NANoor Aboramyah
Noor Aboramyah
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Noor Aboramyah has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Abdalrahman Alsulaili
Kuwait Univ
1 shared publication - Mohamad Alkhalidi
Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait
1 shared publication - Nasser Alenezi
Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait
1 shared publication
Latest Publications
- Open Access
- 24 July 2024
- Published by MDPI AG in Sustainability
- Vol. 16 (15) , 6326
Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches
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 (), 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 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.- 24 July 2024
- Published by MDPI AG in Sustainability
- Vol. 16 (15) , 6326
Open AccessAdvancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches
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 (), 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 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.