| A Comparative Study of Machine Learning Algorithms for Short-term Electrical Load Forecasting |
| Paper ID : 1021-ICCI2021 (R1) |
| Authors: |
|
Mahmoud Mohammed Hussein1, Wesam Yehia Bakr2, Ashraf B. Elsisi *3 1Department of Computer Science, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt 2Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt 3Faculty of Computers & Information, Computer Science Dept. Menoufia University, Shebin El-Kom 32511, Menoufia, EGYPT |
| Abstract: |
| Electrical load forecasting is an important field, where it can be used to estimate the amount of electricity needed, the price of electricity, and the number of generators to be used. Such forecasting can be performed using machine learning approaches. Therefore, in this paper, we compare four machine learning algorithms that can be used to predict electrical load. These algorithms are Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Gradient Boosting Machines (GBM), and Random Forest Regression (RF). The comparison is done on three months hourly recorded data set that is publicly available from Pennsylvania Jersey Maryland (PJM) company. Our contribution is the identification of which algorithm has a better accuracy and less execution time. This enables electricity companies concerned with load forecasting to choose an algorithm from the existing ones as fast as possible without wasting time searching for an appropriate algorithm. The results show that RF achieves the best accuracy, and also has the least execution time. |
| Keywords: |
| Machine Learning, Electrical Load Forecasting, SVM, LSSVM, GBM, RF |
| Status : Paper Accepted |
