Load data cleaning with data mining techniques

Almeida, Victor A.; Pessanha, José Francisco Moreira; Calôba, L. P.

doi:10.1109/sbse.2018.8395916

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…the detection and classification of voltage events [10][11][12][13][14][15] • the calculation and prediction of power losses [16][17][18] • the diagnosis of faults in power transformers [19][20][21][22][23] • load forecasting [24][25][26][27][28][29] • load pattern segmentation [30][31][32][33] • fault detection [34][35][36][37][38] • fault prediction [39][40][41][42] • the defining of energy consumption [43][44][45][46][47][48] • the forecasting of energy gaining from renewable energy sources [49][50][51][52] • the reliability assessment of renewable sources of energy [53][54][55][56] • energy management in a household …”

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The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

et al. 2020

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This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

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The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

et al. 2020

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Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution

Sun,

Ning,

Shen

et al. 2023

Data Sci. Eng.

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An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.

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Short Term Load Forecasting Based on iForest-LSTM

Zhang

Ding³

et al. 2019

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Load data cleaning with data mining techniques

Cited by 3 publications

References 14 publications

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution

Short Term Load Forecasting Based on iForest-LSTM

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