通过机器学习和统计方法推进干旱气候下的用电量预测

Sustainability Pub Date : 2024-07-24 DOI:10.3390/su16156326
A. Alsulaili, Noor Aboramyah, Nasser Alenezi, M. Alkhalidi
{"title":"通过机器学习和统计方法推进干旱气候下的用电量预测","authors":"A. Alsulaili, Noor Aboramyah, Nasser Alenezi, M. Alkhalidi","doi":"10.3390/su16156326","DOIUrl":null,"url":null,"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 (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.","PeriodicalId":509360,"journal":{"name":"Sustainability","volume":"17 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches\",\"authors\":\"A. Alsulaili, Noor Aboramyah, Nasser Alenezi, M. Alkhalidi\",\"doi\":\"10.3390/su16156326\",\"DOIUrl\":null,\"url\":null,\"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 (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.\",\"PeriodicalId\":509360,\"journal\":{\"name\":\"Sustainability\",\"volume\":\"17 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/su16156326\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/su16156326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本研究调查了以极端温度和高湿度为特征的干旱地区气象因素对用电量的影响。研究采用了多元线性回归 (MLR) 和乘法时间序列 (MTS) 等统计方法以及先进的机器学习方法极端梯度提升 (XGBoost) 来分析历史用电量数据。利用确定系数 (R2)、均方根误差 (RMSE) 和平均绝对百分比误差 (MAPE) 等既定指标对所开发的模型进行了严格评估。模型的性能非常准确,回归型模型的 R2 一直大于 0.9。此外,相对于整体数据规模而言,RMSE 和 MAPE 等其他指标的数值也非常低,从而加强了模型的精确性和可靠性。该分析不仅强调了耗电量的重要气象驱动因素,还评估了模型在管理季节性和不规则变化方面的有效性。这些发现为类似气候地区改善能源管理和促进可持续发展提供了重要启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 (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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信