Main Reference PaperRobust Big Data Analytics for Electricity Price Forecasting in the Smart Grid, IEEE Transactions on Big Data, 2018 [Java/Hadoop]
  • The proposed work handles an issue related to electricity price forecasting. By exploiting the SVM classifier, this work predicts the electricity price. But due to the training phase in SVM classification, the irrelevant and redundant features are involved,. Thus, it degrades the performance of forecasting accuracy. To solve this issue, an important features are selected and tune the classifier. The following methods Grey Correlation Analysis (GCA) and Principle Component Analysis are proposed to select the important features and eliminate the feature redundancy. Thus, it improves the classification accuracy.

Description
  • The proposed work handles an issue related to electricity price forecasting. By exploiting the SVM classifier, this work predicts the electricity price. But due to the training phase in SVM classification, the irrelevant and redundant features are involved,. Thus, it degrades the performance of forecasting accuracy. To solve this issue, an important features are selected and tune the classifier. The following methods Grey Correlation Analysis (GCA) and Principle Component Analysis are proposed to select the important features and eliminate the feature redundancy. Thus, it improves the classification accuracy.

  • To improve the classification accuracy and speed.

  • To predict the electricity price forecasting.

Aim & Objectives
  • To improve the classification accuracy and speed.

  • To predict the electricity price forecasting.

  • In the proposed work, the electricity price forecasting does not handle the missing data, it decreases the classification accuracy. So the algorithm is contributed for missing data prediction.

Contribution
  • In the proposed work, the electricity price forecasting does not handle the missing data, it decreases the classification accuracy. So the algorithm is contributed for missing data prediction.

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