Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study
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-pr...
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| Veröffentlicht in: | Electronics (Basel) 2022-01, Vol.11 (2), p.267 |
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| creator | Morales, Félix García-Torres, Miguel Velázquez, Gustavo Daumas-Ladouce, Federico Gardel-Sotomayor, Pedro E. Gómez-Vela, Francisco Divina, Federico Vázquez Noguera, José Luis Sauer Ayala, Carlos Pinto-Roa, Diego P. Mello-Román, Julio César Becerra-Alonso, David |
| description | 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. |
| doi_str_mv | 10.3390/electronics11020267 |
| format | Article |
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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.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics11020267</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Artificial intelligence ; Case studies ; Clustering ; Data processing ; Datasets ; Electricity ; Energy consumption ; Feature extraction ; Feeders ; Genetic algorithms ; Machine learning ; Neural networks ; Support vector machines ; Time series ; Wavelet transforms</subject><ispartof>Electronics (Basel), 2022-01, Vol.11 (2), p.267</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Case studies</subject><subject>Clustering</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Electricity</subject><subject>Energy consumption</subject><subject>Feature extraction</subject><subject>Feeders</subject><subject>Genetic algorithms</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Support vector machines</subject><subject>Time series</subject><subject>Wavelet transforms</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUMtOwzAQtBBIVKVfwMUS58DGTu2YWxSVh1REJeg5chy7uKR2sJND_p6UcuDAXnY1mh3NDELXKdxSKuBOt1r1wTurYpoCAcL4GZoR4CIRRJDzP_clWsS4h2lESnMKM_RZONmO0UbsDV79KFmFV06H3YhL7-Jw6HrrHd4Eb2yrI95G63ZY4hepPqzTeK1lcEeo6LrgJ_AeF3gjg9wNcpQOlzJq_NYPzXiFLoxso1787jnaPqzey6dk_fr4XBbrRFFC-oRneZ7VDRhGG95kAjRhGWQ5p2bZyFooqVgmWE2XYIhQFJjmzAgCKUw_oqZzdHPSnfx8DTr21d4PYcoZK8JISjjPczax6Imlgo8xaFN1wR5kGKsUqmOx1T_F0m8WM25N</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Morales, Félix</creator><creator>García-Torres, Miguel</creator><creator>Velázquez, Gustavo</creator><creator>Daumas-Ladouce, Federico</creator><creator>Gardel-Sotomayor, Pedro E.</creator><creator>Gómez-Vela, Francisco</creator><creator>Divina, Federico</creator><creator>Vázquez Noguera, José Luis</creator><creator>Sauer Ayala, Carlos</creator><creator>Pinto-Roa, Diego P.</creator><creator>Mello-Román, Julio César</creator><creator>Becerra-Alonso, David</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-5174-7743</orcidid><orcidid>https://orcid.org/0000-0003-3161-8383</orcidid><orcidid>https://orcid.org/0000-0003-2479-9876</orcidid><orcidid>https://orcid.org/0000-0001-8891-7208</orcidid><orcidid>https://orcid.org/0000-0002-0964-9506</orcidid><orcidid>https://orcid.org/0000-0002-6867-7080</orcidid><orcidid>https://orcid.org/0000-0001-9850-9937</orcidid><orcidid>https://orcid.org/0000-0001-6578-2769</orcidid><orcidid>https://orcid.org/0000-0001-7376-5790</orcidid><orcidid>https://orcid.org/0000-0002-3698-4043</orcidid><orcidid>https://orcid.org/0000-0001-5441-5736</orcidid><orcidid>https://orcid.org/0000-0002-9766-4182</orcidid></search><sort><creationdate>20220101</creationdate><title>Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study</title><author>Morales, Félix ; 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| subjects | Algorithms Artificial intelligence Case studies Clustering Data processing Datasets Electricity Energy consumption Feature extraction Feeders Genetic algorithms Machine learning Neural networks Support vector machines Time series Wavelet transforms |
| title | Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study |
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