Data Analytics for Electricity Load and Price Forecasting in the Smart Grid
The present strategies for the prediction of price and load may be difficult to deal with huge amount of load and price data. To resolve the problem, three modules are incorporated within the model. Firstly, the fusion of Decision Tree (DT) and Random Forest (RF) are used for feature selection and to remove the redundancy among feature. Secondly, Recursive Feature Elimination (RFE) is taken for feature extraction purpose that extracts the principle components and also used for dimensionality reduction. Finally, to forecast load and price, Support Vector Machine (SVM) and Logistic Regression (LR) as a classifiers are used through which we achieve good accuracy results in load and price prediction.
This research is supported by Al Yamamah university Riyadh Saudi Arabia.
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