PV太阳能建模的特征构造与选择
Feature Construction and Selection for PV Solar Power Modeling
摘要：
在过程工业中使用太阳能可以减少温室气体排放，使生产过程更具可持续性。然而，太阳能发电的间歇性使其使用具有挑战性。建立一个预测光伏发电的模型，使决策者能够对冲能源短缺，并进一步设计适当的操作。太阳能输出是时间序列数据，取决于许多因素，如辐照度和天气。本文基于历史数据，提出了一种提前1小时预测太阳能发电量的机器学习框架。我们的方法将输入数据集扩展到高维Chebyshev多项式空间。然后，一个特征选择方案的约束线性回归构建不同的天气类型的预测。实验结果表明，该方法的均方误差比支持向量机（SVM）、随机森林（RF）和梯度提升决策树（GBDT）等经典机器学习方法低.
英文摘要：
Using solar power in the process industry can reduce greenhouse gas emissions and make the production process more sustainable. However, the intermittent nature of solar power renders its usage challenging. Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages and further design proper operations. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data. Our method extends the input dataset into higher dimensional Chebyshev polynomial space. Then, a feature selection scheme is developed with constrained linear regression to construct the predictor for different weather types. Several tests show that the proposed approach yields lower mean squared error than classical machine learning methods, such as support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
