Electrical Load Forecasting Based on Random Forest, XGBoost, and Linear Regression Algorithms

Power infrastructure management requires a consistent power supply. One approach of doing this is predicting the power usage. This requires a variety of elements to be considered such as the environment and the spatial and temporal aspects. These tend to demonstrate a considerable fluctuation in the...

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Published in:International Conference on Information Technology Research (Online) pp. 25 - 31
Main Authors: Abumohsen, Mobarak, Owda, Amani Yousef, Owda, Majdi
Format: Conference Proceeding
Language:English
Published: IEEE 09.08.2023
Subjects:
Companies
electric power
electrical demand
Linear regression
Load forecasting
machine learning
Machine learning algorithms
Mean square error methods
Power supplies
Radio frequency
ISSN:2831-3399
Online Access:Get full text
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Abstract Power infrastructure management requires a consistent power supply. One approach of doing this is predicting the power usage. This requires a variety of elements to be considered such as the environment and the spatial and temporal aspects. These tend to demonstrate a considerable fluctuation in the electrical load pattern depending on the temporal and environmental variables. The primary goal of this research is to develop forecasting models that properly anticipate in predicting the electrical load based on a real and unique dataset of the district energy company (Tubas District Electricity Company - in Palestine). Three machine learning models were used to forecast the electrical loads; namely: (1) Random Forest (RF), (2) XGBoost, and (3) Linear Regression (LR). The models were evaluated, and the RF model was found to achieve the best performance in terms of accuracy. The RF model obtained an R-squared of 87.749%, a Mean Absolute Error (MAE) of 0.03904, and a Mean Square Error (MSE) of 0.00270.
AbstractList Power infrastructure management requires a consistent power supply. One approach of doing this is predicting the power usage. This requires a variety of elements to be considered such as the environment and the spatial and temporal aspects. These tend to demonstrate a considerable fluctuation in the electrical load pattern depending on the temporal and environmental variables. The primary goal of this research is to develop forecasting models that properly anticipate in predicting the electrical load based on a real and unique dataset of the district energy company (Tubas District Electricity Company - in Palestine). Three machine learning models were used to forecast the electrical loads; namely: (1) Random Forest (RF), (2) XGBoost, and (3) Linear Regression (LR). The models were evaluated, and the RF model was found to achieve the best performance in terms of accuracy. The RF model obtained an R-squared of 87.749%, a Mean Absolute Error (MAE) of 0.03904, and a Mean Square Error (MSE) of 0.00270.
Author Abumohsen, Mobarak
Owda, Amani Yousef
Owda, Majdi
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  organization: Arab American University,Department of Natural, Engineering and Technology Sciences,Ramallah,Palestine,P600
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  givenname: Amani Yousef
  surname: Owda
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  organization: Arab American University,Department of Natural, Engineering and Technology Sciences,Ramallah,Palestine,P600
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  givenname: Majdi
  surname: Owda
  fullname: Owda, Majdi
  email: majdi.owda@aaup.edu
  organization: Arab American University Unisco Chair for Data Science,Faculty of Data Science,Ramallah,Palestine,P600
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SubjectTerms Companies
electric power
electrical demand
Linear regression
Load forecasting
machine learning
Machine learning algorithms
Mean square error methods
Power supplies
Radio frequency
Title Electrical Load Forecasting Based on Random Forest, XGBoost, and Linear Regression Algorithms
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