This study aimed to develop accurate short-term air quality forecasting models for Asunción, Paraguay using machine learning algorithms. The dataset, which consisted of PM concentrations, air quality indices, meteorological variables, and traffic information, spanned a duration of 18 months. Two machine learning algorithms, Support Vector Regression (SVR) and XGBoost, implemented for window-based regression, were optimized using a grid search and Bayesian optimization process. The optimization explored the impact of the look-back window$(w)$, sampling frequency$(f)$, and prediction horizon$(h)$on model performance. The models were evaluated through a hold-out scheme and trained and evaluated for all stations in the network. Results showed that the XGBoost model outperformed the SVR model, with a mean absolute error of 2.774, 4.643, and 7.819 for AQI 2.5 predictions using 6, 12, and 24-hour horizons, respectively. The optimization process significantly improved the performance of both models compared to using default settings. The study's findings have implications for public health decisions and could be used to issue alerts or advisories when air pollution levels are expected to be high.

Short-term electricity demand forecasting is a fundamental part of the decision-making process of entities involved in electricity consumption management, since it allows the development of strategies to deal with variations in electricity demand in short periods of time. Developing a highly-accurate predictive model is necessary to understand and reflect the consumption behavior, as well as to adjust the generation program essentially to meet the demand at each moment. Therefore, a performance comparison has been made between statistical, machine learning and deep learning models for short-term forecasting. The deep learning models are based on recurrent neural networks, incorporating attention mechanisms in some of them. Hyperparameter tuning was also applied using a Bayesian optimization algorithm. A dataset was also developed including historical electricity demand and external factors such as weather and calendar variables recorded in Paraguay from 2009 to 2022. Models were evaluated from a set of numerical experiments using classical error metrics such as: mean squared error, root mean square error, mean absolute error and mean percentage absolute error. In addition, new special measures were introduced to analyze the error in this type of applications: the percentage error at peak hour and the maximum percentage error of the day to analyze the error in certain events.