Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

PytuTester is an open-source ventilator tester developed to help bio-engineers in the design and verification of new ventilator prototypes. A ventilator tester allows measuring the flow, pressure, volume, and oxygen concentration provided to the patient. During the global pandemic COVID-19, several open-source ventilators prototypes were developed; however, due to high cost and demand testers, they were not available. In this context, a low-cost tester was developed using a Raspberry Pi and medical-grade sensors for the test ventilators prototypes. This paper presents the design files, software interface, and validations tests. Our results indicate that the tester has good accuracy to evaluate the efficacy and performance of new prototypes. When tested on two ventilator designs developed in Paraguay, PytuTester reported flow profiles that were concordant with the industry-standard VT650 Gas Flow Analyzer. PytuTester was then field deployed to test several DIY ventilator designs in low-resource areas.

In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions.

Short-term electricity demand forecasting represents a fundamental tool for decision-making by entities engaged in electricity management since it allows the development of strategies to meet variations in electricity demand in short periods. The accuracy of predictive models is an important factor for energy operations and the scheduling of energy generation sources to meet the demand at each instant. Intelligent models based on Recurrent Neural Networks (RNN) require hyperparameter adjustment. These models have several hyperparameters that substantially affect their performance. Our paper implements a Long-Short Term Memory (LSTM) model and four search methods to adjust its hyperparameters. First, we select the length of historical window and the hidden state size of LSTM cells for optimization. Second, we draw comparisons between the grid search, random search, a Bayesian scheme, and a genetic algorithm. The data set used for training and validation of the model includes hourly electricity consumption and meteorological variables recorded in Paraguay from 2015 to 2021. The proposed model was evaluated through numerical experiments with classical error measures such as the root mean square error (RMSE), the correlation, the runtime, and the mean absolute percentage error (MAPE). Our comparative study shows that grid search and genetic algorithm give the optimal hyperparameters with high validation accuracy on the test dataset. However, it is important to note that grid search may require much more evaluations and computational resources.

This article presents a comprehensive dataset integrating hourly electricity demand data from Paraguay's National Interconnected System (SIN) with meteorological records from 2008 to 2022. Electricity demand data were obtained from the National Electricity Administration (ANDE), while meteorological data were sourced from the Global Data Assimilation System (GDAS).