Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

Abdalrahman Alsulaili (), Noor Aboramyah, Nasser Alenezi and Mohamad Alkhalidi
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Abdalrahman Alsulaili: Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait
Noor Aboramyah: Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait
Nasser Alenezi: Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait
Mohamad Alkhalidi: Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait

Sustainability, 2024, vol. 16, issue 15, 1-14

Abstract: This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ( R 2 ), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R 2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

Keywords: electricity consumption; machine learning; XGBoost; arid climate; meteorological factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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