干旱区生态环境知识资源中心(Arid): Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

Arid

DOI	10.3390/su16156326
	Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches
	Alsulaili, Abdalrahman; Aboramyah, Noor; Alenezi, Nasser; Alkhalidi, Mohamad
通讯作者	Alsulaili, A
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2024
卷号	16 期号:15
英文摘要	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 (R2), 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 R2 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.
英文关键词	electricity consumption machine learning XGBoost arid climate meteorological factors
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:001287155300001
WOS关键词	PREDICTION ; DEMAND ; STATE
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405757
推荐引用方式 GB/T 7714	Alsulaili, Abdalrahman,Aboramyah, Noor,Alenezi, Nasser,et al. Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches[J],2024,16(15).
APA	Alsulaili, Abdalrahman,Aboramyah, Noor,Alenezi, Nasser,&Alkhalidi, Mohamad.(2024).Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches.SUSTAINABILITY,16(15).
MLA	Alsulaili, Abdalrahman,et al."Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches".SUSTAINABILITY 16.15(2024).