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
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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。
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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.
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