Earticle

초록

영어

The more intense use of renewable energy brings about variability overwhelming grid stability. Five state-of-the-art machine learning models, namely, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost are used in this paper to make predictions about grid stability on the basis of hourly data on electricity consumption and production. The binary stability label was created using a production-consumption imbalance and the models have been evaluated in terms of accuracy, precision, recall, F1-score and ROC-AUC. As it can be seen, the best performing model was CatBoost with its highest accuracy of 98.88 and ROC-AUC of 0.999. The findings point to the opportunities of AI-based forecasting to enhance the incorporation of the renewable energy and the overall stability of the grids.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
A. Dataset Description
B. Data Preprocessing
C. Machine Learning Models
D. Model Evaluation
IV. RESULTS AND DISCUSSION
V. CONCLUSION
REFERENCES

저자

• Tahir Alyas [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Nadia Tabassum [ Department of Computer Science) Virtual University of Pakistan Lahore, Pakistan ]
• Arif Jawaid [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Qaiser Abbas [ Faculty of Computer and Information Systems Islamic University of Madinah, Madinah 42351, Saudi Arabia ]
• Sami Albouq [ Faculty of Computer and Information Systems Islamic University of Madinah, Madinah 42351, Saudi Arabia ]
• Muhammad Tayyab Khan [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]

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