Data mining approach for short term load forecasting by combining wavelet transform and group method of data handling (WGMDH)

Yuniarti, Trisna; Surjandari, Isti; Muslim, Erlinda; Laoh, Enrico

doi:10.1109/icsitech.2017.8257085

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…The discovery of the structure provides new knowledge that can assist automotive companies in making business decisions. Data processing with this technique goes through several steps [27], namely: Data Cleaning, Data Integration, and Data Transformation. Figure 1 is a flowchart for using data mining modeling with clustering techniques.…”

Section: Data Preprocessingmentioning

confidence: 99%

Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia

Astuti,

Yuniarti

2023

j. manaj. ind. dan logist.

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The research aims to build a model based on sales data for all automotive products in Indonesia using data mining with a k-means approach. This study uses automotive product sales data from January 2017 to September 2022. The lowest Davis-Bouldin index shows that three clusters (k=3) have the best performance. Based on the clustering results, 92% of the items are in cluster 0, 1% in cluster 1, and 7% in cluster 2. In addition, the clustering results show that cluster 1 is a car product with high sales volume. Cluster 2 is a car product with medium sales volume. Furthermore, cluster 0 is a car product with low sales volume. Business people or related parties can use data visualization and extraction from clustering results to learn the latest insights and information in determining business strategies, policies, and decisions to improve business competitiveness.

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Section: Data Preprocessingmentioning

confidence: 99%

Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia

Astuti,

Yuniarti

2023

j. manaj. ind. dan logist.

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“…Data luas areal, data produksi, dan data produktivitas selanjutnya diolah menggunakan metode data mining dengan tahapan: pembersihan data; pengintegrasian data; penyeleksian data; transformasi data; data mining; dan representasi pengetahuan yang didapatkan dari hasil pengolahan data mining tersebut [17]. Gambar 1. merupakan proses untuk setiap tahapan dalam melakukan pengolahan data.…”

Section: Proses Pengolahan Dataunclassified

Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

Yuniarti

Hayati

2021

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The oil palm is the most productive plantation product in Indonesia. Government strategies and policies related to oil palm plantations continue to be carried out considering that the plantation area is increasing every year. Segmentation of oil palm plantations based on area, production, and productivity aims to identify groups of potential oil palm plantations in the territory of Indonesia. This segmentation can provide consideration in formulating strategies and policies that will be made by the government. The segmentation method for grouping oil palm plantations uses the K-Means Clustering Data Mining technique with 3 clusters specified. Data mining stages start from data collection until representation is carried out, where 34 data sets are collected, only 25 data sets can be processed further. The results of this grouping obtained three plantation segments, namely 72% of the plantation group with low potential, 20% of the plantation group with medium potential, and 8% of the plantation group with high potential.

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“…In a few cases, MI-ANN, WNN, GRU, DBN, RBM, ANFIS, and ART network approaches were applied to obtain better performances. Cascade NN, KNN-ANN [44], [48], [63], [65], [66], [73], [74], [75], [96], [109], [124], [129], [131], [133], [134], [137], [ [40], [42], [45] - [65], [67] - [73], [76] - [95], [97], [98], [101] - [103], [105] - [107], [110] - [119], [121], [123] - [126], [130] - [41], [42], [45] - [48], [52], [57], [61] - [63], [65], [69], [70], [72], [76],…”

Section: B Different Ann Techniques In Deep Learning Based Load Forementioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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Data mining approach for short term load forecasting by combining wavelet transform and group method of data handling (WGMDH)

Cited by 6 publications

References 22 publications

Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia

Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia

Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

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