hodrick–Prescott filter for denoising. To enhance the model's performance, its hyperparameters are optimized by the Optuna framework based on the tree‐structured Parzen estimator. For benchmarking, the multilayer perceptron, long short‐term memory, recurrent neural network (RNN), Dilated RNN, temporal convolutional neural, neural hierarchical interpolation for time series forecasting, deep non‐parametric time series forecaster, and the standard TFT are considered. The results show that the proposed model can make predictions with high performance compared to other methods, being 29.12% better than the second‐best model, and 59.22% better than the original TFT model for very short‐term forecasting, making it a promising alternative to be used as additional information for planning the operation of the electrical power system.