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

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

doi:10.3390/electronics11020267

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…They have disadvantages such as the amount of Table 2 shows the advantages or disadvantages of unsupervised learning algorithms. Clustering algorithms consist of k-means [19,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45], DBSCAN [21], hierarchical clustering [46][47][48], and spectral clustering [49][50][51], and provide a simple structure, widely used methodology, and diversity according to various needs. However, they have disadvantages, such as spherical cluster assumption, sensitivity to scale, and difficulty processing complex data structures.…”

Section: Unsupervised Learningmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.

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Section: Unsupervised Learningmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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“…In order to create the clustered data, they used k-means and hierarchical agglomerative clustering with all five linkages, along with validation using the Silhouette, Calinski-Harabasz, and Davies-Bouldin indices. However, this method is not suitable for all features and algorithms, so they planned to incorporate additional new sets of new algorithms for improvisation (20) . The study highlighted the usefulness of employing the Davies-Bouldin index to assess cluster centres when applying the x-means algorithm to particular sets of iris datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Cluster Predictive Model Using Affinity Propagation Algorithm to Group Mushroom 5.8s rRNA Sequences

Sudhasini¹,

Ashadevi²

2022

IJST

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Background:The main emphasis of the article is biological information on a distinct species of mushroom (Phylum Basidiomycota) data collection of 5.8s rRNA sequences. Macrofungi from the phylum Basidiomycota are predominantly used as therapeutic mushrooms in several countries. During the rainy season, hundreds of macrofungal basidiocarps were discovered in Tamilnadu. The internal transcribed spacer (ITS) and 5.8S rRNA gene sequence markers, which have been collected from NCBI, were used to isolate at least thirty of these strains that fall under the Basidiomycota kingdom (suborders of Polyporales, Hymenochataeles, and Russuales), which have the therapeutic properties of the Basidiomycota kingdom. Objectives: This article's main objective is to organise the sequences according to similarity utilising multiple sequence alignment and an algorithmic perspective. Methods: In this paper, we use 30 × 30 pairwise similarity matrix data of these thirty 5.8s rRNA mushroom sequences obtained using the clustal omega tool to develop an affinity propagation approach. As a continuation of earlier work, this will be evaluated against k-means, hierarchical clustering based on the ideal cluster, and time and space complexity. Findings: The affinity propagation algorithm typically discourages providing the initial number of clusters; therefore, the optimal number of cluster values and grouping of clustered results obtained from the affinity propagation algorithm are also the same as the results obtained from the previous existing research work using the kmeans, hierarchical agglomerative clustering algorithm. Novelty: The overall suggested technique involves applying the cluster validation metrics Silhouette score, Calinski-Harabasz Index, and Davies-Bouldin Index methodologies to find the ideal number of clusters. The CD-hit Clustering tool does not offer these metrics, and the Cluster Omega tool does not support this kind of extension work. This follow-up work assists bioinformatics researchers in obtaining favourable results by utilising the existing software prior to working in wet laboratories; rather than wasting a lot of chemical resources, this result https://www.indjst.org/ 2129

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“…Del conjunto de 1179 medidores inteligentes, obtuvieron dos clústers, uno de alto consumo, y otro de consumo menor. De la misma forma, Morales et al (2022) utilizan dos algoritmos de agrupamiento (K-Means y Agrupamiento Jerárquico) para identificar diferentes perfiles de consumidores de energía eléctrica, utilizando datos de consumo de energía eléctrica de la región oriental del Paraguay almacenados entre enero del 2017 y diciembre del 2020. Por otra parte, Rajabi et al (2019) desarrollaron un estudio de comparación de técnicas de agrupamiento para segmentación de patrones de carga eléctrica utilizando datos provenientes de medidores inteligentes manejados por la Comisión de Regulación de Energía de Irlanda.…”

Section: Introductionunclassified

Uso de algoritmos de Machine Learning para analizar los datos de energía eléctrica facturada en la Ciudad de Buenos Aires durante el período 2010–2021

Ramírez

2022

cienaplic

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Con las capacidades actuales de las computadoras, los algoritmos de Machine Learning se implementan con facilidad en distintas áreas de interés. En el presente estudio se utilizan métodos de Machine Learning para analizar los datos de energía eléctrica facturada mensualmente en la Ciudad de Buenos Aires, durante el período 2010-2021. Los objetivos son: determinar patrones en los datos utilizando el algoritmo K-Means y determinar las variables que más impactan la energía facturada total a través del uso del algoritmo de Regresión Lineal. Como técnica de reducción de la dimensionalidad se utilizó el análisis de componentes principales. La investigación fue de tipo cuantitativa-explicativa, utilizando los datos de la Dirección General de Estadística y Censos de Buenos Aires, los cuales fueron analizados y preprocesados antes de la aplicación de los algoritmos; para generar los modelos se toma el 75 % de los datos para entrenamiento y 25 % para la evaluación del modelo obtenido. Para el modelo de agrupamiento K-Means se determinó el K óptimo a través del método del codo, y se obtuvo que los datos de energía facturada total presentan una estacionalidad mensual. Para el modelo de Regresión Lineal se utilizaron las métricas R2, RMSE y MAE, y se obtuvo que las energías facturadas residencial, comercial e industrial, más el número de usuarios residenciales, son las variables que mayor impacto tienen sobre la energía eléctrica facturada total.

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

Cited by 11 publications

References 46 publications

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Cluster Predictive Model Using Affinity Propagation Algorithm to Group Mushroom 5.8s rRNA Sequences

Uso de algoritmos de Machine Learning para analizar los datos de energía eléctrica facturada en la Ciudad de Buenos Aires durante el período 2010–2021

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