论文标题
适用的建模框架，用于全球水平辐照度的短期预测
Robust modelling framework for short-term forecasting of global horizontal irradiance
论文作者
论文摘要
对电力的需求不断增长，对清洁能源的需求增加了太阳能的使用。为了轻松管理电网，需要对太阳能的准确预测。本文比较了两种高斯过程回归（GPR）模型的准确性与添加剂分位数回归（AQR）和贝叶斯结构时间序列（BSTS）模型，并在对全球水平辐照度进行预测的2天预测中，使用比勒陀利亚大学7月2020年7月2020年8月至2021年8月。提高回归）。使用了使用GBR选择的变量，因为它们产生了最低的MAE（最小绝对错误）值。比较七个模型GPR（高斯过程回归），两层DGPR（两层深高斯过程回归），bstslong（贝叶斯结构时间序列长），AQRA（添加剂几分化回归平均），qRNN（QRNN），qRNN（数量回归神经网络），PlaAQR（plaaqr），plaaqr（部分通过量化量化量化的量化量化量），并进行了量化量化（量化量化），并进行了量化（量化量化），并进行了量化（在线量化），并进行了量化（量化）和量化。用于选择最佳模型的评估指标是MAE（平均绝对误差）和RMSE（根平方误差）。使用适当的评分规则和墨菲图进行了进一步的评估。发现最好的单个模型是GPR。最好的预测组合是基于MAE的AQRA（AQR平均）。但是，基于RMSE，GPNN是最好的预测组合方法。诸如Eskom之类的公司可以使用本研究中采用的方法来控制和管理电网。结果将促进能源资源的经济发展和可持续性。
The increasing demand for electricity and the need for clean energy sources have increased solar energy use. Accurate forecasts of solar energy are required for easy management of the grid. This paper compares the accuracy of two Gaussian Process Regression (GPR) models combined with Additive Quantile Regression (AQR) and Bayesian Structural Time Series (BSTS) models in the 2-day ahead forecasting of global horizontal irradiance using data from the University of Pretoria from July 2020 to August 2021. Four methods were adopted for variable selection, Lasso, ElasticNet, Boruta, and GBR (Gradient Boosting Regression). The variables selected using GBR were used because they produced the lowest MAE (Minimum Absolute Errors) value. A comparison of seven models GPR (Gaussian Process Regression), Two-layer DGPR (Two-layer Deep Gaussian Process Regression), bstslong (Bayesian Structural Time Series long), AQRA (Additive Quantile Regression Averaging), QRNN(Quantile Regression Neural Network), PLAQR(Partial Linear additive Quantile Regression), and Opera(Online Prediction by ExpRt Aggregation) was made. The evaluation metrics used to select the best model were the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Further evaluations were done using proper scoring rules and Murphy diagrams. The best individual model was found to be the GPR. The best forecast combination was AQRA ((AQR Averaging) based on MAE. However, based on RMSE, GPNN was the best forecast combination method. Companies such as Eskom could use the methods adopted in this study to control and manage the power grid. The results will promote economic development and sustainability of energy resources.
