Distribution level electric current consumption and meteorological data set of the east region of Paraguay

Velázquez, Gustavo; Morales, Félix; García-Torres, Miguel; Gómez-Vela, Francisco; Divina, Federico; Noguera, José Luis Vázquez; Daumas-Ladouce, Federico; Ayala, Carlos Sauer; Pinto-Roa, Diego P.; Gardel-Sotomayor, Pedro E.; Román, Julio César Mello

doi:10.1016/j.dib.2021.107699

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…The data set, named "Electricity consumption and meteorological data of Alto Paraná, Paraguay", is freely and publicly available at [12].…”

Section: Datamentioning

confidence: 99%

“…The data that support the findings of this study are openly available in mendeley at https://data.mendeley.com/datasets/hzfwzzsk8f/4, accessed on 6 January 2022, doi:10.17632/hzfwzzsk8f.4. More information about the data is available at [12].…”

Section: Data Availability Statementmentioning

confidence: 99%

“…The data corresponded to the eastern region of the country and were recorded from January 2017 to December 2020, with measurements recorded every hour and a half. It is important to remark that these data were obtained and made public as part of the same research protect that made this paper possible [12]. In order to be used, the raw data were processed to obtain the following four data sets applicable to the clustering analysis:…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

et al. 2022

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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.

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“…The data set, named "Electricity consumption and meteorological data of Alto Paraná, Paraguay", is freely and publicly available at [12].…”

Section: Datamentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

et al. 2022

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Machine learning for electric energy consumption forecasting: Application to the Paraguayan system

Morales-Mareco,

García-Torres,

Divina

et al. 2024

Logic Journal of the IGPL

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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.

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A Novel Short Electric Load Forecasting Approach Using a Multivariate Transformer Neural Network and CEEMDAN

Elsayed,

Eladany,

Elfar

et al. 2023

2023 24th International Middle East Power System Conference (MEPCON)

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Distribution level electric current consumption and meteorological data set of the east region of Paraguay

Cited by 3 publications

References 6 publications

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Machine learning for electric energy consumption forecasting: Application to the Paraguayan system

A Novel Short Electric Load Forecasting Approach Using a Multivariate Transformer Neural Network and CEEMDAN

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